Protein folding in vitro and in the cell: from a solitary journey to a team effort

Miranda F Mecha; Rachel B Hutchinson; Jung Ho Lee; Silvia Cavagnero

doi:10.1016/j.bpc.2022.106821

. Author manuscript; available in PMC: 2023 Aug 1.

Published in final edited form as: Biophys Chem. 2022 Apr 29;287:106821. doi: 10.1016/j.bpc.2022.106821

Protein folding in vitro and in the cell: from a solitary journey to a team effort

Miranda F Mecha ^a,^†, Rachel B Hutchinson ^a,^†, Jung Ho Lee ^a,¹, Silvia Cavagnero ^a,^*

PMCID: PMC9636488 NIHMSID: NIHMS1839739 PMID: 35667131

Abstract

Correct protein folding is essential for the health and function of living organisms. Yet, it is not well understood how unfolded proteins reach their native state and avoid aggregation, especially within the cellular milieu. Some proteins, especially small, single-domain and apparent two-state folders, successfully attain their native state upon dilution from denaturant. Yet, many more proteins undergo misfolding and aggregation during this process, in a concentration-dependent fashion. Once formed, native and aggregated states are often kinetically trapped relative to each other. Hence, the early stages of protein life are absolutely critical for proper kinetic channeling to the folded state and for long-term solubility and function. This review summarizes current knowledge on protein folding/aggregation mechanisms in buffered solution and within the bacterial cell, highlighting early stages. Remarkably, teamwork between nascent chain, ribosome, trigger factor and Hsp70 molecular chaperones enables all proteins to overcome aggregation propensities and reach a long-lived bioactive state.

Keywords: Cotranslational folding, aggregation, ribosome, chaperones, kinetic trapping, free landscapes

Overview.

For most proteins, a well-folded native three-dimensional protein structure is a prerequisite for biological activity. While intrinsically disordered proteins (IDPs) are an exception, protein folding remains a fundamental process for life on earth [1]. Yet, it is not well understood how unfolded proteins achieve their functional native state, despite the enormous number of conformations that they can potentially populate. The cell machinery ensures that predominantly native states are generated and that thermodynamically stable – and often undesirable – aggregated states are not. After being generated, many native and aggregated states of bacterial proteins remain kinetically trapped from each other, under physiologically relevant conditions. Given that the early stages of protein life are absolutely critical for the success of this process, this review will summarize current knowledge on protein folding in buffered solution and in the cell, including the early stages of protein’s life. Successful folding in the complex cellular environment has clearly evolved as a team effort and is often achieved through the combined involvement of many molecular players, including the ribosome and a variety of chaperones. This review focuses on three of these major players in bacteria, namely the ribosome, and the molecular chaperones trigger factor and Hsp70.

Protein folding and misfolding are intimately connected processes.

The practical consequences of aberrant protein folding are often severe and undesirable. For instance, protein overexpression in bacteria frequently leads to the formation of insoluble aggregates known as inclusion bodies. The latter species are difficult and expensive to disaggregate and to convert to the native state. This challenge often renders protein production in the basic-science, biotechnology, pharmaceutical and biomaterials settings extremely costly and inefficient [2, 3]. In medicine, protein misfolding and aggregation in higher organisms is often associated with deadly maladies known as proteinopathies, including brain disorders like Parkinson’s, Huntington’s, and Alzheimer’s disease [4, 5]. In summary, understanding how proteins fold is necessary to advance basic science, biotechnology and human health.

Protein folding research encompasses two major topics: the prediction of native structure from amino acid sequence and the mechanism by which proteins attain their native state. Significant advances were recently made in protein structure prediction. For instance, in the 2020 protein structure prediction challenge known as Critical Assessment of Structure Prediction (CASP), the software AlphaFold 2 (from the DeepMind artificial-intelligence company) predicted structures that matched the experimental structure of nearly two-thirds of the target proteins. This result is comparable, yet even better, to the predictions achieved with other protein-structure prediction programs (e.g., RoseTTaFold) [6] and to an earlier AlphaFold version [7, 8]. The AlphaFold family is based on a large structural-database and on sophisticated deep learning tools [7, 9]. Conveniently, AlphaFold 2 predictions have recently been integrated into online resources devoted to protein sequence and biochemical/physical properties, e.g., UniProt [10, 11].

The second protein folding topic is the mechanism by which proteins attain their native state. In addition to enhancing basic knowledge, understanding protein folding mechanisms is a prerequisite for comprehending and controlling the relative flux through the parallel kinetic paths that lead to either folding or aggregation in Nature. Mechanistic insights into the overall folding/misfolding/aggregation process promise to yield invaluable insights to design and experimentally generate next-generation aggregation-free biomaterials, biosensors and drugs, as well as to devise better strategies to combat a variety of deadly proteinopathies [12–14].

