Tuning site-specific dynamics to drive allosteric activation in a pneumococcal zinc uptake regulator

Daiana A Capdevila; Fidel Huerta; Katherine A Edmonds; My Tra Le; Hongwei Wu; David P Giedroc

doi:10.7554/eLife.37268

. 2018 Oct 17;7:e37268. doi: 10.7554/eLife.37268

Tuning site-specific dynamics to drive allosteric activation in a pneumococcal zinc uptake regulator

Daiana A Capdevila ¹, Fidel Huerta ^1,², Katherine A Edmonds ¹, My Tra Le ¹, Hongwei Wu ¹, David P Giedroc ^1,^3,^✉

Editors: Lewis E Kay⁴, John Kuriyan⁵

PMCID: PMC6224198 PMID: 30328810

Abstract

MarR (multiple antibiotic resistance repressor) family proteins are bacterial repressors that regulate transcription in response to a wide range of chemical signals. Although specific features of MarR family function have been described, the role of atomic motions in MarRs remains unexplored thus limiting insights into the evolution of allostery in this ubiquitous family of repressors. Here, we provide the first experimental evidence that internal dynamics play a crucial functional role in MarR proteins. Streptococcus pneumoniae AdcR (adhesin-competence repressor) regulates Zn^II homeostasis and Zn^II functions as an allosteric activator of DNA binding. Zn^II coordination triggers a transition from somewhat independent domains to a more compact structure. We identify residues that impact allosteric activation on the basis of Zn^II-induced perturbations of atomic motions over a wide range of timescales. These findings appear to reconcile the distinct allosteric mechanisms proposed for other MarRs and highlight the importance of conformational dynamics in biological regulation.

Research organism: E. coli, Other

Introduction

Successful bacterial pathogens respond to diverse environmental insults or changes in intracellular metabolism by modulating gene expression (Alekshun and Levy, 2007). Such changes in gene expression are often mediated by ‘one-component’ transcriptional regulators, which directly sense chemical signals and convert such signals into changes in transcription. Members of the multiple antibiotic resistance regulator (MarR) family are critical for the survival of pathogenic bacteria in hostile environments, particularly for highly antibiotic-resistant pathogens (Ellison and Miller, 2006; Yoon et al., 2009; Weatherspoon-Griffin and Wing, 2016; Tamber and Cheung, 2009; Aranda et al., 2009; Grove, 2017). Chemical signals sensed by MarRs include small molecule metabolites (Deochand and Grove, 2017), reactive oxygen species (ROS) (Liu et al., 2017; Sun et al., 2012) and possibly reactive sulfur species (RSS) (Peng et al., 2017). It has been proposed that evolution of new MarR proteins enables microorganisms to colonize new niches (Deochand and Grove, 2017), since species characterized by large genomes and a complex lifestyle encode many, and obligate parasitic species with reduced genome sizes encode few (Pérez-Rueda et al., 2004). Therefore, elucidating how new inducer specificities and responses have evolved in this ubiquitous family of proteins on what is essentially an unchanging molecule scaffold is of great interest, as is the molecular mechanism by which inducer binding or cysteine thiol modification allosterically regulates DNA operator binding in promoter regions of regulated genes.

Obtaining an understanding of how allostery has evolved in one-component regulatory systems (Ulrich et al., 2005; Marijuán et al., 2010), including MarR family repressors, requires a comprehensive analysis of the structural and dynamical changes that occur upon inducer and DNA binding (Capdevila et al., 2017a; Tzeng and Kalodimos, 2013; West et al., 2012; Tzeng and Kalodimos, 2009; Capdevila et al., 2018). For MarRs, several distinct allosteric mechanisms have been proposed, from a ‘domino-like’ response (Bordelon et al., 2006; Gupta and Grove, 2014; Perera and Grove, 2010) to ligand binding-mediated effects on asymmetry within the dimer (Anandapadamanaban et al., 2016), to oxidative crosslinking of E. coli MarR dimers into DNA binding-incompetent tetramers (Hao et al., 2014). While there are more than 130 crystal structures of MarR family repressors in different allosteric states (Figure 1—figure supplement 1), an understanding of the role of atomic motions and the conformational ensemble in MarRs is nearly totally lacking and what is known is based exclusively on simulations (Anandapadamanaban et al., 2016; Sun et al., 2012). Here, we provide the first experimental evidence in solution that internal dynamics play a crucial functional role in a MarR protein, thus define characteristics that may have impacted the evolution of new biological outputs in this functionally diverse family of regulators.

In the conventional regulatory paradigm, the binding of a small molecule ligand, or the oxidation of conserved ROS-sensing cysteines, induces a structural change in the homodimer that typically negatively impacts DNA binding affinity. This results in a weakening or dissociation of the protein-DNA complex and transcriptional derepression. Several reports provide evidence for a rigid body reorientation of the two α4 (or αR)-reading heads within the dimer (Figure 1A–B, Figure 1—figure supplement 1) (Alekshun et al., 2001; Fuangthong and Helmann, 2002; Wilke et al., 2008; Chang et al., 2010; Liu et al., 2017; Deochand and Grove, 2017; Dolan et al., 2011; Deochand et al., 2016). The generality of this simple paradigm is inconsistent with the findings that some MarR proteins share very similar static structures in the DNA binding competent and DNA binding-incompetent states (Anandapadamanaban et al., 2016; Kim et al., 2016; Liguori et al., 2016); furthermore, several DNA binding competent states have been shown to require a significant rearrangement to bind DNA (Alekshun et al., 2001; Liu et al., 2017; Zhu et al., 2017b; Hao et al., 2014; Gao et al., 2017; Chin et al., 2006; Saridakis et al., 2008). In fact, a comprehensive analysis of all available MarR family structures strongly suggests that the degree of structural reorganization required to bind DNA, characterized by a narrow distribution of α4-α4’ orientations, is comparable whether transitioning from the DNA-binding incompetent or competent states of the repressor (Figure 1C, Table 1, Figure 1—source data 1). These observations strongly implicate a conformational ensemble model of allostery (Motlagh et al., 2014) (Figure 1B–D), where inducer sensing impacts DNA binding by restricting the conformational spread of the active repressor, as was proposed in a recent molecular dynamics study (Anandapadamanaban et al., 2016).

Figure 1. — The two Zn(II) ions in each protomer are represented by spheres, and coordinating ligands are shown in stick representation. The DNA binding helices are shaded *red*. (B) Simplified free energy diagram showing the DNA binding competent (*green*) and DNA binding incompetent (*blue*) states with the relative population of two distinct conformations: compatible with DNA binding (*red* rectangle, α4-α4’ distance between DNA binding helices, ≈30 Å) and incompatible with DNA binding (larger α4-α4’ distances). In this free energy diagram, the DNA binding-incompetent state has a comparatively higher population of the conformation incompatible with DNA binding relative to the DNA binding-competent state. (C) The α4-α4’ distance distribution plotted against the DNA-binding inter-helical α4-α4’ orientation distribution for all the reported MarR crystal structures (see Table 1 and Figure 1—source data 1 for details) in the allosterically DNA binding competent conformation (*green*), a DNA binding incompetent conformation (*blue*) and in the DNA-bound (*red*) conformation. *Filled* circles represent states that have been assigned based on DNA binding data, while for the *hollow* circles the DNA binding properties were assigned taking into account the conformational state in the crystal structure (*i.e.*, reduced, ligand bound) and the degree of sequence similarity to other MarR repressors. The structures for ZitR and AdcR have been highlighted with a *white* star. The inferred conformational space occupied by the DNA-bound conformation in all MarR regulators (Table 1) is shaded in *red* oval. Ribbon representations of the molecules in each conformation are shown in the inset, as well as a scheme of how the distances and angles were measured. (D) Histogram plot of the α4-α4’ distance (see panel C) extracted from 136 different crystal structures of MarR repressors in the DNA binding incompetent, DNA binding competent and DNA-bound conformations.

Figure 1—source data 1. Table of details on MarR proteins structures: PDB ID, reference for the structure, protein state and ligand bound (if any), DNA binding competence classification, organism, Methods used for determining the DNA binding properties, DNA binding constant (K_a), reference for the DNA binding constant, residues in the α1-α2 loop obtained from pymol secondary sequence assignment, minimal distance between the α4-α4’ helices, and angle between the α4-α4’ helices.

elife-37268-fig1-data1.xlsx^{(26.6KB, xlsx)}

DOI: 10.7554/eLife.37268.004

Figure 1—figure supplement 1. — The two Zn(II) ions in each protomer are represented by spheres, and coordinating ligands are shown in stick representation. The DNA binding helices are shaded *red*. (B) Simplified free energy diagram showing the DNA binding competent (*green*) and DNA binding incompetent (*blue*) states with the relative population of two distinct conformations: compatible with DNA binding (*red* rectangle, α4-α4’ distance between DNA binding helices, ≈30 Å) and incompatible with DNA binding (larger α4-α4’ distances). In this free energy diagram, the DNA binding-incompetent state has a comparatively higher population of the conformation incompatible with DNA binding relative to the DNA binding-competent state. (C) The α4-α4’ distance distribution plotted against the DNA-binding inter-helical α4-α4’ orientation distribution for all the reported MarR crystal structures (see Table 1 and Figure 1—source data 1 for details) in the allosterically DNA binding competent conformation (*green*), a DNA binding incompetent conformation (*blue*) and in the DNA-bound (*red*) conformation. *Filled* circles represent states that have been assigned based on DNA binding data, while for the *hollow* circles the DNA binding properties were assigned taking into account the conformational state in the crystal structure (*i.e.*, reduced, ligand bound) and the degree of sequence similarity to other MarR repressors. The structures for ZitR and AdcR have been highlighted with a *white* star. The inferred conformational space occupied by the DNA-bound conformation in all MarR regulators (Table 1) is shaded in *red* oval. Ribbon representations of the molecules in each conformation are shown in the inset, as well as a scheme of how the distances and angles were measured. (D) Histogram plot of the α4-α4’ distance (see panel C) extracted from 136 different crystal structures of MarR repressors in the DNA binding incompetent, DNA binding competent and DNA-bound conformations.

Figure 1—source data 1. Table of details on MarR proteins structures: PDB ID, reference for the structure, protein state and ligand bound (if any), DNA binding competence classification, organism, Methods used for determining the DNA binding properties, DNA binding constant (K_a), reference for the DNA binding constant, residues in the α1-α2 loop obtained from pymol secondary sequence assignment, minimal distance between the α4-α4’ helices, and angle between the α4-α4’ helices.

elife-37268-fig1-data1.xlsx^{(26.6KB, xlsx)}

DOI: 10.7554/eLife.37268.004

Table 1. Interprotomer distances between the Cα of the N-terminal residue in the α4 and α4’ helices for representatives MarR proteins.

	DNA-bound state		DNA binding incompetent state^a,*		DNA binding competent state^b*
MarR	Distance (Å)	Pdb id	Distance (Å)	Pdb id	Distance (Å)	Pdb id
ZitR (AdcR)	32.3/31.7	5yi2/5yi3	59.6/57.3	5yh0/5yh1	35.5/54.0/50.2 (22.2/34/33.8)	5yhx/5yhy/ 5yhz (3tgn/5jls/5 jlu)
Ec MarR	29	5hr3	12.9/12	1jgs/4jba	8.3/8.4	3vod/3voe
OhrR	27.6	1z9c	(32.2)	(2pfb)	23.9 (28.9)	1z91 (2pex)
SlyA	27.8	3q5f	29.4	3deu	15.5 (23.8, 20)	3qpt (1lj9, 4mnu)
AbsC	26.3	3zpl	30.8	3zmd	-	-
RovA	21.8/21.9	4aij/ 4aik	-	-	20.9	4aih
MosR	25.1	4f×4	15.1	4f×0	-	-
MepR	26.4/26.9	4lll/ 4lln	18.9/16.9/ 30.8/57.9	3eco/4l9n/ 4l9t/4l9v	27.9/46.8	4l9j/4ld5
AbfR	29.9/30	5hlh/5hlg	40.7	5hli	37	4hbl
Rv2887	22.5	5hso	7.9/15.1	5hsn/5hsl	8.3	5hsm
HcaR	28.6	5bmz	19.1/19.8/19.5/19.2	4rgx/4 rgu/4rgs/ 4rgr	18.7	3k0l
ST1710	10.1^c	3gji	23	3gf2	22.8	2eb7
TcaR	19.1^d	4kdp	22.3/24.7	4eju/3kp7	26.4/22.5/21.1/22/27.6/ 18.3/21.1/18.2	3kp2/3kp3/3kp4/3kp5 /3kp7/4ejt/4ejv/4ejw

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^aAny protein allosteric state that has been shown to bind to DNA in-vitro with an affinity higher than 10⁷M^-1 or is capable of repressing the expression of downstream gene. .

^bAny protein allosteric state that fails to repress these genes and/or exhibits a significantly lower DNA binding affinity from the DNA binding-competent conformation (at least 10-fold) or an affinity lower than 10⁶M^-1 *In addition to these two categories, two other categories were classified as DNA binding-competent or DNA binding-incompetent states in Figure 1C. They refer to any protein allosteric state for which the DNA binding properties have not been determined, but the conformational state in the crystal structure is known (i.e., reduced, ligand bound). .

^cNot inserted in the major groove of the DNA. .

^dThis structure was co-crystallized with ssDNA. Any entry in parentheses corresponds to a structure of a homologue from a different organism (see Figure 1—source data 1).

MarR proteins are obligate homodimers that share a winged-helical DNA-binding domain connected to a DNA-distal all-helical dimerization domain where organic molecules bind in a cleft between the two domains (Figure 1—figure supplement 1B). Individual MarR members have been shown to bind a diverse range of ligands at different sites on the dimer (Otani et al., 2016; Takano et al., 2016); likewise, oxidation-sensing cysteine residues are also widely distributed in the dimer (Fuangthong and Helmann, 2002; Liu et al., 2017; Hao et al., 2014; Dolan et al., 2011; Chen et al., 2006). This functional diversity is accompanied by relatively low overall sequence similarity, which suggests that a conserved molecular pathway that connects sensing sites and the DNA binding heads is highly improbable. Complicating our current mechanistic understanding of this family is that for many members, including E. coli MarR, the physiological inducer (if any) is unknown, rendering functional conclusions on allostery from crystallographic experiments alone less certain (Hao et al., 2014, Zhu et al., 2017b).

In contrast to the extraordinary diversity of thiol-based switching MarRs, MarR family metallosensors are confined to a single known regulator of Zn^II uptake, exemplified by AdcR (adhesin competence regulator) from S. pneumoniae and closely related Streptococcus ssp. (Loo et al., 2003; Reyes-Caballero et al., 2010) and ZitR from Lactococcus spp (Llull et al., 2011; Zhu et al., 2017c). AdcR and ZitR both possess two closely spaced pseudotetrahedral Zn^II binding sites termed site 1 and site 2 (Figure 1A) that bind Zn^II with different affinities (Reyes-Caballero et al., 2010; Guerra et al., 2011; Sanson et al., 2015; Zhu et al., 2017c). Zn^II is an allosteric activator of DNA operator binding which is primarily dependent on the structural integrity of site 1 (Reyes-Caballero et al., 2010; Zhu et al., 2017c). ZitR has been recently structurally characterized, with crystallographic models now available for the apo- and Zn^II₁- (bound to site 1) and Zn^II₂- and Zn^II₂-DNA operator complexes, thus providing significant new insights into ZitR and AdcR function (Zhu et al., 2017c). These structures reveal that Zn^II₂-ZitR and Zn^II₂-AdcR form triangularly-shaped homodimers and are essentially identical, as anticipated from their high sequence identity (49%). Apo-ZitR adopts a conformation that is incompatible with DNA binding, and filling of both Zn^II sites is required to adopt a conformation that is similar to that of the DNA-complex. Thermodynamically, filling of the low affinity site two enhances allosteric activation of DNA-binding by ≈10-fold, and this occurs concomitant with a change in the H42 donor atom to the site 1 Zn^II ion from Nε2 in the apo- and Zn^II₁-states to Nδ1 in the Zn^II₂-ZitR [as in Zn^II₂ AdcR; (Guerra et al., 2011) and Zn^II₂ ZitR-DNA operator complexes (Zhu et al., 2017c). Allosteric activation by Zn^II is in strong contrast to all other members of the MarR superfamily, consistent with its biological function as uptake repressor at high intracellular Zn^II.