Refolding of small purified proteins into buffer: experimental studies.

Soluble and correctly folded proteins typically bury a significant fraction (60%–80%) of their nonpolar residues inside the core to minimize exposure to the hydrophilic environment of typical intracellular media [15–17]. Thus, an essential function of protein folding is the intramolecular burial of most nonpolar amino-acid side chains, rendering them inaccessible to mostly nonpolar/nonpolar-type interactions with other proteins. The latter interactions would eventually lead to intermolecular aggregation via the hydrophobic effect [18]. Research over the past decades sought to explain how small proteins achieve their soluble functional native structure. More recently, investigations have also explored how native structure formation is coupled with the avoidance of the pervasive risk of protein aggregation.

In the 1960s, Christian Anfinsen showed that ribonuclease A and other proteins fold reversibly from a chemically denatured unfolded state [19]. These results led Anfinsen to propose the well-known “thermodynamic hypothesis,” which states that the native state has the lowest free energy, out of all possible conformations. Anfinsen showed that reversible protein folding is fully determined by amino-acid sequence and environmental conditions, and that the folding process is under thermodynamic control [20]. In 1969, Cyrus Levinthal argued that because there are so many possible protein conformations, thermodynamic control is not sufficient for proteins to attain a folded state on biologically relevant timescales via a random conformational search [21]. On the other hand, it soon became clear that Levinthal’s paradox [22, 23] could be resolved if proteins were to fold via specific single or multiple pathways, progressively narrowing the accessible conformational space.

More recent experimental work identified the folding pathways of a variety of single-domain globular monomeric proteins [24–28]. In vitro folding has typically been studied upon refolding proteins from chemically or thermally denatured states. Upon refolding from denaturant for instance, proteins typically begin as a fairly expanded unfolded state bearing little or no secondary structure, and finally attain a compact, folded state bearing native secondary and tertiary structure [29, 30]. This final structure buries the nonpolar residues within the protein hydrophobic core, enabling the protein to be soluble within the hydrophilic environment of the cell.

The simplest folding pathways follow two-state mechanisms and include only the unfolded and folded states and a single transition state. Small (50–60 residues) single-domain, α-helical proteins that experimentally show two-state folding can form secondary structure before chain collapse (framework mechanism) (Figure 1A). Alternatively, secondary structure formation and chain collapse may occur concurrently (nucleation-condensation mechanism) (Figure 1B) [27, 31]. For instance, engrailed homeodomain from Drosophila melanogaster (59 residues) folds via the framework mechanism, human TRF1 Myb domain (52 residues) folds via nucleation-condensation, and human c-Myb transforming protein (54 residues) folds with a mixed framework/nucleation-condensation mechanism [27, 32] In general, small proteins with higher local α-helical propensity are more likely to fold via the framework model (leading to considerable secondary structure formation preceding global chain collapse) [33, 34].

Figure 1. — Some small (50–60 residues) proteins fold via (A) a mechanism dominated by secondary structure formation before chain collapse, or via (B) chain collapse preceding the formation of most secondary structure. (C) Plot of folding rate as a function of relative contact order (CO) for small (60–110 residues) two-state folding proteins. The graph is reprinted with permission from Figure 1A of *J. Mol. Biol., 277*, Plaxco, K. W.; Simons, K. T.; Baker, D., 985–994, Copyright (1998) [35]. (D) Larger proteins (> 60 residues) fold via more complex folding mechanisms, often including folding intermediates.

Remarkably, proteins were found to fold more slowly when their native state bears a greater number of long-range interactions. The latter are defined as noncovalent contacts between residues far away in sequence. This trend is described by the relative contact order (CO) parameter, which is defined as [35, 36]

C O = \frac{1}{L * N} \sum^{N} Δ S_{i, j},

(1)

where L is the total number of residues, N is the total number of noncovalent contacts between nonhydrogen atoms, and ΔSi,j is the sequence separation between interacting residues i and j [36]. As CO increases, the speed of protein folding decreases, for small two-state folding proteins (Figure 1C) [24, 35–37].

Refolding of mid-size to large purified proteins into buffer: experimental studies.

Most proteins in the cell are larger than 100 residues. For instance, the average protein size is 360 and 530 residues in prokaryotes and eukaryotes, respectively [38]. In addition, a significant fraction of proteins have multiple domains. For instance, 40–65% of proteins in prokaryotes and 65–80% of proteins in eukaryotes have multiple domains [39]. Larger single- and multi-domain proteins have, by definition, a large number of degrees of freedom and may experience more complex folding paths [40, 41].

Large proteins are more likely to have experimentally detectable folding intermediates [24, 40, 42]. While typical intermediates are on-path to the native state [43–55], a few off-path intermediates have also been identified [56]. In general, unequivocally identifying folding intermediates can be challenging. For instance, it was reported that transient aggregates can sometimes be mistaken for folding intermediates [57]. Two well-studied mid-size proteins are sperm whale and horse apomyoglobin, each of which has 153 residues and a multistate folding mechanism with experimentally-detectable compact intermediates (Figure 1D) [58–60]. Some of these intermediates are obligatory [61] and have a partially-folded structure bearing quasi-native features and lacking a few structural elements [58, 59].The major apomyoglobin folding intermediate later resolves to a fully native conformation via slight conformational rearrangements within the early-folding A, B, G and H helices, which then enable final native-structure formation during the later stages of folding [62].

One model for the folding of medium-to-large proteins (100–370 residues) is the foldon model, according to which proteins fold via progressively populating small independent cooperative units known as foldons (Figure 2) [28, 63–66]. Foldons are folding intermediates with some regions bearing native or quasi-native structure and other region unfolded or only partially folded. The presence of foldons naturally limits the dimensionality of the conformational search, providing a simple justification for how Nature avoids exhaustive sampling (Levinthal’s paradox) during protein folding [28, 67, 68]. Examples of proteins that fold via foldons include: cytochrome c (104 residues) [69], RnaseH (155 residues) [70], apoflavodoxin (179 residues) [71], apomyoglobin (153 residues) [72], staphylococcal nuclease (149 residues) [73] and the following two-domain proteins: maltose binding protein (370 residues) [74] and DapA (292 residues) [75].

Figure 2. — Scheme illustrating how proteins may fold via native-like intermediates denoted as foldons, which are generated either (A) sequentially or (B) in parallel.

Similar to foldons, some multi-domain proteins fold successfully when each domain folds independently [76]. Some multi-domain proteins show independent domain folding, including titin [77], fibronectin [78], and the double B domain of protein A (BBdpA) [79, 80]. Other proteins, including spectrin [81, 82], phosphoglycerate kinase [83], and the ankyrin repeat domains [80, 84] do not exhibit independently-folding domains, yet they are able to successfully refold from denaturant [39].

Folding of large purified proteins: experimental studies in vitro and in cell-like environments.

Many medium- to large-size proteins do not attain a 100% population of native state upon refolding from denaturant and give rise to some soluble or insoluble aggregates. Several examples of proteins that are known to form aggregates upon in vitro refolding from denaturant are shown in Table 1. This class of biomolecules includes proteins ranging from 153–550 residues, single and multi-domain proteins, monomeric proteins, and protein complexes. Without assistance from “folding helpers”, these and likely many other proteins are unable to fully populate their native state in solution, upon refolding into buffer at physiologically relevant temperature and pH.

Table 1.

List of proteins known to undergo aggregation upon in vitro refolding from denaturant.

Protein name	Number of residues)	Size of monomer (kDa)	Number of domains	Monomer or complex	Reference
Luciferase	550	60	2	monomer	[259]
Rhodanese	293	33	2	monomer	[324]
Rubisco	474 (large subunit), 122 (small subunit)	520 total, 50 for large subunit, 15 for small subunit	1	hexadecamer (8 small subunits, 8 large subunits)	[325]
Apomyoglobin	153	17	1	monomer	[326]
Galactitol-1-phosphate 5-dehydrogenase	346	37.4	2	tetramer	[109]
Glutamate decarboxylase alpha	466	52.7	3	hexamer	[109]
Threonyl-tRNA synthetase (ThrRS)	642	74	4	dimer	[109]
5,10-methylenetetrahydrofolate reductase	296	33.1	1	tetramer	[109]
S-adenosylmethionine synthetase	384	41.8	3	tetramer	[109]
Dihydrodipicolinate synthase (DHDPS)	292	31.3	2	tetramer	[109]
Tagatose-1,6-bisphosphate aldolase gatY (TBPA)	286	30.8	1	monomer	[109]
Tryptophanase	471	52.8	2	tetramer	[327]
α₁-antitrypsin	418	44.4	1	monomer	[328]

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One key parameter that facilitates misfolding and aggregation over folding during refolding from denaturant and upon release from the ribosome in the cell is a slower folding rate than aggregation rate. Small (<50 residues) two-state folders tend to fold quickly, with folding rate constants (k_f) greater than 12,000 s⁻¹ (Figure 3A, Supplementary Table S1) [85]. Yet, many larger two-state proteins fold more slowly, and some two-state folders have observed k_f values of less than 1 s⁻¹. Large proteins (>200 residues) with multi-state folding mechanisms have the slowest folding rate constants, with k_f values as low as 0.0004 s⁻¹ (Figure 3B, Supplementary Table S1) [85]. Given that folding and aggregation pathways proceed in parallel, proteins that fold slowly are in general more likely to misfold and aggregate than proteins that fold rapidly [86–88]. While evolution has granted a few slow-folding proteins the ability to concurrently aggregate even more slowly (see well-behaved proteins in Figure 3) thereby defying aggregation, this is often not the case. For instance, many proteins undergo insoluble aggregate formation upon release from the ribosome in the absence of chaperones [89]. In addition, slow-folding globins undergo both folding and soluble aggregate formation upon release form the ribosome in the absence of the heme cofactor and chaperones [86].