Here we employ a combination of NMR-based techniques and small angle x-ray scattering (SAXS) to show that apo- (metal-free) AdcR in solution is characterized by multiple semi-independent domains connected by flexible linkers, resulting in a distinct quaternary structure from the Zn-bound state previously structurally characterized (Guerra et al., 2011). Our backbone relaxation dispersion-based NMR experiments show that apo-AdcR samples distinct conformational states in the µs-ms timescale, while Zn^II narrows this distribution, likely increasing the population of a state that has higher affinity for DNA. This finding is consistent with the crystallographic structures of Zn^II₂ ZitR and the Zn^II₂ ZitR:DNA complex (Zhu et al., 2017c). The site-specific backbone and methyl sidechain dynamics in the sub-ns timescale show that Zn^II not only induces a general restriction of these internal protein dynamics, but also subtly enhances fast timescale backbone and sidechain motions in the DNA binding domains. Together, these data suggest that Zn^II coordination drives a conformational change that enhances internal dynamics uniquely within the DNA binding domain, thus poising the repressor to interact productively with various DNA operator target sequences (Reyes-Caballero et al., 2010). We demonstrate the functional importance of these dynamics by characterizing both methyl sidechain and hydrogen-bonding substitution mutants of AdcR (Capdevila et al., 2017a) in terms of function, stability and dynamical impact. Overall, our findings suggest that protein dynamics on a wide range of timescales strongly impact AdcR function. We propose an ensemble model of allostery that successfully reconciles the distinct mechanisms proposed for other MarR family repressors and suggests a mechanism of how evolution tunes dynamics and structure to render distinct biological outputs (allosteric activation vs. allosteric inhibition) on a rigorously conserved molecular scaffold.

Results and discussion

Solution structural differences between apo and Zn^II bound forms of AdcR

Our crystal structure suggests that once AdcR is bound to both Zn^II, the αR- (α4) reading heads adopt a favorable orientation for DNA binding (Guerra et al., 2011), a finding compatible with structural studies of L. lactis ZitR (Zhu et al., 2017c) (Figure 1A). These structural studies suggest a ‘pre-locked’ model, where Zn^II binding to both sites 1 and 2, concomitant with a H42 ligand atom switch, locks the AdcR homodimer into a DNA binding-competent conformation. This model makes the prediction that the unligated AdcR can explore conformations structurally incompatible with DNA binding, as shown previously for Zn^II₁ ZitR (Zhu et al., 2017c), thus requiring a significant degree of reorganization to bind with high affinity to the DNA (Figure 1B). Despite substantial efforts, it has not yet been possible to obtain the crystal structure of apo-AdcR, suggesting that the apo-repressor may be highly flexible in solution (Guerra et al., 2011; Sanson et al., 2015). Thus, we employed SAXS as a means to explore the apo-AdcR structure and elucidate the structural changes induced by Zn^II binding and conformational switching within the AdcR homodimer.

We first examined the behavior of apo- and Zn^II-bound states. Both states show Guinier plots indicative of monodispersity and similar radii of gyration (R_g). These data reveal that each state is readily distinguished from the other in the raw scattering profiles (to q = 0.5 Å⁻¹) (Figure 2A)as well as in the PDDF plots (p(r) versus r), with the experimental scattering curve of the Zn^II bound state being more consistent than the unligated state with the one obtained from the Zn^II₂ AdcR crystal structure (Figure 2A, inset). Moreover, a qualitative analysis of the PDDF plots suggests that apo-AdcR is less compact than the Zn^II-bound state (Figure 2—figure supplement 1). The molecular scattering envelopes calculated as bead models with the ab initio program DAMMIF for apo-AdcR suggest that the differences between the apo and Zn^II AdcR SAXS profiles can be explained on the basis of a reorientation of the winged helix-turn-helix motif with respect to the dimerization domain, particularly in a distortion in the α5 helix (Figure 2B). The models obtained confirm that the Zn-bound structure in solution resembles the crystallographic models of apo-ZitR and Zn^II AdcR (Guerra et al., 2011; Zhu et al., 2017c) (Figure 2C); however, we note that the SAXS profile of the apo-AdcR differs significantly from the ZitR crystal structure (Figure 2—figure supplement 1D) which is likely related to the high flexibility of this conformational state in solution. Moreover, the resolution of SAXS based models cannot be used to obtain residue-specific information about structural perturbations introduced by Zn^II binding (Figure 2—figure supplement 1). Thus, we turned to NMR-based techniques to provide both high resolution and site-specific information on this highly dynamic system.

Figure 2. — Insets present the residual intensity and χ² estimated for the calculated scattering profile of the previously published AdcR-Zn₂ structure (PDB: 3tgn) in comparison with the scattering profiles of AdcR of apo and Zn₂-states (Guerra et al., 2011). Best-fit DAMMIF *ab initio* model (Franke and Svergun, 2009) for apo- (B) (*blue*) and Zn^II₂-states (C) (*green*), aligned with the ribbon representation of the Zn^II₂ structure (Figure 1A, PDB: 3tgn). The corresponding Guinier, Kratky and pairwise distribution histogram plots are shown in Figure 2—figure supplement 1, along with the fitting parameters.

Figure 2—figure supplement 1. — Insets present the residual intensity and χ² estimated for the calculated scattering profile of the previously published AdcR-Zn₂ structure (PDB: 3tgn) in comparison with the scattering profiles of AdcR of apo and Zn₂-states (Guerra et al., 2011). Best-fit DAMMIF *ab initio* model (Franke and Svergun, 2009) for apo- (B) (*blue*) and Zn^II₂-states (C) (*green*), aligned with the ribbon representation of the Zn^II₂ structure (Figure 1A, PDB: 3tgn). The corresponding Guinier, Kratky and pairwise distribution histogram plots are shown in Figure 2—figure supplement 1, along with the fitting parameters.

TROSY NMR on the 100% deuterated AdcR homodimer (32 kDa) and optimized buffer conditions for both states (pH 5.5, 50 mM NaCl, 35°C) enabled us to obtain complete backbone assignments for Zn^II₂-AdcR and nearly complete assignments for apo-AdcR (missing residues 21, 38 – 40 due to exchange broadening) (Figure 3—figure supplement 1). The chemical shift perturbation maps (Figure 3A–B) reveal that the largest perturbations are found in the immediate vicinity of the metal site region, that is the α1-α2 loop (residues 21 – 35), the remainder of the α2 helix (residues 41 – 47), and the central region of the α5 helix, which provides donor groups to both site 1 (H108, H112) and site 2 (E107) Zn^II. These changes derive partially from changes in secondary structure, such as the extension of the α1 helix and partial unfolding of the α2 helix (Figure 3—figure supplement 1), as well as from proximity to the Zn^II.

Figure 3. — (A) Backbone CSPs. CSPs of the sterospecifically assigned methyl groups at pH 5.5, 50 mM NaCl, 35°C. (B) Both these CSPs are painted on the ribbon representation of the structure of Zn^II₂ AdcR. The shaded bar in each case represents one standard deviation from the mean perturbation. Site 1 and site 2 ligands in the primary structure in panel A are denoted by the *yellow* and *green* circles, respectively; the asterisks at residue positions 21 and 38 – 40 indicate no assignment in the apo-state (see materials and methods), while asterisks mark residue positions 103 and 128 for prolines. Insets show the CSP values painted onto the 3tgn structure.

Figure 3—figure supplement 1. — (A) Backbone CSPs. CSPs of the sterospecifically assigned methyl groups at pH 5.5, 50 mM NaCl, 35°C. (B) Both these CSPs are painted on the ribbon representation of the structure of Zn^II₂ AdcR. The shaded bar in each case represents one standard deviation from the mean perturbation. Site 1 and site 2 ligands in the primary structure in panel A are denoted by the *yellow* and *green* circles, respectively; the asterisks at residue positions 21 and 38 – 40 indicate no assignment in the apo-state (see materials and methods), while asterisks mark residue positions 103 and 128 for prolines. Insets show the CSP values painted onto the 3tgn structure.

The changes in Cα and Cβ chemical shifts in the central region of the α5 helix and the presence of strong NOEs to water for these residues are consistent with a kink in this helix in the apo-state (Figure 3—figure supplement 2A–B), as is commonly found in other structurally characterized MarR repressors in DNA-binding incompetent conformations (Zhu et al., 2017b; Duval et al., 2013). However, the kink is expected to be local and transient, since a TALOS+ analysis of chemical shifts predicts that the α5 helix remains the most probable secondary structure for all tripeptides containing these residues in the apo-state (Shen et al., 2009) (Figure 3—figure supplement 2C). The backbone changes in chemical shifts are accompanied by changes in the hydrophobic cores in the proximity of Zn^II binding as reported by the stereospecific sidechain methyl group chemical shift perturbation maps (Figure 3B). Comparatively smaller perturbations extend to the α1 helix and the C-terminal region of the α6 helix, DNA-binding α4 helix (S74) and into the β-wing itself, consistent with a significant change in quaternary structure within the AdcR homodimer upon binding of both allosteric metal ions (Figure 3A–B).

Overall, our NMR and SAXS data show that the main structural differences between the apo- and Zn^II₂ states are localized in the region immediately surrounding the Zn^II coordination sites, giving rise to a change in quaternary structure, while conserving the size and the overall secondary structure of the molecule. In particular, our data point to a kink in the α5 helix and a structural perturbation in the α1-α2 loop, which could be inducing a reorientation of the winged helix-turn-helix motifs relative to the dimerization domain. In addition to these structural changes, metal binding seems to be restricting the α1-α2 loop dynamics by means of metal coordination bonds, a hydrogen-bond network (Chakravorty et al., 2013) and other intermolecular contacts within the dimerization and DNA binding domains (Zhu et al., 2017c). Flexibility of the α1-α2 loop could potentially destabilize the DNA complex; in this case, interactions formed as a result of Zn^II coordination may be important in allosteric activation of DNA binding. Such a dynamical model contrasts sharply with a rigid body mechanism as previously suggested for other MarRs (Alekshun et al., 2001; Chang et al., 2010; Dolan et al., 2011; Saridakis et al., 2008; Birukou et al., 2014; Radhakrishnan et al., 2014), thus motivating efforts to understand how conformational dynamics impacts biological regulation by Zn^II in AdcR.

Zn^II-induced changes in AdcR conformational plasticity along the backbone

We therefore turned to an investigation of protein dynamics in AdcR. ¹⁵N R₁, R₂, and steady-state heteronuclear ¹⁵N{¹H} NOEs provide information on internal mobility along the backbone, as well as on the overall rotational dynamics (Figure 4A–D; Figure 4—figure supplements 1–2). The R₁ and R₂ data reveal that Zn^II₂ AdcR tumbles predominantly as a single globular unit in solution with a rotational diffusion tensor and ¹⁵N R₂/R₁ ratio compatible with those parameters predicted from the crystal structure (Guerra et al., 2011) using hydroNMR (García de la Torre et al., 2000) (Figure 4B; Figure 4—figure supplement 1). The β-wing region tumbles independently from the rest of the molecule (Figure 4B, Figure 4—figure supplement 1B). These data also reveal that the α1-α2 linker region that donates the E24 ligand to Zn^II binding site one is ordered to an extent similar to the rest of the molecule (Figure 4—figure supplement 1B). In striking contrast, in apo-AdcR, the dimerization and DNA-binding domains each have a significantly smaller ¹⁵N R₂/R₁ ratio (Figure 4B), somewhat closer to what is expected if these domains tumble independently of one another in solution, which might be facilitated by a highly dynamic α1-α2 loop (see also Figure 4—figure supplement 1). These findings are consistent with the SAXS data, which show that apo-AdcR is less compact than the Zn^II₂ state. As in the Zn^II₂ state, the β−wing tumbles independently of the rest of the molecule, revealing that a change in the flexibility or orientation of the β−hairpin is likely not part of the allosteric mechanism, contrary to what has been proposed for other MarRs on the basis of crystal structures alone (Liu et al., 2017; Deochand and Grove, 2017; Kim et al., 2016). Overall, the ¹⁵N relaxation data for backbone amides suggest that Zn^II binding leads to a reduction of mobility of the α1-α2 loop, which in turn, decreases the dynamical independence the DNA-binding and dimerization domains, thereby stabilizing a conformation that tumbles in solution as a single globular unit.

Figure 4. — Backbone ¹H-¹⁵N amide R₂/R₁ for apo- (A) and Zn^II₂ AdcR (B) painted onto the 3tgn structure (Guerra et al., 2011). Heteronuclear NOE analysis of apo- (C) and Zn^II₂ (D) AdcR with the values of the ¹⁵N-{¹H}-NOE (hNOE) painted onto the 3tgn structure. Values of R_ex determined from HSQC ¹⁵N-¹H CPMG relaxation dispersion experiments at a field of 600 MHz for the apo- (E) and Zn^II₂ (F) AdcRs (see Figure 4—figure supplement 3 for complete data sets). Similar results were obtained at 800 MHz. Zn^II ions are shown as black spheres and residues excluded due to overlap are shown in gray. The width of the ribbon reflects the value represented in the color bar.

Figure 4—figure supplement 1. — Backbone ¹H-¹⁵N amide R₂/R₁ for apo- (A) and Zn^II₂ AdcR (B) painted onto the 3tgn structure (Guerra et al., 2011). Heteronuclear NOE analysis of apo- (C) and Zn^II₂ (D) AdcR with the values of the ¹⁵N-{¹H}-NOE (hNOE) painted onto the 3tgn structure. Values of R_ex determined from HSQC ¹⁵N-¹H CPMG relaxation dispersion experiments at a field of 600 MHz for the apo- (E) and Zn^II₂ (F) AdcRs (see Figure 4—figure supplement 3 for complete data sets). Similar results were obtained at 800 MHz. Zn^II ions are shown as black spheres and residues excluded due to overlap are shown in gray. The width of the ribbon reflects the value represented in the color bar.

To further probe this reduction of flexibility upon Zn^II binding, we investigated sub-nanosecond backbone mobility as reported by the steady-state heteronuclear ¹⁵N{¹H} NOEs (Figure 4C–D, Figure 4—figure supplement 1, Figure 4—figure supplement 2) and millisecond mobility as reported by ¹⁵N relaxation dispersion experiments (Figure 4E–F, Figure 4—figure supplement 3). These hNOE data confirm that the internal mobility of the apo-state on this timescale largely localizes to the β−wing, the α1-α2 loop, and the central region of the α5 helix, around E107 (Zn^II site 2 ligand) and H108 and H112 (Zn^II site 1 ligands) (Figure 4C, Figure 4—figure supplement 1). This short-timescale flexibility in these regions is significantly restricted upon Zn^II binding, but somewhat paradoxically leads to a small increase in sub-nanosecond backbone motion in the DNA-binding domain (Figure 4C–D, inset), particularly in the α2 helix, the α3 helix and the N-terminal region of the α4 helix, the latter of which harbors the key DNA-binding determinants (Figure 1—figure supplement 1A) (Zhu et al., 2017c). The Zn^II- induced quenching of sub-nanosecond mobility is also accompanied by an increase in mobility on the µs-ms (slow) timescale in the metal binding site, particularly at or near metal binding residues, including H112 (site 1) and C30 (site 2) (Figure 4F). In addition, the slow timescale backbone dynamics show a restriction of a conformational sampling in a band across the middle of the dimerization domain, including the upper region of the α5 helix, the N-terminus of α1, and the C-terminus of α6 (Figure 4E–F). These slow motions in the apo-state likely report on a global breathing mode of the homodimer reflective of the conformational ensemble, which is substantially restricted upon Zn^II binding.

These large differences in structure and dynamics between the apo and Zn^II₂ AdcRs along the backbone suggest an allosteric mechanism that relies on a redistribution of internal mobility in both fast- and slow timescales, rather than one described by a rigid body motion. This mobility redistribution effectively locks AdcR in a triangular shape compatible with DNA binding, while also inducing a small, but measurable increase in motional disorder in the DNA binding domain (Figure 4C–D). Since other studies connect changes in motional disorder like these to sequence recognition and high affinity binding to DNA, particularly in the side chains (Capdevila et al., 2017a; Kalodimos et al., 2004; Anderson et al., 2013), we decided to probe side chain dynamics in greater detail.

Zn^II-induced perturbations of side chain conformational disorder in AdcR

Sub-nanosecond timescale dynamics have been used as a proxy for the underlying thermodynamics of ligand binding and can report on the role of conformational entropy (∆S_conf) in allosteric mechanisms (Caro et al., 2017; Frederick et al., 2007; Sharp et al., 2015) The contribution of changes in backbone dynamics to the ∆S_conf of ligand binding processes measured in a number of model systems has been shown to be small (<5%), relative to the contribution to ∆S_conf from the side chains (Caro et al., 2017). However, in the case of AdcR, Zn^II binding clearly restricts the backbone dynamics of the α1-α2 loop as reflected by an increase in the N-H order parameters in this region (S²_bb, Figure 4—figure supplement 2), which sums to –T∆S_{conf, bb} to ≈3.5 kcal mol⁻¹ (see materials and methods). Thus, α1-α2 loop restriction to the internal dynamics may well be a significant contributor to the underlying thermodynamics of metal binding. Moreover, if this motional redistribution along the backbone is accompanied by changes in the internal dynamics of the side chains, particularly those in the DNA binding domain, these fast internal dynamics could greatly impact the entropy of metal binding and/or allostery. Mapping these perturbations by measuring the change in methyl group order parameter (ΔS²_axis) upon Zn^II binding, employed as dynamical proxy (Capdevila et al., 2017a; Caro et al., 2017) may in turn, pinpoint residues with functional roles, that is allosteric hotspots (Capdevila et al., 2017a; Capdevila et al., 2018).

We measured the axial order parameter, S²_axis, for all 82 methyl groups, comparing the apo- and Zn-bound states of AdcR (Figure 5—figure supplement 1). These dynamics changes are overall consistent with the stiffening observed along the protein backbone, for example in the α1-α2 loop; L26, in particular, is strongly impacted, changing motional regimes, |∆S²_axis|>0.2 (Frederick et al., 2007) (Figure 5A). We observe a significant redistribution of sidechain mobility throughout the molecular scaffold (23 probes change motional regimes), as has been previously shown for other transcriptional regulators (Capdevila et al., 2017a; Tzeng and Kalodimos, 2012), summing to a small net decrease in conformational entropy upon Zn^II coordination, –T∆S_conf,sc= 1.1 ± 0.2 kcal mol⁻¹ (Figure 5B). Note that this value is quantitatively less than that attributed to the backbone of the α1-α2 loop. However, many of the methyl groups that change motional regimes are located in the DNA binding domain (Figure 5A–B, Figure 5—figure supplement 2). In particular, the side chain flexibility of many residues in the α3 helix increases, including L47, L57, L61, while a small hydrophobic core in the C-terminus of the α4 helix stiffens significantly, for example L81, V34. These changes are accompanied by perturbations in the dynamics at the dimer interface, that is L4, I16, V142, in both motional regimes as reported by ∆S²_axis and ∆R_ex (in the µs-ms timescale), the latter derived from relaxation dispersion experiments (Supplementary file 1-Table S1; Figure 5—figure supplement 3).

On-pathway and off-pathway allosterically impaired mutants of AdcR

Our previous work (Capdevila et al., 2017a) makes the prediction that ‘dynamically active’ sidechains (methyl groups with |∆S²_axis|>0.1 upon Zn^II binding) (see Figure 5A–B) are crucial for allosteric activation of DNA binding by Zn^II. To test this prediction, we prepared and characterized several mutant AdcRs in an effort to disrupt allosteric activation of DNA binding, while maintaining the structure and stability of the dimer, and high affinity Zn^II binding. Since it was not clear a priori how mutations that perturb mobility distributions in one timescale or the other (sub-ns or µs-ms) would impact function, we focused on two kinds of substitution mutants: methyl group substitution mutants of dynamically ‘active’ side chains positioned in either the DNA binding or the dimerization subdomains (Figure 6A,B) (Capdevila et al., 2017a), and substitutions in the hydrogen-bonding pathway in the Zn-state that may contribute to the rigidity of the α1-α2 loop in Zn^II₂-AdcR (Figure 6A) (Chakravorty et al., 2013). We measured DNA binding affinities of the apo and Zn^II₂-states, and calculated the allosteric coupling free energy, ∆G_c, from ∆G_c=–RTln(K_Zn,DNA/K_{apo, DNA}) (Giedroc and Arunkumar, 2007) (Figure 6C, Figure 6—figure supplement 1 and Table 2). All mutants are homodimers by size-exclusion chromatography (Figure 6—figure supplement 2) and all bind the first protomer equivalent of Zn^II (to site 1) with wild-type-like affinity (Figure 6—figure supplement 3, Supplementary file 1-Table S1). Two of the sixteen mutants investigated here (L61V and V63A AdcRs) showed a significantly lower thermal stability as estimated by differential scanning fluorimetry (Figure 6—figure supplement 4, Supplementary file 1-Table S2); this prevented a quantitative analysis of their DNA and metal binding affinities and thus they were not considered further.

Figure 6. — (A) Cα positions of the residues targeted for methyl substitution mutagenesis in the DNA binding domain (DBD) (*red spheres*) and in the dimerization domain (DIM) (*blue spheres*); other residues targeted for substitution in the hydrogen-bonding pathway (N38, Q40; *green spheres*) and zinc ligand E24 (*yellow spheres*) highlighted on the structure of the Zn^II₂ ZitR-DNA operator complex (Zhu et al., 2017c); Zn^II ions (*black spheres*). (B) Zoom of the DNA binding domain (DBD) of one of the two Zn^II₂-bound AdcR protomers highlighting the residues targeted for mutagenesis (methyl substitution mutants, *red stick*; hydrogen-bonding pathway mutants, *green stick*; zinc ligand E24, *yellow stick*), with the helical elements (α1-α5) indicated. (C) Coupling free energy analysis for all AdcR mutants highlighted using the same color scheme as in panels A and B. DBD, DNA-binding domain; DIM, dimerization domain; H-bond, hydrogen binding mutants. K_DNA for apo-AdcRs are shown in fill circles; K_DNA for Zn^II₂ -AdcRs are shown in hollow circles. Lower horizontal line, K_DNA for wild-type apo-AdcR; upper horizontal line, K_DNA for wild-type Zn^II₂ AdcR, for reference. The trend in ∆S²_axis and ∆R_ex is qualitatively indicated (see Table 2). These residues are conserved to various degrees in AdcR-like repressors (Figure 6—figure supplement 5).

Figure 6—figure supplement 1. — (A) Cα positions of the residues targeted for methyl substitution mutagenesis in the DNA binding domain (DBD) (*red spheres*) and in the dimerization domain (DIM) (*blue spheres*); other residues targeted for substitution in the hydrogen-bonding pathway (N38, Q40; *green spheres*) and zinc ligand E24 (*yellow spheres*) highlighted on the structure of the Zn^II₂ ZitR-DNA operator complex (Zhu et al., 2017c); Zn^II ions (*black spheres*). (B) Zoom of the DNA binding domain (DBD) of one of the two Zn^II₂-bound AdcR protomers highlighting the residues targeted for mutagenesis (methyl substitution mutants, *red stick*; hydrogen-bonding pathway mutants, *green stick*; zinc ligand E24, *yellow stick*), with the helical elements (α1-α5) indicated. (C) Coupling free energy analysis for all AdcR mutants highlighted using the same color scheme as in panels A and B. DBD, DNA-binding domain; DIM, dimerization domain; H-bond, hydrogen binding mutants. K_DNA for apo-AdcRs are shown in fill circles; K_DNA for Zn^II₂ -AdcRs are shown in hollow circles. Lower horizontal line, K_DNA for wild-type apo-AdcR; upper horizontal line, K_DNA for wild-type Zn^II₂ AdcR, for reference. The trend in ∆S²_axis and ∆R_ex is qualitatively indicated (see Table 2). These residues are conserved to various degrees in AdcR-like repressors (Figure 6—figure supplement 5).

Table 2. DNA binding parameters for wild-type AdcR and substitution mutants^*.

		Zn^II		Dynamic changes (Zn^II) at 600 MHz
AdcR	K_apo,DNA (x10⁶ M⁻¹)	K_{Zn, DNA} (x10⁶ M⁻¹)	ΔG_c (kcal mol⁻¹)	ΔS²_axis	ΔRex	Fractional ASA^†
wild-type	0.5 ± 0.2	450 ± 220	–4.0 ± 0.6
I104A	0.20 ± 0.01	280 ± 30	–4.3 ± 0.4	−0.08 ± 0.01	−0.3 ± 0.6	0.04
L36A	0.07 ± 0.01	80 ± 30	–4.1 ± 0.4	0.13 ± 0.10	−2.0 ± 0.5	0.05
V34A	0.37 ± 0.17	13 ± 1	–2.0 ± 0.3	0.13 ± 0.02	−2.0 ± 0.5	0.46
L81V	0.16 ± 0.12	12 ± 8	–2.4 ± 0.6	0.13 ± 0.05	0.0 ± 0.5	0.00
L61V**	-	-	-	−0.23 ± 0.01	−1.0 ± 0.5	0.01
L57M	0.035^‡ ± 0.030	1 ± 0.2	–2.0 ± 0.7	−0.18 ± 0.02	1.0 ± 0.5	0.00
L57V**	<0.05^§	<0.05^§	N/A	−0.18 ± 0.02	1.0 ± 0.5	0.00
I16A	1.8 ± 0.9	17 ± 14	–1.8 ± 0.4	−0.08 ± 0.02	−4.0 ± 1.0	0.11
L4A	0.5 ± 0.2	11 ± 8	–1.8 ± 0.3	0.004 ± 0.045	−4.0 ± 1.0	0.01
V142A	0.41 ± 0.05	4.1 ± 2.3	–1.4 ± 0.2	−0.09 ± 0.02	−3.0 ± 1.0	0.31
I27A	0.09 ± 0.01	80 ± 3	–4.0 ± 0.2	0.03 ± 0.01	1.2 ± 0.5	0.07
L17A	0.22 ± 0.1	219 ± 36	–4.0 ± 0.2	−0.10 ± 0.02	0.0 ± 0.5	0.50
V63A**	-	-	-	0.01 ± 0.04	1.0 ± 0.5	0.24
N38A	0.05 ± 0.01	19 ± 10	–3.5 ± 0.7	–^#	–	–
N38A/Q40A	0.10 ± 0.04	2.2 ± 0.4	–1.9 ± 0.2	–	–	–
E24D	0.17 ± 0.04	2.2 ± 1.7	–1.6 ± 0.3	–	–	–

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*Conditions: 10 mM Hepes, pH 7.0, 0.23 M NaCl, 1 mM TCEP (chelexed), 10 nM DNA, 25.0°C with 2.0 mM EDTA (for apo-AdcR) or 20 μM ZnCl₂ (for Zn^II₂ AdcR) added to these reactions. See Figure 6C, for a graphical representation of these data. All ∆G_c values lower than −3.5 kcal mol^-1, with the exception of N38A AdcR are statistically significantly different (p≤0.1) from the wild-type ∆G_c value.

†Accessible surface area (ASA) was calculated from the Zn^II₂-bound AdcR (Guerra et al., 2011) using the web server for quantitative evaluation of protein structure VADAR 1.8 (vadar.wishartlab.com/).

‡Upper limit on measureable K_apo,DNA under these solution conditions.

§ Weaker than upper limit.

#Not measurable using the NMR experiments employed here.

**Significantly lower thermal stability as estimated by differential scanning fluorimetry (Supplementary file 1-Table S2) prevented a quantitative analysis of their DNA and metal binding affinities.

DNA-binding domain mutants

The redistribution of fast time scale side-chain dynamics in the DNA binding domain is delocalized throughout the different secondary structure motifs (Figure 5A–B). Thus, we prepared several methyl substitution mutants of methyl-bearing residues in the α3 (L57, L61, V63), α4 (L81) and α5 (I104) helices, as well as two residues in the α1-α2 loop, V34 and L36. I104 and V63 are not dynamically active in AdcR (|∆S²_axis|<0.1; ∆R_ex <1.0); thus, these mutant are predicted to function as control substitutions. V34 and L36 are dynamically active on both timescales, which is not surprising since the α1-α2 loop folds upon Zn^II binding to AdcR (vide supra) (Zhu et al., 2017c). In contrast, L57, L61 and L81 are characterized by significant perturbations in ∆S²_axis only (|∆S²_axis|≥0.2), with L81 stiffening and L57 and L61 methyls in the α3 helix becoming significantly more dynamic upon Zn^II binding (Figure 5A, Table 2). To investigate the functional role of these residues, we chose missense substitutions (Table 2) generally designed to restrict the number of χ angles (Leu to Val or Ala; Val to Ala) and thus impact their dynamical sensitivities (Capdevila et al., 2017a; Capdevila et al., 2018); in one case (L57), we introduced multiple substitutions, with one characterized by a larger number of χ angles (Leu to Met).

As expected, I104A AdcR is characterized by a DNA binding affinity in the apo- and Zn-states just ≈2-fold lower than wild-type AdcR, returning a ∆G_c that is not statistically different from wild-type AdcR (Figure 6C). Functional characterization of all other methyl substitution mutants in the DNA binding domain results in a ≈5–10-fold decrease or greater (L57V AdcR; Table 2) in the DNA binding affinity of the apo-state (Figure 6C), with Zn^II binding inducing markedly variable degrees of allosteric activation (Figure 6C). L36A, closest to the N-terminus of the α2 helix, is most like wild-type AdcR, while L81V AdcR is severely allosterically crippled, with K_Zn,DNA some 40-fold lower than wild-type AdcR, and ∆G_c ≈2-fold lower, from –4.0 to –2.4 kcal mol⁻¹. L57M AdcR is even more strongly perturbed (∆G_c≈–2.0 kcal mol⁻¹). V34A AdcR shows a comparable degree of functional perturbation, while effectively retaining binding of Zn^II only to site 1, like V142A AdcR (discussed below; Supplementary file 1-Table S1). We emphasize that these methyl-bearing side chains targeted for substitution are not expected to be in direct contact with the DNA, based on solvent accessible area (Table 2) and distance from the DNA binding interface (Figure 6B, Figure 6—figure supplement 5). With the exception of L36A AdcR, the functional impact of each residue substitution correlates with the magnitude of the dynamical perturbations on that residue. This finding provides additional support for the idea that those methyl-bearing side chains in the DNA-binding domain that exhibit large changes in conformational entropy (as measured by ∆S²_axis) make significant contributions to both DNA binding and allosteric activation by Zn^II (Tzeng and Kalodimos, 2012; Capdevila et al., 2017a). Further characterization of the structural and dynamical impact of these substitutions is necessary to confirm that the functional impact of each is a consequence of dynamical perturbations rather than minor structural changes that would escape detection.

To evaluate the possible contributions of backbone dynamics and structural changes, we purified ¹⁵N-labelled V34A and L57M AdcRs. Unfortunately, the thermal stability of V34A AdcR at the slightly acidic pH and temperature (35°C) required to yield high quality NMR spectra proved inadequate (Supplementary file 1-Table S2, Figure 6—figure supplement 4) and it was therefore not investigated further. L57M AdcR, on the other hand, yielded excellent quality spectra in both apo and Zn^II₂ allosteric states, readily yielding backbone resonance assignments (Figure 6—figure supplement 6), which could be used to undertake a detailed backbone dynamics characterization. Although the structural changes upon Zn^II binding are wild-type-like as reported by a chemical shift perturbation map, the impact of the mutation is not restricted to the α3 helix but also affects the α2 helix as anticipated by the crystal structure (Figure 6—figure supplement 7). While the backbone dynamics are largely indistinguishable from wild-type AdcR on both timescales (Figure 6—figure supplement 8–12), there are several small differences in the DNA-binding domain in the immediate vicinity of M57 that could contribute to the allosteric impact of the L57M mutation (Figure 6—figure supplement 8–11). By and large, however, wild-type and L57M AdcRs are rather dynamically similar along the backbone, thus implicating side chain conformational entropy redistribution as an important contributor to allostery in this system. However, it should be noted that although the structural impact of the L57M mutation is likely small and localized as suggested by the chemical shift perturbation maps (Figure 6—figure supplement 7), the effect of a small structural perturbation by M57 can not be ruled out.

Hydrogen-bonding mutants

A candidate hydrogen-bonding pathway in AdcR (Chakravorty et al., 2013) was previously proposed to transmit the Zn^II₂ binding signal to the DNA binding domain. In this pathway, the Oε1 atom from the Zn^II ligand E24 accepts a hydrogen bond from the carboxamide side chain of N38. N38 is the +1 residue of the α2 helix, which is then connected to the α4 helix via a hydrogen bond between the Q40 and S74 side chains; further, Q40 accepts a hydrogen bond from the γ-OH of T37 as part of a non-canonical helix N-capping interaction (Guerra et al., 2011) (Figure 6A). We expect that regardless of the impact that these interactions have on the overall energetics of Zn^II binding, they are important in the restriction of fast-time scale dynamics in the α1-α2 loop. We therefore targeted residues E24 (Zn-ligand and H-bond acceptor), N38 and Q40, by characterizing two single mutants, E24D and N38A, and the double mutant, N38A/Q40A AdcR. Although all three mutants undergo conformational switching upon Zn-binding as revealed by ¹H−¹⁵N TROSY spectra (Figure 6—figure supplement 12) all three exhibit ≈5 – 10-fold decreases in apo-state DNA-binding affinity (Figure 6C; Table 2). While the single mutant N38A binds Zn^II to give ∆G_c of ≈–3.5 kcal mol⁻¹, quite similar to that of wild-type AdcR, in marked contrast, N38A/Q40A AdcR is functionally perturbed, characterized by a ∆G_c of ≈–1.9 kcal mol⁻¹ as is E24D AdcR, which targets a Zn^II binding residue (Figure 6C). These perturbations provide additional evidence that this hydrogen-bonding pathway may contribute to the motional restriction of the α1-α2 loop, jointly with a redistribution of internal dynamics in the DNA binding domain. This effect can be perturbed directly by mutation of ‘dynamically active’ sidechains (L81V, L57M) or by significantly impacting the interactions that restrict the loop (N38A/Q40A).

Dimerization domain mutants

To test the functional role of the dimerization domain in dynamical changes, we targeted four methyl-bearing residues in this domain, including L4, I16 and L17 on opposite ends of the α1 helix; V142, near the C-terminus of the α6 helix (Figure 6B) and I27 a α1-α2 loop in the proximity of V142. I16 and L17 are closer to the intervening minor groove of the DNA operator, while V142, I27 and L4 are increasingly distant from the DNA. With the exception of L17 and I27, these side chains are primarily active in slow timescale dynamics, with Zn^II-binding quenching side chain mobility on the µs-ms timescale, that is, global motions, but relatively smaller changes in ∆S²_axis (Figure 5B; Table 2). Methyl substitution mutants of these residues (I16A, L4A and V142A) bind DNA in the apo-state with wild-type like affinities, but each is allosterically strongly perturbed, with only ≈10 – 20-fold allosteric activation by Zn^II, giving ∆G_c values of –1.4 to –1.8 kcal mol⁻¹. On the contrary, L17A and I27A AdcR shows a wild-type-like ∆G_c, consistent with the fact that L17 and I27 are nearly dynamically silent upon Zn binding (Figure 5B).

These findings suggest that Zn^II-dependent quenching of global motions far from the DNA binding domain play a significant role in allostery in this system. Our characterization of allosterically compromised mutants that affect site-specific conformational entropy (L81V, L57M) and conformational exchange (V34A, L4A, I16A) provides evidence for two classes of functional dynamics in AdcR that comprise different regions of the molecule, operating on different timescales (from sub-nanoseconds to milliseconds). Thus, we propose that a Zn^II-dependent redistribution of internal dynamics quenches global, slow and fast motions in the dimer, yet detectably enhances local dynamical disorder in the DNA binding domain, which we propose can ultimately be harnessed to maximize contacts at the protein-DNA interface.

Conclusions

Members of the multiple antibiotic resistance repressor (MarR) family of proteins comprise at least 12,000 members (Capdevila et al., 2017b), and many have been subjected to significant structural inquiry since the original discovery of the E. coli mar operon and characterization of E. coli MarR some 25 years ago (Cohen et al., 1993; Seoane and Levy, 1995). The crystallographic structure of this prototypical E. coli MarR appeared a few years later (Alekshun et al., 2001) and has inspired considerable efforts to understand the inducer specificity and mechanisms of transcriptional regulation in E. coli MarR (Hao et al., 2014) and other MarR family repressors (Grove, 2013), which collectively respond to an wide range of stimuli, including small molecules, metal ions, antibiotics and oxidative stress (Deochand and Grove, 2017). We have examined the wealth of crystallographic data available from 135 MarR family repressor structures solved in a variety of functional states, including DNA-binding competent, DNA-binding incompetent and DNA-bound states (Figure 1). This analysis of the crystal structures suggests that a conformational ensemble model of allostery must be operative in a significant number of these repressor systems, where ligand binding or thiol oxidation narrows the conformational spread and, thus, activates or inhibits DNA binding. Here, we present the first site-specific dynamics analysis of any MarR family repressor in solution, and establish that conformational dynamics on a range of timescales is a central feature of Zn^II-dependent allosteric activation of DNA operator binding by the zinc uptake regulator S. pneumoniae AdcR (Reyes-Caballero et al., 2010) and closely related repressors (Zhu et al., 2017c).

We explored dynamics in the sub-nanosecond and ms timescales with residue-specific resolution, both along the backbone, as measured by N-H bond vectors, and in the methyl groups of the methyl-bearing side chains of Ala, Met, Val, Leu and Ile. These measurements, coupled with small angle x-ray scattering measurements of both conformational states, lead to a self-consistent picture of allosteric activation by Zn^II in AdcR. The apo-state conformational ensemble is far broader than the Zn^II₂ state, and features at least partial dynamical uncoupling of the core DNA-binding and dimerization domains, facilitated by rapid motions in the α1-α2 loop and the α5 helix in the immediate vicinity of the Zn^II coordinating residues. This motion is superimposed on much slower motions across the dimerization domain, far from the DNA interface, which affect both backbone amide and side chain methyl groups (Figures 4–5). Zn^II binding substantially quenches both the low amplitude internal motions and global, larger amplitude movements like the ones reflected by SAXS data, with an accompanying redistribution of these dynamics into the DNA-binding domain.

As observed previously for another Zn^II metalloregulatory protein (Capdevila et al., 2018), Zn^II binding induces a small, net global conformational stiffening of the internal dynamics or sub-ns motions; however, in AdcR, there are significant contributions from both the backbone (in folding the α1-α2 loop) and the methyl-bearing side chains upon Zn^II binding. These are superimposed on pockets of increased dynamical disorder, particularly in the α2-α3 loop along the backbone (Figure 4), and in the α3-α4 region of the DNA binding domain (Figure 5). To test the functional importance of both these fast-time scale motions in the DNA binding domain, as well as slow timescale dynamics in the dimerization domain, we exploited these side chain dynamics results (Figure 5) (Capdevila et al., 2017a) to guide our introduction of methyl substitutions of both dynamically active and dynamically silent residues (Figure 6). We generally find that methyl substitutions in the DNA binding domain are strongly deleterious for residues that are dynamically active in the fast timescale (|∆S²_axis|>0.2), that is L81, L61, L57. The same is true of dynamically active slow timescale residues,that is L4, I16 and V142. These findings confirm a functional role of these pronounced changes in dynamics (Capdevila et al., 2017a; Capdevila et al., 2018) and suggest that Zn^II₂-bound AdcR has an optimal distribution of internal millisecond dynamics that if perturbed, leads to weakened DNA binding affinity in the allosterically active Zn-bound state.

The extent to which this dynamics-centered regulatory model characterizes other MarR family repressors in solution is of course unknown. However, the differences between the crystal structures of the DNA binding-competent and incompetent states appear sufficient to adequately describe the allosteric mechanism in only a handful of MarR repressors (Figure 1). From this perspective, it is interesting to speculate on the evolutionary origin of allosteric activation and allosteric inhibition within this simple molecular scaffold. Clearly, models that invoke only rigid body domain motions as contributing to allostery (Alekshun et al., 2001; Chang et al., 2010; Dolan et al., 2011; Saridakis et al., 2008; Birukou et al., 2014; Radhakrishnan et al., 2014) would fail to capture the evolution of allosteric activation vs. inhibition from a common progenitor repressor (Motlagh et al., 2014). Further, we have previously speculated that nature is capable of harnessing dynamics properties and entropy reservoirs to evolve new inducer specificities in another structural class of bacterial repressors (Capdevila et al., 2017a).

Here, we propose that both internal dynamics, reflected in a more favorable conformational entropy term, and structural features, reflected in a more favorable ΔH term, were originally optimized in a common progenitor MarR that was capable of transcriptionally repressing genes that became deleterious when colonizing a new environment (Deochand and Grove, 2017). Then, any set of sequence variations could allow for the emergence of both allosteric activation and inhibition. For example, introduction of a dynamic element(s), that is loops or disordered regions (Pabis et al., 2018; Campbell et al., 2016) would impact both coupled fast sub-ns motions and concerted slower motions and as a result, introduce an entropic penalty that leads to inhibition of DNA-binding. Indeed, a structural comparison and an extensive multiple sequence alignment reveals that only AdcR-like repressors harbor an α1-α2 loop larger than 10 residues (Figure 7A), and that ligand (Zn^II) binding to what we now know is a highly dynamical loop element, becomes an important feature of allosteric activation of DNA binding.

Figure 7. — Proteins that are DNA binding-competent in the apo- state and DNA binding-incompetent in the ligand-bound state are colored in *red*, while proteins that are DNA binding-incompetent in the apo-state and DNA binding-competent in the liganded state are colored in *blue* (see Figure 1—source data 1 for a full accounting of these structures). A schematic representation of allosteric inhibition and activation are shown (*inset*), with shorter α1-α2 loops associated with allosteric inhibition of DNA binding upon ligand binding, while longer loops are associated with allosteric activation (like that for AdcR/ZitR) upon ligand binding. (B) Dynamically driven model for how conformational dynamics can be harnessed to evolve allosteric activation (*upper right*) vs. allosteric inhibition (*lower right*) in the same molecular scaffold. This model suggests that dynamic properties of the DNA binding competent states have been conserved to give rise to a more favorable conformational entropy. In the metalloregulatory MarRs (AdcR, ZitR), the inactive state shows perturbed dynamics over a range of timescales; apo-AdcR therefore exhibits low affinity for DNA. Metal ion (*yellow circle*) coordination quenches both local and global modes in the dimerization domain and linkers, while inducing conformational disorder in the DNA-binding domain that enhances DNA binding affinity, thus stabilizing a conformation that has high affinity for DNA and giving rise to a favorable conformational entropy. For prototypical MarRs, where the ligand (*yellow star*) is an allosteric inhibitor, ligand binding narrows the conformational ensemble to a DNA-binding incompetent conformation decreasing the enthalpic contribution to DNA binding.

On the other hand, allosteric inhibition could have arisen from sequence variations that define a pocket where ligand binding disrupts structural (Hong et al., 2005; Dolan et al., 2011; Quade et al., 2012; Birukou et al., 2014; Zhu et al., 2017a; Gao et al., 2017; Otani et al., 2016) and/or dynamical features (Capdevila et al., 2017a) of a DNA binding-competent conformation (Figure 7B). Although the presence of functionally important entropic reservoirs on any allosterically inhibited MarR has not yet been reported experimentally, molecular dynamics simulations show that DNA binding-impaired mutants of MexR differ from the wild-type repressor in the nature of the dynamical connection between the dimerization and DNA binding domains (Anandapadamanaban et al., 2016). This dynamical connectivity is in fact exploited by the binding the ArmR peptide, leading to DNA dissociation (Anandapadamanaban et al., 2016; Wilke et al., 2008). We propose that conformational entropy can contribute to other mechanisms of allosteric inhibition to yield a repressor that binds tightly to the operator sequence and yet has the ability to readily evolve new inducer specificities.

It is interesting to note that mutations that lead to inactivation are not necessarily part of a physical pathway with the DNA binding site (Clarke et al., 2016), since they only need to affect dynamical properties that are likely delocalized in an extended network. Notably, single point mutants in the dimerization domain of various MarR family repressors have been shown to modulate allostery and DNA binding (Anandapadamanaban et al., 2016; Deochand et al., 2016; Liguori et al., 2016; Duval et al., 2013; Alekshun and Levy, 1999; Andrésen et al., 2010), perhaps exemplified by the L4, I16 and V142 AdcR substitution mutants. In AdcR, while structural perturbations induced by Zn^II binding are essentially confined to the Zn^II binding pocket, dynamical perturbations extend all over the molecule, and feature many residues that are far from either ligand binding site, and are dynamically active on the sub-nanosecond and/or µs-ms timescales (Figures 4–5). Thus, a conformational entropy contribution that is inherently delocalized and easily perturbed can enable rapid optimization of new inactivation mechanisms that would allow new biological functionalities to emerge (Figure 7). These findings inspire efforts to explore the evolution of allostery in this remarkable family of transcriptional repressors, by exploiting an allosterically crippled AdcR, for example L57M AdcR, to re-evolve allostery in this system.

Materials and methods

AdcR mutant plasmid production

An overexpression plasmid for S. pneumoniae AdcR in a pET3a vector was obtained as previously described and was used as a template for the production of all mutant plasmids (Reyes-Caballero et al., 2010). Mutant AdcR plasmids were constructed by PCR-based site-directed mutagenesis, and verified using DNA sequencing.

Protein production and purification

AdcR plasmids were transformed into either E. coli BL21(DE3) pLysS or Rosetta cells. E. coli cultures were either grown in LB media or M9 minimal media supplemented with ¹⁵NH₄Cl as the sole nitrogen source with simple ¹H,¹⁵N HSQC spectroscopy to assess the structural integrity of selected mutant proteins. Protein samples for backbone and methyl group assignments of AdcR were isotopically labeled using published procedures as described in our previous work (Capdevila et al., 2017a; Arunkumar et al., 2007), with all isotopes for NMR experiments purchased from Cambridge Isotope Laboratories. Protein expression and purification were carried out essentially as previously described (Reyes-Caballero et al., 2010). All proteins were confirmed to have <0.05 molar equivalents of Zn(II) as measured by atomic absorption spectroscopy and were dimeric by gel filtration chromatography. The AdcR protein concentration was measured using the estimated molar extinction coefficient at 280 nm of 2980 M⁻¹ cm⁻¹.

Small angle x-ray scattering experiments

Small angle and wide angle x-ray scattering data of the apo and Zn^II₂ states of AdcR was collected at three different protein concentrations (5 mg/mL, 2.5 mg/mL and 1.25 mg/mL) in buffer 25 mM MES pH 5.5, 400 mM NaCl, 2 mM EDTA/10 μM ZnCl₂, 2 mM TCEP at sector 12ID-B at the Advanced Photo Source (APS) at Argonne National Laboratory. For each protein concentration and matching background buffer, 30 images were collected and averaged using NCI-SAXS program package. The scattering profile at each concentration was manually adjusted with the scale factor to remove the effect of concentration prior to subtraction of the scattering profile of the buffer. Scattering profiles of each protein concentration were then merged for further analysis. The GUINIER region was plotted with ln (I(q)) vs q² to check for monodispersity of the sample and to obtain I₀ and the radius of gyration (R_g) within the range of q_max*R_g <1.3. The R_g values obtained for apo-AdcR and Zn(II)-bound-AdcR are 25.5 ± 0.9 Å and 23.7 ± 1.1 Å, respectively. The scattering profiles of each AdcR conformational state was then normalized with I₀. The compaction of each states of AdcR was examined using the Kratky plot for q < 0.3 Å⁻¹. Scattering profiles for apo and Zn^II₂ states of AdcR were then Fourier-transformed using GNOM of the ATSAS package to obtain the normalized pair-wise distance distribution graph (PDDF).

Ab initio modeling was performed using the program DAMMIF in a slow mode (Franke and Svergun, 2009). For each conformational state of AdcR, 10 models were obtained. These models were compared, aligned and averaged using the DAMSEL, DAMSUP, DAMAVER, DAMFILT, respectively, as described in the ATSAS package (http://www.embl-hamburg.de/bioSAXS). Normalized spatial discrepancy (NSD) between each pair of the models was computed. The model with the lowest NSD value was selected as the reference against which the other models were superimposed. Outliner models (two models) with an NSD above mean +2*standard deviation of NSD were removed before averaging. For refinement, the averaged envelope of the first run was used as search volume for the second round of modeling. Modeling of the envelope of apo-AdcR was restrained by enforcing P₂ rotational symmetry while that Zn^II₂ AdcR was restrained using compact, hallow and no-penalty constraints. Scattering profiles of crystal structures were calculated using the fast x-ray scattering (FOXS) webserver (https://modbase.compbio.ucsf.edu/foxs/) (Schneidman-Duhovny et al., 2010).

NMR spectroscopy

NMR spectra were acquired on a Varian VNMRS 600 or 800 MHz spectrometer, each equipped with a cryogenic probe, at the Indiana University METACyt Biomolecular NMR laboratory. The two-dimensional spectra were processed using NMRPipe (Delaglio et al., 1995). The three-dimensional spectra were acquired using Poisson-gap non-uniform sampling and reconstructed using hmsIST (Hyberts et al., 2012) and analyzed using Sparky (Lee et al., 2015) or CARA (http://cara.nmr.ch). Typical solution conditions were ~500 µM protein (protomer), 25 mM MES pH 5.5, 50 mM NaCl, 1 mM TCEP, 0.02% (w/v) NaN₃, and 10% D₂O. Some spectra were recorded at pH 6.0 as indicated. Our previous NMR studies of AdcR (Guerra et al., 2011; Guerra and Giedroc, 2014) were carried out with samples containing ≈70% random fractional deuteration, pH 6.0, 50 mM NaCl, 35°C; under those conditions, the backbone amides of residues 21 – 26 in the α1-α2 loop and harboring zinc ligand E24 as well as the N-terminal region of the α2 helix (residues 37 – 40) exhibited significant conformational exchange broadening in the apo-state and could not be assigned (Guerra et al., 2011). In this work, we acquired comprehensive ¹H-¹⁵N TROSY-edited NMR data sets at 600 and 800 MHz for a 100% deuterated AdcR sample in both apo- and Zn₂-bound states at pH 5.5, 50 mM NaCl, 35° C. Under these conditions, only four backbone amides residues in the apo-state were broadened beyond detection (residues 21, 38 – 40); all were visible and therefore assignable in the Zn^II₂ state. Thus, the N-terminus of the α2 helix, including N38 and Q40 are clearly exchange broadened in the apo-state. Sidechains were assigned following published procedures as described in our previous work (Capdevila et al., 2017a; Arunkumar et al., 2007). The Leu and Val methyl resonances were distinguished using through-bond information such as HMCMCBCA or HMCM[CG]CBCA experiments (Tugarinov and Kay, 2003) which correlate the Leu or Val methyl resonances with other side chain carbon resonances. All apo-protein samples contained 1 mM EDTA. All Zn^II₂ samples contained two monomer mol equiv of Zn^II. Chemical shifts were referenced to 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS; Sigma) (Wishart and Sykes, 1994). Chemical shift perturbations (CSP) of the backbone and methyl groups upon Zn^II binding or mutation were calculated using ¹H and ¹⁵N chemical shifts of the methyl groups (Δδ=(Δδ_H)²+ 0.2(Δδ_N)²) and ¹H and ¹³C chemical shifts of the methyl groups (Δδ=(Δδ_H)²+ 0.3(Δδ_C)²), respectively.

¹⁵N spin relaxation rates, R₁ and R₂, and ¹H-¹⁵N heteronuclear NOE (hNOE) values were measured using TROSY pulse sequences described elsewhere (Zhu et al., 2000) on the 100% deuterated AdcR sample. The relaxation delays used were 0.01, 0.05, 0.11, 0.19, 0.31, 0.65, 1, 1.5, 1.9, 2.3, 2.7, and 3.2 s for R₁ and 0.01, 0.03, 0.05, 0.07, 0.09, 0.11, 0.13, 0.15, 0.19, and 0.25 s for R₂. Residue-specific R₁ and R₂ values were obtained from fits of peak intensities vs. relaxation time to a single exponential decay function, while hNOE ratios were ascertained directly from intensities in experiments recorded with (2 s relaxation delay followed by 3 s saturation) and without saturation (relaxation delay of 5 s). Theoretical hNOEs values were estimated using the Solomon equation that takes into account the fact that the recycle delay is not much longer than T₁ (Gong and Ishima, 2007; Freedberg et al., 2002; Lakomek et al., 2012). Errors in hNOE values were calculated by propagating the error from the signal to noise.

Values of rotational correlation times were obtained from Monte Carlo simulations with tensor2 software (Dosset et al., 2000), using T₁, T₂, and heteronuclear NOE (hNOE) recorded at 35°C at 800 MHz, in 10% D₂O (Figure 4—figure supplement 2). A chemical shift anisotropy (CSA) angle of value of 17 degrees was used for these calculations. For apo- and Zn^II₂ AdcRs, the τ_c obtained in this way is 16.9 ± 0.1 ns and 21.1 ± 0.1 ns respectively. The results for Zn^II₂-AdcR were in very good agreement with the correlation time and relaxation rates obtained from HydroNMR (García de la Torre et al., 2000) for the crystal structure of Zn^II₂-AdcR (3tgn, τ_c=20 ns, Figure 4—figure supplement 1, grey lines). A value of the atomic radius element of 3.2 Å and the known viscosity for water at 35°C (Cho et al., 1999) were used for this calculation.

S²_axis of the Ile δ1, Leu δ1/δ2, Val γ1/γ2, Ala β, and Met ε methyl groups in apo and Zn(II)₂ states were determined using ¹H spin-based relaxation experiments at 600 MHz at 35.0°C (Tugarinov et al., 2007). S²_axis values, cross-correlated relaxation rates, η, between pairs of ¹H–¹H vectors in ¹³CH₃ methyl groups were measured using Equation. 2

η = \frac{R_{2, H}^{F} - R_{2, H}^{S}}{2} \approx \frac{9}{10} {(\frac{μ_{o}}{4 π})}^{2} {[P_{2} (c o s θ_{a x i s, H H})]}^{2} \frac{S_{a x i s}^{2} γ_{H}^{4} ħ^{2} τ_{c}}{r_{H H}^{6}}

(2)

where τ_c is the tumbling time of the protein; R^F_2,H and R^S_2,H are the fast and slow relaxing magnetization, respectively; γ_H is the gyromagnetic ratio of the proton; and r_HH is the distance between pairs of methyl protons.

In order to obtain an approximation of the differences in fast and slow relaxation rates (2η, we measured the time-dependence of the cross peak intensities in a correlated pair of single and double quantum (2Q) experiments (Tugarinov et al., 2007). Using various delay time, T, values (3, 5, 8, 12, 17, 22, and 27 ms, recorded in an interleaved manner), the rates of η were obtained by fitting ratios of peak intensities measured in pairs of experiments (I_a and I_b, spin-forbidden and spin-allowed, respectively) with Equation. 3:

\frac{I_{a}}{I_{b}} = \frac{- 0.5 η t a n h (\sqrt{η^{2} + δ^{2}} T)}{\sqrt{η^{2} + δ^{2}} - δ t a n h (\sqrt{η^{2} + δ^{2}} T)}

(3)

where T is the variable delay time, δ is a parameter that is related to the ¹H spin density around the methyl group, and I_a and I_b are the time dependencies of differences and sums, respectively, of magnetization derived from methyl ¹H single-quantum transitions, as described (Tugarinov et al., 2007). Peak heights and spectral noise were measured in Sparky (Lee et al., 2015). A python script (Source code 1) was used to fit the peak height ratios to η values and to determine S²_axis values in the apo- or Zn-bound states, as described previously (Tugarinov and Kay, 2004; Tugarinov et al., 2007; Capdevila et al., 2017a). τ_c was obtained from Monte Carlo simulations with tensor2 software.

The conformational entropy between Zn and apo states was obtained using a methyl order parameters, S²_axis, as dynamical proxy (Caro et al., 2017):

{- T ∆ S}_{C O N F, s c, a \to b}^{} = - T (- 0.00116 kcal {mol}^{-1} K^{-1}) N_{χ}^{prot} [⟨S_{b}^{2}⟩ - ⟨S_{a}^{2}⟩]

(4)

where N_χ^prot is the total number of side-chain torsion angles in the protein dimer.

We also evaluated the contribution of the changes in the backbone dynamics using previously reported calibration curve for backbone entropy obtained from molecular dynamics simulations (Sharp et al., 2015):

- T Δ S_{c o n f, b b, a \to b}^{} = - T (0.0017 kcal {mol}^{-1} K^{-1}) N_{r e s}^{prot} [⟨ l n (1 - S_{N H, b}^{2}) - l n (1 - S_{N H, a}^{2}) ⟩]

(5)

where N_res^prot is the total number of residues in the protein dimer (292 in the case of AdcR). This calculation was performed only for residues that had $S_{N H}^{2}$ <0.8 in at least one of the allosteric states.

Relaxation dispersion measurements were acquired using a TROSY adaptation of ¹⁵N and a ¹H-¹³C HMQC-based Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence for amides from the backbone (Tollinger et al., 2001) and methyl groups from the sidechains (Korzhnev et al., 2004), respectively. Experiments were performed at 35°C at 600 and 800 MHz ¹H frequencies using constant time interval T = 40 ms with CPMG field strengths (ν_CPMG) of 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 600, 700, 850, and 1,000 Hz. Peak intensities in CPMG experiments were converted to effective transverse relaxation rates (R_2,eff) using the equation, R_2,eff = (−1/T) ln(I/I₀), where I and I₀ are peak intensities measured with and without the CPMG delay (Korzhnev et al., 2004). We estimated the exchange regime from the analysis of the R_2,eff dependence with the B₀ (Millet et al., 2000). Since all the measured probes had values compatible with a fast exchange regime, variation in R_2,eff as a function of CPMG pulsing frequency was fit to:

R_{2, e f f}^{} = R_{2} + R_{e x} . [1 - 2 τ v_{C P M G} \tanh (\frac{1}{2 τ . v_{C P M G}})]

(6)

The authors note that this analysis fails to provide several additional details that could be obtained from the full Carver-Richards equations such as populations and chemical shift differences, however to obtain those parameters it is necessary to have a significant number of probes in slow or intermediate exchange (Kovrigin et al., 2006).Most of the probes that show significant exchange share similar values of τ and there was no significant improvement in the fit using a residue-specific τ, so a two-state model was preferred (Source code 2). The global τ for each state was obtained by averaging the fitted τs for all well-fit probes showing significant exchange, and evaluated by the reduced χ2 (Source code 3). R_ex values were included in the analysis only if the reduced χ2 value for the fit fell under the threshold of 1.7. The χ2 values for representative probes are shown in Figure 4—figure supplement 3, Figure 5—figure supplement 3, and Figure 6—figure supplement 11.

DNA binding experiments and determination of allosteric coupling free energies (∆G_c)

For all DNA binding experiments a 28 bp double stranded DNA was obtained as previously described (Reyes-Caballero et al., 2010) with the following sequence of the AdcO: 5’-TGATATAATTAACTGGTAAACAAAATGT[F]−3’. Apo AdcR binding experiments were conducted in solution conditions of 10 mM HEPES, pH 7.0, 0.23 M NaCl, 1 mM TCEP (chelexed), 10 nM DNA, 25.0°C with 2.0 mM EDTA (for apo-AdcR) or 20 μM ZnCl₂ (for Zn^II₂ AdcR) added to these reactions. Anisotropy experiments were performed on an ISS PC1 spectrofluorometer in steady-state mode with Glan-Thompson polarizers in the L-format. The excitation wavelength was set at 494 nm with a 1 mm slit and the total emission intensity collected through a 515 nm filter. For Zn(II)-bound-AdcR DNA-binding experiments, the data were fit with DynaFit (Kuzmic, 1996) using a non-dissociable dimer 1:1 dimer:DNA binding model (K_dim = 10¹² M⁻¹) (Source code 4). For Zn(II)-bound experiments, the initial anisotropy (r₀) was fixed to the measured value for the free DNA, with the anisotropy response of the saturated protein:DNA complex (r_complex) optimized during a nonlinear least squares fit using DynaFit (Kuzmic, 1996). Apo binding data were fit in the same manner, except r_complex was fixed to reflect the anisotropy change (r_complex – r₀) observed for wild-type AdcR in the presence of zinc. The errors on K_apo,_DNA and K_Zn,DNA, reflect the standard deviation of 3 independent titrations (Table 2). The coupling free energies were calculated using the following equation:

∆G_c= −RTln(K_Zn,DNA/K_apo,_DNA)(Giedroc and Arunkumar, 2007). Negative values of ∆G_c were observed since AdcR is a positive allosteric activator in the presence of Zn^II (K_apo,_DNA <K_Zn,DNA,).

Mag-fura-2 competition assays

All mag-fura-2 competition experiments were performed on an ISS PC1 spectrofluorometer in operating steady-state mode or a HP8453 UV-Vis spectrophotometer as described in our previous work (Capdevila et al., 2017a; Campanello et al., 2013) using the following solution conditions: 10 mM Hepes, pH 7.2, 400 mM NaCl that was Chelex (Bio-rad) treated to remove contaminating metals. 10 mM protein concentration was used for all and MF2 concentration ranged from 13 to 16 μM. These data were fit using a competitive binding model with DynaFit (Kuzmic, 1996) (Source code 5) to determine zinc binding affinities for wild-type and each mutant AdcR using a four-site-nondissociable homodimer binding model, as previously described (Reyes-Caballero et al., 2010) with K_Zn = 4.9×10⁶ M⁻¹ for mag-fura-2 fixed in these fits. K₁ and K₂ correspond to filling the two high affinity sites (site 1), and only a lower limits (≥10⁹ M⁻¹) could be obtained for these sites; K₃ and K₄ were allowed to vary in the fit, and are reported in Supplementary file 1-Table S1. Experiments were conducted three times for each AdcR variant. Errors of the binding constant parameters were estimated from global fits.

SYPRO orange Differential Scanning Fluorimetry assays

All SYPRO Orange assays were done in triplicate 25 μL reactions on a 96-well plate in a PCR machine in a chelexed buffer containing 10 mM Hepes, pH 7.0, 0.23 M NaCl, 1 mM TCEP. 4 – 8 μM protein concentration and 5x SYPRO orange were added to all reactions (Niesen et al., 2007). 10 μM EDTA was added to apo-AdcR melts to remove any contaminating metals from apo-AdcR samples. For Zn^II₂ AdcR samples, two protomer mol-equivalents of ZnCl₂ were added to these reactions (for Zn^II₂ AdcR). Other assays were carried out in solution conditions used for NMR spectroscopy, 25 mM MES, pH 5.5, 50 mM NaCl, 1 mM TCEP (chelexed), and 4 – 8 μM protein concentration and 5x SYPRO orange. The temperature was increased from 25°C to 95°C at a ramp rate of 1°C per minute. Apparent melting temperatures (T_m) were determined from the maximum of the first derivative of the florescence signal in each data set. Errors were determined from the standard deviation derived from triplicate measurements.

Acknowledgements

We gratefully acknowledge support of this work by the NIH (R35 GM118157 to DPG). NMR instrumentation in the METACyt Biomolecular NMR Laboratory at Indiana University was generously supported by a grant from the Lilly Endowment. DAC acknowledges support from the Pew Latin American Fellows Program in the Biomedical Sciences and the Fundación Williams. We also thank Dr. Lixin Fan of the Small-Angle X-ray Scattering Core Facility, National Cancer Institute, Frederick, MD for acquiring the SAXS data. We appreciate the use of pulse sequences modified by Dr. Marco Tonelli, NMRFAM, for T₁, T₂, and heteronuclear NMR (hNOE) measurements.

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

David P Giedroc, Email: giedroc@indiana.edu.

Lewis E Kay, University of Toronto, Canada.

John Kuriyan, University of California, Berkeley, United States.

Funding Information

This paper was supported by the following grants:

NIH Office of the Director GM118157 to David P Giedroc.
Pew Charitable Trusts Latin American Fellowship to Daiana A Capdevila.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Formal analysis, Supervision, Investigation, Writing—original draft.

Formal analysis, Investigation.

Data curation, Formal analysis, Investigation, Writing—review and editing.

Formal analysis, Investigation.

Resources.

Conceptualization, Funding acquisition, Writing—original draft.

Additional files

Supplementary file 1. Supplementary table for wild-type AdcR and selected AdcR mutants zinc binding affinities and melting temperatures from differential scanning fluorimetry.

elife-37268-supp1.docx^{(38.2KB, docx)}

DOI: 10.7554/eLife.37268.034

Source code 1. Python script for determining S²_axis values.

Representative python script used to analyze sets of peaklists from Sparky (Lee et al., 2015) to compute ratios of peak heights and to fit the peak height ratios to η values in order to determine S²_axis values for sidechain methyls of AdcR. Spectral noise must be input for propagation to errors in the peak height ratios, from which Monte Carlo simulation is used to obtain an error estimate for S²_axis.

elife-37268-code1.txt^{(11.9KB, txt)}

DOI: 10.7554/eLife.37268.035

Source code 2. Python script for determining relaxation parameters for two state model.

Representative python script used to read in a table of peak heights as output from the sparky ‘rh’ or ‘Relaxation Peak Heights’ command, where the number of cycles has been entered as the time parameter. This script calculates R2_eff for each peak at each number of cycles, then uses these values to fit R2 and R_ex, given a fixed τ_e, specified in s. Output consists of Excel spreadsheets giving fitted parameters and errors in fitting, as well as PDF charts of each fit and R_ex values for all probes.

elife-37268-code2.txt^{(8.2KB, txt)}

DOI: 10.7554/eLife.37268.036

Source code 3. Python script for finding τ for obtaining relaxation parameters.

Representative python script used to read in a table of peak heights as output from the sparky ‘rh’ or ‘Relaxation Peak Heights’ command, where the number of cycles has been entered as the time parameter. This script calculates R2_eff for each peak at each number of cycles, then uses these values to fit τ_e, R2 and R_ex for each probe. Output consists of Excel spreadsheets giving fitted parameters and errors in fitting, as well as PDF charts of each fit and R_ex values for all probes.

elife-37268-code3.txt^{(8.5KB, txt)}

DOI: 10.7554/eLife.37268.037

Source code 4. Dynafit script DNA binding isotherms.

Representative Dynafit script file used for nonlinear least squares fitting of the fluorescence anisotropy-derived DNA binding isotherms to a dimer linkage model, in which AdcR monomers-dimer equilibrium is displaced to dimers (P2) (defined by K_dimer >> protein concentration), which are competent to bind a single AdcO with an affinity Ka. Units of [concentrations] and [constants] in this script are nM and nM⁻¹, respectively.

elife-37268-code4.txt^{(1,013B, txt)}

DOI: 10.7554/eLife.37268.038

Source code 5. Dynafit script Zn^II-binding isotherms.

Representative Dynafit script file used for nonlinear least squares fitting of the Zn^II-binding isotherms obtained from a titration of apo (metal-free) wild-type AdcR or a mutant AdcR and mag-fura-2 (mf2) with ZnSO₄. Units of [concentrations] and [constants] in this script are µM and µM-1, respectively.

elife-37268-code5.txt^{(1.1KB, txt)}

DOI: 10.7554/eLife.37268.039

Transparent reporting form

elife-37268-transrepform.pdf^{(283KB, pdf)}

DOI: 10.7554/eLife.37268.040

Data availability

NMR datasets were deposited in Biological Magnetic Resonance Bank. Representative data generated or analyzed during this study are included in the supporting files.

The following datasets were generated:

Capdevila D, Edmonds K, Wu H, Giedroc D. 2018. Backbone and side-chain methyl relaxation rates, methyl order parameters, and stereospecific resonance assignments for Zn(II) AdcR. Biological Magnetic Resonance Data Bank. 27447

Capdevila D, Edmonds K, Wu H, Giedroc D. 2018. Backbone and side-chain methyl relaxation rates, methyl order parameters, and stereospecific resonance assignments, for apo AdcR. Biological Magnetic Resonance Data Bank. 27448

Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for Zn(II) L57M AdcR. Biological Magnetic Resonance Data Bank. 27655

Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for apo L57M AdcR. Biological Magnetic Resonance Data Bank. 27656

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eLife. doi: 10.7554/eLife.37268.050

Decision letter

Editor: Lewis E Kay¹

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Tuning site-specific dynamics to drive allosteric activation in a pneumococcal zinc uptake regulator" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Lewis E Kay as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by John Kuriyan as the Senior Editor. The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

All three reviewers find the study to be of interest and the role of dynamics and allostery in controlling function in this important system to be important. Yet all three reviewers raise serious concerns, as detailed below, that must be properly addressed in a revised manuscript.

The reviewers raise a number of important points, of which two In particular will require additional experimental work. We view these new data as essential for addressing our collective concerns with the validation of the structure/dynamics/function linkage discussed here. Please let us know if you anticipate problems in responding appropriately to these comments. As you will see, there are several other major points raised by the reviewers, but these are in the nature of requests for clarification or amendment of the manuscript.

Review:

This manuscript by Capdevila et al. details a variety of biophysical studies to examine the allosteric linkage between Zn(II) and DNA binding in the bacterial AdcR transcriptional regulator. This dimeric protein is known to undergo a substantial increase in DNA binding affinity in response to metal binding, but the process by which it does so has remained poorly characterized by crystal structures which have shown only minor conformational changes. In contrast, solution studies, ranging from SAXS to NMR chemical shift perturbation, showed marked metal-induced changes. Backbone and methyl relaxation measurements demonstrated that these structural changes are accompanied by substantial changes in dynamics at several timescales. Of particular note, the authors examined the importance of several methyl-containing residues in the allosteric transmission process, showing those with the greatest changes in S2 (methyl) values upon activation were important in the signal transmission process as assessed by DNA binding measurements. Yet it is felt that some of the claims are based on over-interpretation of the data and that the authors could do a significantly better job in explaining results.

Major points (requiring additional experimental work):

1) To show that the allosteric path is comprised of residues with dynamics the authors make a number of mutations of the dynamic residues (based on sidechain measurements) and show that these affect binding. Yet, while we agree with the importance of dynamics to allostery, we wonder whether the authors are really showing it. What if they take residues in the same general area with DS2=0, would they see a binding affect? They look at 1 control residue far away, but it may be that what is important is changes to residues in a given area, rather than the actual DS2 for that residue. In any event, we do think that the authors should prove it by considering residues with DS2~0 but in the area of interest. We suggest looking at 2-3 residues with DS2~0 and showing that mutation of these has little effect.

2) Do the introduced mutations change the dynamic response to Zn binding that then influences binding to DNA? One might assume so. That is for a sidechain mutant that has a big effect, does one also see a large change in the response to Zn binding at the level of changes in motion of the backbone (say less uncoupling of the DNA and dimerization domains) that would then decrease binding efficacy. We suggest looking at the sidechain mutant with the biggest effect and investigating what happens at the level of the backbone motions that so clearly change in a defined way up binding of Zn.

Other critical points to respond to:

1) The relaxation data are analyzed in a minimalist way and often it is not easy to see what the authors are referring to in their small figures. We suggest major changes to these sections. For example, the authors state that in the Zn2 form the protein tumbles as a complete unit. This is fine, based on the figure. However, they state that the derived correlation time is consistent with expectations from calculations. Please use hydro-NMR or some such program to see what is expected from calculations. It is not clear how the calculations were performed. The authors then state that the DNA binding and dimerization domains have significantly smaller tauc values – 10.5 ns. We find it surprising that both are the same, especially considering the data of Figure 4A were there are very distinct differences in R2/R1 values in certain regions (not all white/not all red). We might have thought that the DNA binding domain would have a smaller tauc than the dimerization domains, given the size difference, if the two were truly uncoupled. Also how clear is it that the apo form is a dimer? The conclusions about how Zn binding decrease fast time-scale motion (NOEs) is difficult to see from the plot. We strongly suggest adding for each a panel that shows R2/R1 or NOE or Rex vs residue with secondary structural elements and loops delineated so that one can see better what appears clear to the authors. We think with the NOE data the authors are trying to say something about taue values, but this is unclear from their description. A more precise and careful discussion is called for here. The authors speak about an increase in sub ns motions in helix 4 upon Zn binding based on NOE values. Yet from the NOE it looks like there is little change to helix 4. The authors then state that there is a concomitant increase in Rex values. How were these obtained (hopefully by CPMG) and again it is very hard to see this from the figures. Panels must be included here as well. Can the CPMG data be fit to a single process?

2) In the last paragraph of the subsection “Zn^II-induced changes in AdcR conformational plasticity along the backbone” the authors state that the increased internal flexibility of the DNA binding domain (please show J(w) values from a reduced spectral density mapping to better establish the point) may be important for higher DNA binding. Yet why should this be the case? One could easily imagine quite the opposite. That the increased dynamics would be quenched upon DNA binding leading to an entropic penalty. Would it not be better to speak about S2 values for the backbone?

3) The authors state that "unlike backbone.. changes in sidechain S2 values.." We don't agree with this statement. Indeed, it seems like the most compelling argument about dynamics comes from the backbone results that show a very significant change in the overall motion of the DNA binding area from the dimerization domain upon metalation that appears crucial for binding (see Figure 7 that further makes this point).

4) The points made in the text for the sidechain data would be similarly improved by having panels showing S2 and DS2 values vs. residue.

5) Following up on points above. Why do all the mutations decrease binding? Should not some increase it? It is always easy to destroy something via mutation, but shouldn't the model predict that some mutations would improve the allostery or binding. Would one expect that DS2>0 or <0 would have different affects to binding; the data does not suggest so, but we wonder why not?

6) In the Discussion the authors state that upon Zn binding there is a striking redistribution in dynamics onto the DNA binding domain. We don't find it striking really. Perhaps a panel (see above) would help as would more discussion.

7) Figure 7 needs far more discussion. Presumably the DS and DH refer to DNA binding. The introduction of dynamical elements inactivates as there would be a steep entropy price to pay upon binding likely. But then if an organic ligand disrupts binding (bottom, yellow star) why would this affect DS so much. After all there is little dynamics now and so there should not be a penalty upon binding. If the binding of organics affects the ability of the DNA domain to reorient to a proper orientation upon DNA binding one might imagine a predominantly enthalpy affect.

8) The authors analyze over 130 crystal structures of MarR proteins in different states (DNA-binding inactive, DNA-binding active and DNA-bound). This analysis is quite interesting, and provides part of the rationale for investigating the dynamics of a representative member of this family. Table S1 provides a small sample of structures and interprotomer distances for the three states. However, the criteria used to assign each structure to a particular state, particularly the active and inactive states, were not made clear in the manuscript (or in Table S1). Furthermore, it would be useful to provide additional information in the text about how many structures were assigned to each state. The complete dataset could be summarized in a table or in an Excel spreadsheet, including the state that each structure represents, and added to the supplemental data. Table S1 could then be added the main text (as Table 1), as this is a small sample of the entire dataset.

9) Figure 1 summarizes the analysis of the ~130 structures. Overall, it is a very useful figure. However, a few changes might improve it. Panel A should include larger type for the secondary structure and it should be slightly offset from the structure so that the labels are easier to read. Similarly, the zinc ions are small and very difficult to see. Coloring the zinc darker would increase contrast. An energy diagram (Panel B) would be useful, but it is confusing because the "active" and "inactive" states are not made clear. This is similar to the comment made above. Apparently, panel B depicts two hypothetical conformational "states", where the active state (with inducer?) is of higher energy than the inactive state (apo?). Is the y-axis correct? Should it be deltaG or G? If deltaG is correct, what are the two states? Also, why is the DNA-bound state at a higher energy (red shaded region?) then the DNA-free state?

10) Apparently, the conclusion from Figure 1 and Table 1 is that the DNA-bound state has a narrow distribution of conformations and both the allosterically inactivated (DNA-binding inactive by ligand or redox) and active states have broad conformational ensembles. What makes this confusing is that the active and inactive states are not defined. Taken at face value, these data still show that structures determined in the absence of DNA (apparently the active and inactive structures) have a broad ensemble of conformations that in some cases overlap with the DNA-bound conformations. Thus, a similar conclusion might be reached without having to define the active and inactive states, which the authors already indicate is a difficult task (Introduction, third paragraph). In the then end, it is not clear why it is necessary to parse the DNA-free states into active and inactive states, especially with the potential uncertainties of doing this. Despite this limitation, the analysis of the structures still suggests that a conformational ensemble model of allostery might be operative.

11) Figure 4 shows the raw backbone relaxation parameters (R2/R1, hNOE and Rex), while Figure 5—figure supplement 3 shows sample dispersion data for a few residues (NH and methyl groups). The first observation is that the Rex values are relatively small and their corresponding errors large (especially for the NH data). From this figure it is not apparent how significant the NH Rex values are in comparison to the reported error in the fit. Moreover, the quality of data fits (e.g. chi2) are not shown nor discussed in the text (for both NH and methyl groups).

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Tuning site-specific dynamics to drive allosteric activation in a pneumococcal zinc uptake regulator" for further consideration at eLife. Your revised article has been favorably evaluated by John Kuriyan (Senior Editor), a Reviewing Editor, and two reviewers.

The manuscript has been improved but there are some remaining issues that need to be addressed before acceptance, as outlined below.

All three agree that the manuscript is substantially better. There are a number of highlights of the work, including:

1) Analyses of >130 MarR family structures that suggests that their DNA-competent and incompetent states exist as an ensemble.

2) SAXS data, NMR chemical shift data, and NOESY data indicating structural differences between apo and zinc-bound AdcR.

3) Dynamic changes along the backbone and side chain between apo and zinc-bound AdcR (although the backbone changes are subtle, the side chain results are more robust).

4) A correlation between dynamics (DS2axis or DRex) and DGc of zinc-dependent DNA binding.

In particular the identification of dynamic hotspots that are perturbed upon metal binding and the impact that this has on DNA binding is important and of great interest.

In principle the paper is acceptable for publication, but we strongly feel that additional textual changes be made to avoid over-interpretation of the data. In a revised version, considered only at the editorial level, the authors are asked to focus only on 1-4 above and the identification of hotspots that their data nicely show, without additional claims.

Major comments:

1) The main weakness or issue with this paper is that there is no direct evidence for the conclusion that perturbing "dynamically active" residues redistributes the dynamics to impact allostery. The authors make this claim in the revised manuscript (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”). Although this claim may very well be true, the backbone data for the L57M variant does not support this hypothesis. Indeed, the authors suggest that the redistribution is likely at the level of side chain dynamics. The alternative possibility (not really described in the Discussion) is that subtle changes in protein conformation (enthalpy) may also be important. In my mind, analyzing the side chain dynamics would be essential for the authors to solidify their claim about the link between dynamical active residues and allostery. Otherwise, the authors should tone down the language and focus on the redistribution of dynamics that their analyses of the WT apo and zinc-bound data already provide.

2) In several places the authors speak about the fact that analysis of crystal structures suggests a conformational selection mechanism for binding. The authors should be careful with this, however, because they do not have kinetic data to support this assumption. Unless the authors can show that a particular binding pathway involving selecting the bound conformation from an ensemble of different conformers is the dominant flux pathway they cannot make this conclusion.

eLife. 2018 Oct 17;7:e37268. doi: 10.7554/eLife.37268.051

Author response

Major points (requiring additional experimental work):

1) To show that the allosteric path is comprised of residues with dynamics the authors make a number of mutations of the dynamic residues (based on sidechain measurements) and show that these affect binding. Yet, while we agree with the importance of dynamics to allostery, we wonder whether the authors are really showing it. What if they take residues in the same general area with DS2=0, would they see a binding affect? They look at 1 control residue far away, but it may be that what is important is changes to residues in a given area, rather than the actual DS2 for that residue. In any event, we do think that the authors should prove it by considering residues with DS2~0 but in the area of interest. We suggest looking at 2-3 residues with DS2~0 and showing that mutation of these has little effect.

In the previous version of the manuscript, we presented the results of two mutants of residues with ∆S²_axis ≈0. We have now characterized an additional three (3) methyl group substitution mutants that are dispersed throughout the structure (Figure 6A and Table S1). In this revised version of the manuscript, we also consider changes in the thermal stability of the mutants as a potential factor that can lead to misinterpretation of the results (Supplementary file 1, Figure 6—figure supplement 4). Indeed, substitution of a number of conserved residues leads to a decrease in thermal stability; we have therefore eliminated from discussion all mutants that are significantly destabilized on the basis of this simple scanning fluorimetry assay.

We also evaluated the proximity of the different control mutant substitutions in the crystal structure. The C_δδ1-I104 methyl group is less than 5 Å away from the methyl groups of two dynamically active residues we tested previously, in the DNA binding domain (V34 and L36). We expressed and purified V63A AdcR as a possible control substitution in the _αα3 helix for the dynamically active L57 in the L57M AdcR mutant. However, the thermal stability of this mutant, as well as the L61V AdcR (also in the _αα3 helix) were found to be significantly lower at physiological pH and thus could not be investigated further. On the other hand, we provide new data on the L17A and I27A substitution mutants that serve as a control residues for the two dynamically active mutants in the dimerization domain (less than 10 Å away from the I16A and V142A substitutions). These have characteristics consistent with expectations from a dynamics- driven allosteric model, i.e.,essentially no impact on allostery.

2) Do the introduced mutations change the dynamic response to Zn binding that then influences binding to DNA? One might assume so. That is for a sidechain mutant that has a big effect, does one also see a large change in the response to Zn binding at the level of changes in motion of the backbone (say less uncoupling of the DNA and dimerization domains) that would then decrease binding efficacy. We suggest looking at the sidechain mutant with the biggest effect and investigating what happens at the level of the backbone motions that so clearly change in a defined way up binding of Zn.

We prepared the V34A and L57M AdcRs for detailed NMR backbone dynamics characterization. ¹⁵N-TROSY spectra showed that V34A AdcR was not sufficiently stable at 35

°C to obtain backbone assignments in both allosteric states, a finding confirmed by the scanning fluorimetry experiments (Supplementary file 1, Figure 6—figure supplement 4). Thus, we continued with the characterization of L57M AdcR (Figure 6—figure supplements 6 to 11). Much of the structural and dynamic data along the backbone is wild-type-like. This is as expected from the rationale behind this substitution: the L57M AdcR substitution is expected to possess nonnative sidechain dynamics (beyond the scope of this manuscript), accompanied by minor or no perturbations in the structure or dynamics of the backbone, which were measured, as suggested by the reviewer. While we do observe small, local differences in the immediate vicinity of M57 that could contribute to the allosteric impact of the L57M substitutions (Figure 6—figure supplements 7 to 11), these changes are quite minor. We recognize however that side chain dynamics measurements cannot, in and of themselves, rule out any contribution to backbone to allosteric function, and we discuss the L57M mutant in this way (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”).

Other critical points to respond to:

1) The relaxation data are analyzed in a minimalist way and often it is not easy to see what the authors are referring to in their small figures. We suggest major changes to these sections. For example, the authors state that in the Zn2 form the protein tumbles as a complete unit. This is fine, based on the figure. However, they state that the derived correlation time is consistent with expectations from calculations. Please use hydro-NMR or some such program to see what is expected from calculations.

The residue-specific R₁ and R₂ data obtained for apo- and Zn- AdcRs are now presented in the figure supplements, as requested (Figure 4—figure supplement 1) and compared to the hydroNMR predictions based on the crystal structure of Zn₂-AdcR at the same temperature and viscosity. This analysis allows us to confirm that the Zn-bound form is tumbling as a single hydrodynamic unit, whereas apo-AdcR has a smaller tumbling time, a finding compatible with more independent domain tumbling in the apo-state. These data are presented along with the hNOE (Figure 4—figure supplement 1A, bottom panel).

We also performed a more comprehensive analysis of these data as suggested by the reviewer, using tensor2. The resulting parameters and the errors of the fitting with the different models are now presented in Figure 4—figure supplement 2 for wild-type AdcR and in Figure 6—figure supplement 10 for L57M AdcR.

It is not clear how the calculations were performed. The authors then state that the DNA binding and dimerization domains have significantly smaller tauc values – 10.5 ns. We find it surprising that both are the same, especially considering the data of Figure 4A were there are very distinct differences in R2/R1 values in certain regions (not all white/not all red). We might have thought that the DNA binding domain would have a smaller tauc than the dimerization domains, given the size difference, if the two were truly uncoupled.

The way in which these calculations were performed are now clarified in the Materials and methods section (subsection “NMR spectroscopy”). We apologize for the confusion we originally raised on the ^{ττ _Χ} values of the DNA binding and dimerization domains in the apo-state. We were not suggesting that the tumbling time of these domains is the same nor did we mean to imply that these two domains tumble entirely independently of one another. This has been clarified on the current version on the manuscript (subsection “Zn^II-induced changes in AdcR conformational plasticity along the backbone”). The prediction from hydroNMR on these domains is well below the T₁/T₂ ratio obtained for Zn-bound and apo- AdcR; however, the observed average T₁/T₂ ratios from the residues in the DNA binding and dimerization domains in apo-AdcR are in fact intermediate between the independent domain values and the homodimer. These observations suggest that in apo-AdcR, these domains are not fully uncoupled; however, they are not tumbling as a single unit as in the Zn-bound state.

Also how clear is it that the apo form is a dimer?

The oligomerization state of the protein has been independently assessed by gel filtration as shown in Figure 6—figure supplement 2 (done at a lower concentration) and is compatible with the radius of gyration measured from the SAXS analysis (Figure 2, Figure 2—figure supplement 1) Also, a lack of chemical shift differences in residues positioned at the dimer interface reveals that Zn binding does not affect significantly the dimerization equilibrium, at least under these conditions.

The conclusions about how Zn binding decrease fast time-scale motion (NOEs) is difficult to see from the plot. We strongly suggest adding for each a panel that shows R2/R1 or NOE or Rex vs residue with secondary structural elements and loops delineated so that one can see better what appears clear to the authors. We think with the NOE data the authors are trying to say something about taue values, but this is unclear from their description. A more precise and careful discussion is called for here. The authors speak about an increase in sub ns motions in helix 4 upon Zn binding based on NOE values. Yet from the NOE it looks like there is little change to helix 4.

Figure panels that provide all R₂/R₁ (T₁/T₂) and hNOE data have been added in the supplement (Figure 4—figure supplement 1). The description has been clarified and more comprehensive discussion of the R₁, R₂ and hNOE experiments is now presented in the subsection “Zn^II-induced changes in AdcR conformational plasticity along the backbone”. The ^ττ _εε values are also now presented in Figure 4—figure supplement 2A-B). Changes on the sub- ns motions in the DNA binding domain are now presented more clearly in the circular-shaped insets to the structure figures in Figure 4C, D.

The authors then state that there is a concomitant increase in Rex values. How were these obtained (hopefully by CPMG) and again it is very hard to see this from the figures. Panels must be included here as well. Can the CPMG data be fit to a single process?

Panels with the parameters obtained from the fitting of the CPMG data are now included in Figure 4—figure supplement 3 and Figure 5—figure supplement 3, for the backbone and side chains respectively. There is no improvement in the fitting if independent parameters are used; we therefore conclude that a single process is sufficient to describe the current data. More details about fitting of the CPMG data is now provided in the Materials and methods section (subsection “NMR spectroscopy”, last two paragraphs).

2) In the last paragraph of the subsection “Zn^II-induced changes in AdcR conformational plasticity along the backbone” the authors state that the increased internal flexibility of the DNA binding domain (please show J(w) values from a reduced spectral density mapping to better establish the point) may be important for higher DNA binding. Yet why should this be the case? One could easily imagine quite the opposite. That the increased dynamics would be quenched upon DNA binding leading to an entropic penalty. Would it not be better to speak about S2 values for the backbone?

Order parameters on the backbone as well as information about the model used to obtain those order parameters are now presented as figure supplements (Figure 4—figure supplement 2A, B). As the reviewers correctly points out, our interpretation of these data is one of many and in fact, could lead to an entropic penalty upon DNA binding. However, these dynamic changes are not unprecedented and as we have shown for other proteins (Capdevila, 2017; Capdevila, 2018), a particular distribution of site-specific motion can be required to access entropy reservoirs that can facilitate DNA binding.

3) The authors state that "unlike backbone.. changes in sidechain S2 values.." We don't agree with this statement. Indeed, it seems like the most compelling argument about dynamics comes from the backbone results that show a very significant change in the overall motion of the DNA binding area from the dimerization domain upon metalation that appears crucial for binding (see Figure 7 that further makes this point).

We thank the reviewers for encouraging us to perform a more comprehensive estimation of the contribution that backbone dynamics makes to function and particularly to conformational entropy in this system. We have now done that, and have better clarified the contribution of backbone (–T∆S_conf,_bb= 3.5 ± 0.5 kcal mol^-1) and sidechain (–T∆S_conf,_sc = 1.1 ±

0.2 kcal mol^-1; note that this value has changed slightly from the previous report as a result of a more robust tumbling time obtained from tensor2) dynamics to the entropy of Zn binding. It is interesting to point out that the contribution of sidechains dynamics has been shown to play a much larger role on model systems (Caro, 2017); however, for AdcR, it seems reasonable to suggest that loop dynamics make a more significant contribution to the overall entropy. Also, it should be noted that the estimation of the backbone contribution is based mainly on parameters obtained from molecular dynamics simulations (Sharp, 2015) while the sidechain contribution is supported on a much more extensive experimental analysis (Caro, 2017).

In the current version of the manuscript we emphasize the advantage of measuring the sidechain dynamics to test the predictions of a dynamic model of allostery (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”, first paragraph). This is because methyl substitutions have a direct impact on sub-ns sidechain dynamics, since they modify the number of χχ angles which directly determines the contribution to site-specific conformational entropy.

4) The points made in the text for the sidechain data would be similarly improved by having panels showing S2 and DS2 values vs. residue.

We have now incorporated ∆S²_axis values vs. residue as a main text figure (Figure 5A) with the raw S²_axis values vs. residue number for each allosteric state incorporated as a figure supplement to that figure (Figure 5—figure supplement 1).

5) Following up on points above. Why do all the mutations decrease binding? Should not some increase it? It is always easy to destroy something via mutation, but shouldn't the model predict that some mutations would improve the allostery or binding. Would one expect that DS2>0 or <0 would have different affects to binding; the data does not suggest so, but we wonder why not?

The model predicts that a particular (re)distribution of internal motions is necessary for the allosteric regulation. In this case, the allosteric connection means allosteric activation of DNA binding. Our results suggest that the single point mutations introduced in this work perturb the native distribution of internal motions and thus impact to different degrees the allosteric connectivity. We have recently shown that is also the case in other systems (Capdevila, 2017; Glauninger, Chem. Sci., 2017; Capdevila, 2018) and it is the expected result for allosteric hotspots.

On the other hand, it is interesting to speculate what sequence perturbations could potentially enhance the allosteric connection between Zn^II and DNA binding. As the reviewer points out, this will require a more challenging experimental approach. We expect that a more complete exploration of the sequence space is necessary to find those sequence perturbations; we are, in fact, currently developing a directed evolution approach to explore those possibilities.

6) In the Discussion the authors state that upon Zn binding there is a striking redistribution in dynamics onto the DNA binding domain. We don't find it striking really. Perhaps a panel (see above) would help as would more discussion.

We apologize for this overstatement; we have removed the word “striking” from this sentence. Now, we explicitly point out what we find striking about this redistribution, particularly the number of residues that change motional regime and are not in close proximity to the Zn binding site.

7) Figure 7 needs far more discussion. Presumably the DS and DH refer to DNA binding. The introduction of dynamical elements inactivates as there would be a steep entropy price to pay upon binding likely. But then if an organic ligand disrupts binding (bottom, yellow star) why would this affect DS so much. After all there is little dynamics now and so there should not be a penalty upon binding. If the binding of organics affects the ability of the DNA domain to reorient to a proper orientation upon DNA binding one might imagine a predominantly enthalpy affect.

Figure 7 has been significantly modified to emphasize the role of conformational entropy while providing a clearer explanation of the concept of entropic reservoirs in the context of allosteric inhibition or activation. We have also thoroughly modified (and significantly shortened!) the text that discusses this figure (subsection “Conclusions”). We do not believe that the orientation of the DNA binding domain necessarily plays a role as large as was inferred by static crystal structures. However, that possibility is now taken into account in the revised Figure 7. We thank the reviewer for these suggestions to improve our presentation of these ideas.

8) The authors analyze over 130 crystal structures of MarR proteins in different states (DNA-binding inactive, DNA-binding active and DNA-bound). This analysis is quite interesting, and provides part of the rationale for investigating the dynamics of a representative member of this family. Table S1 provides a small sample of structures and interprotomer distances for the three states. However, the criteria used to assign each structure to a particular state, particularly the active and inactive states, were not made clear in the manuscript (or in Table S1). Furthermore, it would be useful to provide additional information in the text about how many structures were assigned to each state. The complete dataset could be summarized in a table or in an Excel spreadsheet, including the state that each structure represents, and added to the supplemental data. Table S1 could then be added the main text (as Table 1), as this is a small sample of the entire dataset.

The data in Table S1 is now integrated into Table 1. The criteria have been clarified both in the figure caption and in the footnote of Table 1. In addition, we provide an excel file with all the information available from each crystal structure has been added as additional information.

9) Figure 1 summarizes the analysis of the ~130 structures. Overall, it is a very useful figure. However, a few changes might improve it. Panel A should include larger type for the secondary structure and it should be slightly offset from the structure so that the labels are easier to read. Similarly, the zinc ions are small and very difficult to see. Coloring the zinc darker would increase contrast. An energy diagram (Panel B) would be useful, but it is confusing because the "active" and "inactive" states are not made clear. This is similar to the comment made above. Apparently, panel B depicts two hypothetical conformational "states", where the active state (with inducer?) is of higher energy than the inactive state (apo?). Is the y-axis correct? Should it be deltaG or G? If deltaG is correct, what are the two states? Also, why is the DNA-bound state at a higher energy (red shaded region?) then the DNA-free state?

The suggested changes to panel A of this figure have been made. The terms “active” and “inactive” have been removed from the text, and changed to “DNA binding competent” and “DNA binding incompetent”, respectively. Since the purpose is to summarize a schematic model that could possibly explain Figure 1C, we prefer not to associate at this point “DNA binding competent” and “DNA binding incompetent” with apo and ligand bound, respectively, because this would only be true in ligand-mediated allosterically inactivated MarRs. It is ∆G because the unfolded state is the reference state as it is usually done in this kind of energy diagram. The DNA-bound state is not represented; what is shaded is the DNA-binding conformation.

10) Apparently, the conclusion from Figure 1 and Table S1 is that the DNA-bound state has a narrow distribution of conformations and both the allosterically inactivated (DNA-binding inactive by ligand or redox) and active states have broad conformational ensembles. What makes this confusing is that the active and inactive states are not defined. Taken at face value, these data still show that structures determined in the absence of DNA (apparently the active and inactive structures) have a broad ensemble of conformations that in some cases overlap with the DNA-bound conformations. Thus, a similar conclusion might be reached without having to define the active and inactive states, which the authors already indicate is a difficult task (Introduction, third paragraph). In the then end, it is not clear why it is necessary to parse the DNA-free states into active and inactive states, especially with the potential uncertainties of doing this. Despite this limitation, the analysis of the structures still suggests that a conformational ensemble model of allostery might be operative.

We apologize for the confusion. The “active” and “inactive” states are now redefined here as “DNA binding-competent” and “DNA binding-incompetent” states and these categories have been clarified in Table 1, and are explicitly stated in the excel file with the appropriate references. Briefly we have classified as follows:

- “DNA binding-competent” is any protein allosteric state that has been shown to bind to DNA in-vitrowith an affinity higher than 10⁷ M^-1 or is capable of repressing the expression of downstream genes;

- “DNA binding-incompetent” is any protein allosteric state that fails to repress these genes and/or exhibits a significantly lower DNA binding affinity from the DNA binding-competent conformation (at least 10-fold) or an affinity lower than 10⁶ M^-1;

- Putative DNA binding-competent and incompetent states refer to any protein allosteric state for which the DNA binding properties have not been determined, but the conformational state in the crystal structure is known (i.e., reduced, ligand bound). For these systems is it possible to suggest a DNA binding property (competent or incompetent), particularly when one takes into account the degree of sequence similarity to other MarR repressors.

11) Figure 4 shows the raw backbone relaxation parameters (R2/R1, hNOE and Rex), while Figure 5—figure supplement 3 shows sample dispersion data for a few residues (NH and methyl groups). The first observation is that the Rex values are relatively small and their corresponding errors large (especially for the NH data). From this figure it is not apparent how significant the NH Rex values are in comparison to the reported error in the fit. Moreover, the quality of data fits (e.g. chi2) are not shown nor discussed in the text (for both NH and methyl groups).

The quality of the data fits and the values for all the residues are now discussed in the Materials and methods section. The full Carver-Richards equation was not applied to these data, since we observed no significant improvement in these fits; in fact, the field strength- dependence suggests that all the significant values were not in the slow or intermediate regime.

[Editors' note: further revisions were requested prior to acceptance, as described below.]

Major comments:

1) The main weakness or issue with this paper is that there is no direct evidence for the conclusion that perturbing "dynamically active" residues redistributes the dynamics to impact allostery. The authors make this claim in the revised manuscript (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”). Although this claim may very well be true, the backbone data for the L57M variant does not support this hypothesis. Indeed, the authors suggest that the redistribution is likely at the level of side chain dynamics. The alternative possibility (not really described in the Discussion) is that subtle changes in protein conformation (enthalpy) may also be important. In my mind, analyzing the side chain dynamics would be essential for the authors to solidify their claim about the link between dynamical active residues and allostery. Otherwise, the authors should tone down the language and focus on the redistribution of dynamics that their analyses of the WT apo and zinc-bound data already provide.

As requested, we have toned down the language regarding the conclusion that perturbation of "dynamically active" residues redistributes the dynamics to impact allostery (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”). We agree with the reviewer that we have no direct evidence for this conclusion, which is the subject of ongoing experiments beyond the scope of this paper. We instead use the analysis of the mutants as additional data in support of the conclusions derived from the WT dynamical measurements.

Additionally, we included several sentences in the Results and Discussion section that take into account the uncertainty of the impact of minor structural changes in the functional outcome (subsection “On-pathway and off-pathway allosterically impaired mutants of AdcR”, third paragraph).

2) In several places the authors speak about the fact that analysis of crystal structures suggests a conformational selection mechanism for binding. The authors should be careful with this, however, because they do not have kinetic data to support this assumption. Unless the authors can show that a particular binding pathway involving selecting the bound conformation from an ensemble of different conformers is the dominant flux pathway they cannot make this conclusion.

We agree with the reviewer on this point. The use of the term “conformational selection” in the manuscript has been removed. We now refer exclusively to a conformational ensemble model of allostery (subsection “Conclusions”, first paragraph).

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

Capdevila D, Edmonds K, Wu H, Giedroc D. 2018. Backbone and side-chain methyl relaxation rates, methyl order parameters, and stereospecific resonance assignments for Zn(II) AdcR. Biological Magnetic Resonance Data Bank. 27447
Capdevila D, Edmonds K, Wu H, Giedroc D. 2018. Backbone and side-chain methyl relaxation rates, methyl order parameters, and stereospecific resonance assignments, for apo AdcR. Biological Magnetic Resonance Data Bank. 27448
Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for Zn(II) L57M AdcR. Biological Magnetic Resonance Data Bank. 27655
Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for apo L57M AdcR. Biological Magnetic Resonance Data Bank. 27656

Supplementary Materials

Figure 1—source data 1. Table of details on MarR proteins structures: PDB ID, reference for the structure, protein state and ligand bound (if any), DNA binding competence classification, organism, Methods used for determining the DNA binding properties, DNA binding constant (K_a), reference for the DNA binding constant, residues in the α1-α2 loop obtained from pymol secondary sequence assignment, minimal distance between the α4-α4’ helices, and angle between the α4-α4’ helices.

elife-37268-fig1-data1.xlsx^{(26.6KB, xlsx)}

DOI: 10.7554/eLife.37268.004

Supplementary file 1. Supplementary table for wild-type AdcR and selected AdcR mutants zinc binding affinities and melting temperatures from differential scanning fluorimetry.

elife-37268-supp1.docx^{(38.2KB, docx)}

DOI: 10.7554/eLife.37268.034

Source code 1. Python script for determining S²_axis values.

elife-37268-code1.txt^{(11.9KB, txt)}

DOI: 10.7554/eLife.37268.035

Source code 2. Python script for determining relaxation parameters for two state model.

elife-37268-code2.txt^{(8.2KB, txt)}

DOI: 10.7554/eLife.37268.036

Source code 3. Python script for finding τ for obtaining relaxation parameters.

Representative python script used to read in a table of peak heights as output from the sparky ‘rh’ or ‘Relaxation Peak Heights’ command, where the number of cycles has been entered as the time parameter. This script calculates R2_eff for each peak at each number of cycles, then uses these values to fit τ_e, R2 and R_ex for each probe. Output consists of Excel spreadsheets giving fitted parameters and errors in fitting, as well as PDF charts of each fit and R_ex values for all probes.

elife-37268-code3.txt^{(8.5KB, txt)}

DOI: 10.7554/eLife.37268.037

Source code 4. Dynafit script DNA binding isotherms.

elife-37268-code4.txt^{(1,013B, txt)}

DOI: 10.7554/eLife.37268.038

Source code 5. Dynafit script Zn^II-binding isotherms.

elife-37268-code5.txt^{(1.1KB, txt)}

DOI: 10.7554/eLife.37268.039

Transparent reporting form

elife-37268-transrepform.pdf^{(283KB, pdf)}

DOI: 10.7554/eLife.37268.040

Data Availability Statement

NMR datasets were deposited in Biological Magnetic Resonance Bank. Representative data generated or analyzed during this study are included in the supporting files.

The following datasets were generated:

Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for Zn(II) L57M AdcR. Biological Magnetic Resonance Data Bank. 27655

Daiana A Capdevila, Katherine A Edmonds, Hongwei Wu, David P Giedroc. 2018. Backbone assignments and relaxation rates for apo L57M AdcR. Biological Magnetic Resonance Data Bank. 27656

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PERMALINK

Tuning site-specific dynamics to drive allosteric activation in a pneumococcal zinc uptake regulator

Daiana A Capdevila

Fidel Huerta

Katherine A Edmonds

My Tra Le

Hongwei Wu

David P Giedroc

Roles

Abstract

Introduction

Figure 1. (A) Ribbon representation of dimeric Zn(II)-bound AdcR, with one protomer shaded white and the other shaded light blue (PDB code: 3tgn; Guerra et al., 2011).

Figure 1—figure supplement 1. Structural comparison ZitR and AdcRs with other MarR family repressors. .

Table 1. Interprotomer distances between the Cα of the N-terminal residue in the α4 and α4’ helices for representatives MarR proteins.

Results and discussion

Solution structural differences between apo and ZnII bound forms of AdcR

Figure 2. (A) Small angle X-ray scattering (SAXS) curve of AdcR in apo- and Zn2-states.

Figure 2—figure supplement 1. Small angle X-ray scattering (SAXS) analysis of AdcR in the apo and Zn-binding states.

Figure 3. Chemical shift perturbation (CSP) maps for ZnII binding to to AdcR.

Figure 3—figure supplement 1. Backbone 1H-15N TROSY spectra showing the assignments of (A) apo WT AdcR and (B) ZnII WT AdcR.

Figure 3—figure supplement 2. NMR analysis of the α5 helix in AdcR.

ZnII-induced changes in AdcR conformational plasticity along the backbone

Figure 4. Dynamical characterization of the apo- (A) (C) (E) and ZnII2 (B) (D) (F) AdcR conformational states.

Figure 4—figure supplement 1. Backbone 15N spin relaxation analysis of AdcR. .

Figure 4—figure supplement 2. Dynamic parameters obtained for (A) apo- and (B) ZnII2-WT AdcR using tensor 2.

Figure 4—figure supplement 3. Backbone (NH) relaxation dispersion analysis of AdcR.

ZnII-induced perturbations of side chain conformational disorder in AdcR

Figure 5. Effect of ZnII binding to AdcR on the site-specific stereospecifically assigned methyl group axial order parameter.

Figure 5—figure supplement 1. Methyl group axial order parameter analysis of AdcR.

Figure 5—figure supplement 2. Absolute values of methyl group order parameters, S2axis on the methyl-bearing residues. .

Figure 5—figure supplement 3. Methyl group relaxation dispersion analysis of AdcR.

On-pathway and off-pathway allosterically impaired mutants of AdcR

Figure 6. Graphical summary of the functional properties of AdcR methyl substitution and hydrogen bonding mutants.

Figure 6—figure supplement 1. Representative DNA operator binding isotherms obtained for wild-type (WT) AdcR and selected AdcR mutants in the apo- and ZnII2-states.

Figure 6—figure supplement 2. Gel filtration chromatograms for AdcR variants in the apo-state.

Figure 6—figure supplement 3. Representative ZnII-binding isotherms obtained from a titration of apo (metal-free) wild-type AdcR or a mutant AdcR and mag-fura-2 (mf2) with ZnSO4.

Figure 6—figure supplement 4. Differential scanning fluorimetry (DSF) of AdcR and mutants Representative DSF plots acquired with SYPRO orange.

Figure 6—figure supplement 5. Multiple sequence analysis of AdcRs and closely related MarR family repressors.

Figure 6—figure supplement 6. Backbone 1H-15N TROSY spectra showing the resonance assignments of (A) apo L57M AdcR and (B), ZnII L57M AdcR.

Figure 6—figure supplement 7. Backbone chemical shift perturbation (CSP) maps for L57M AdcR. Apo-L57M AdcR and for ZnII2 L57M AdcR at pH 5.5, 50 mM NaCl, 35°C relative to WT AdcR as indicated.

Figure 6—figure supplement 8. 15N NMR spin relaxation analysis of L57M vs. wild-type AdcR.

Figure 6—figure supplement 9. Heteronuclear NOE analysis of L57M vs. wild-type AdcR. Apo- and ZnII2 L57M AdcR values of the 15N-{1H}-NOE (hNOE) are painted onto the 3tgn structure, relative to the WT parameters reproduced here to facilitate comparison.

Figure 6—figure supplement 10. Backbone dynamics parameters obtained for L57M vs. wild-type AdcRs.

Figure 6—figure supplement 11. Backbone relaxation dispersion analysis of the L57M vs. wild-type AdcRs.

Figure 6—figure supplement 12. TROSY NMR spectra of selected AdcR mutants.

Table 2. DNA binding parameters for wild-type AdcR and substitution mutants*.

DNA-binding domain mutants

Hydrogen-bonding mutants

Dimerization domain mutants

Conclusions

Figure 7. (A) Histogram representation of the distribution of α1-α2 loops lengths in the reported structures in MarR family of proteins, with the bars are colored to account for the measured or proposed coupling free energies in each case.

Materials and methods

AdcR mutant plasmid production

Protein production and purification

Small angle x-ray scattering experiments

NMR spectroscopy

DNA binding experiments and determination of allosteric coupling free energies (∆Gc)

Mag-fura-2 competition assays

SYPRO orange Differential Scanning Fluorimetry assays

Acknowledgements

Funding Statement

Contributor Information

Funding Information

Additional information

Competing interests

Author contributions

Additional files

Data availability

References

Decision letter

Roles

Author response

Associated Data

Data Citations

Supplementary Materials

Data Availability Statement

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Solution structural differences between apo and Zn^II bound forms of AdcR

Figure 2. (A) Small angle X-ray scattering (SAXS) curve of AdcR in apo- and Zn₂-states.

Figure 3. Chemical shift perturbation (CSP) maps for Zn^II binding to to AdcR.

Figure 3—figure supplement 1. Backbone ¹H-¹⁵N TROSY spectra showing the assignments of (A) apo WT AdcR and (B) Zn^II WT AdcR.

Zn^II-induced changes in AdcR conformational plasticity along the backbone

Figure 4. Dynamical characterization of the apo- (A) (C) (E) and Zn^II₂ (B) (D) (F) AdcR conformational states.

Figure 4—figure supplement 1. Backbone ¹⁵N spin relaxation analysis of AdcR. .

Figure 4—figure supplement 2. Dynamic parameters obtained for (A) apo- and (B) Zn^II₂-WT AdcR using tensor 2.

Zn^II-induced perturbations of side chain conformational disorder in AdcR

Figure 5. Effect of Zn^II binding to AdcR on the site-specific stereospecifically assigned methyl group axial order parameter.

Figure 5—figure supplement 2. Absolute values of methyl group order parameters, S²_axis on the methyl-bearing residues. .

Figure 6—figure supplement 1. Representative DNA operator binding isotherms obtained for wild-type (WT) AdcR and selected AdcR mutants in the apo- and Zn^II₂-states.

Figure 6—figure supplement 3. Representative Zn^II-binding isotherms obtained from a titration of apo (metal-free) wild-type AdcR or a mutant AdcR and mag-fura-2 (mf2) with ZnSO₄.

Figure 6—figure supplement 6. Backbone ¹H-¹⁵N TROSY spectra showing the resonance assignments of (A) apo L57M AdcR and (B), Zn^II L57M AdcR.

Figure 6—figure supplement 7. Backbone chemical shift perturbation (CSP) maps for L57M AdcR. Apo-L57M AdcR and for Zn^II₂ L57M AdcR at pH 5.5, 50 mM NaCl, 35°C relative to WT AdcR as indicated.

Figure 6—figure supplement 8. ¹⁵N NMR spin relaxation analysis of L57M vs. wild-type AdcR.

Figure 6—figure supplement 9. Heteronuclear NOE analysis of L57M vs. wild-type AdcR. Apo- and Zn^II₂ L57M AdcR values of the ¹⁵N-{¹H}-NOE (hNOE) are painted onto the 3tgn structure, relative to the WT parameters reproduced here to facilitate comparison.

Table 2. DNA binding parameters for wild-type AdcR and substitution mutants^*.

DNA binding experiments and determination of allosteric coupling free energies (∆G_c)