Figure 3. — (A) Plot illustrating the dependence of protein folding rate constant (k_f) on the number of residues for two-state folding proteins. Small (<50 residues) two-state proteins fold quickly with ln(k_f) > 9.4 (green box). Many larger two-state folders fold more slowly (orange box). (B) Dependence of protein folding rate constant (k_f) on the number of residues for multi-state folding proteins. Large (>200 residues) multi-state proteins have the slowest folding rates, with ln(k_f) <−2.5 (red box). A list of the proteins and references for the data in this plot is available as Supplementary Information Table S1.

Kinetic trapping of the native state relative to aggregates: experimental studies in vitro and in cell-like environments.

After proteins attain their native state for the first time, they continue sampling thermally accessible conformational states. Structural dynamics is often important for protein function. For instance, as pictorially described in the plots of Figure 4A, some non-aggregation-prone proteins routinely fold and unfold in the cell, displaying Anfinsen-like behavior [19, 20]. On the other hand, other proteins have more complex energy landscapes, which include aggregates (Figure 4B–C). These proteins typically experience some degree of kinetic trapping to avoid aggregation under physiologically relevant conditions. Native states can be kinetically trapped relative to aggregates or aggregation-prone intermediates, as shown in Figure 4B [90–93]. Alternatively, native states can also be kinetically trapped relative to their unfolded states, hence rarely unfold during the lifetime of their host organism (Figure 4C) [94, 95].

Kinetic trapping of the native state relative to aggregates (Figure 4B) has been detected under physiologically relevant conditions for a number of proteins. A few eukaryotic proteins (including bovine insulin, and human β2-microgloblin, lysozyme and αβ-crystallin) are kinetically trapped and metastable relative to amyloid fibrils at pH 7 [90, 91, 96–98]. In addition, Varela et al. showed that sperm-whale apomyoglobin and most soluble proteins of the E. coli bacterium (ca. 2,246 – 2,545 proteins) are kinetically trapped relative to aggregates that are not necessarily amyloid in nature, under physiologically relevant conditions [92]. This result was found to hold at concentrations much lower than physiologically relevant concentrations [92]. In this study, kinetic trapping of bacterial proteins was demonstrated by heating the E. coli proteome, enabling it to transiently populate landscape regions inaccessible at physiological temperatures. The proteome was then slowly cooled, thus reverting back to physiologically relevant landscapes (Figure 4D). As shown in Figure 4D and 4E, after the heating-and-cooling process, most E. coli proteins form insoluble aggregates under both reducing and non-reducing conditions. Given the negligible extent of covalent protein modifications (assessed by mass spectrometry), this result demonstrates the presence of kinetic barriers that typically prevent conversion of several E. coli native proteins to the “aggregated region” of the landscape, under physiologically relevant conditions [92]. Kinetic trapping relative to insoluble aggregates occurs mostly for proteins larger than ca. 25 kDa at 0.5 – 3.5 mg/mL total protein concentration (Figure 4F) [92]. The above findings are significant because they show that many bacterial proteins have an energy landscape that includes aggregates. Native states are kinetically trapped relative to these aggregates under physiologically relevant conditions, hence they do not convert. It is common knowledge that thermally denatured proteins are particularly aggregation-prone. Indeed, some of this aggregation is known to be due to covalent protein modifications (e.g. new disulfide bridges upon boiling an egg). However, the study by Varela et al. (under reducing conditions) ruled out that the observed aggregation is a consequence of covalent modifications. The work was based on the analysis of an E. coli S100 protein mixture. Hence, its conclusions apply only within that mixture. While pure proteins like sperm whale apomyoglobin were also found to exhibit analogous behavior, future investigations on additional isolated proteins will contribute to establish the generality of the present findings.

Importantly, aggregation rates can be modulated by protein concentration and by environmental conditions that modify protein energy landscapes. Some changes in environmental conditions may even lead to protein covalent damage. For instance, an increase in protein concentration and kinetic-barrier curvature (for the aggregation rate-determining steps), as well as a decrease in barrier height, are sufficient to trigger pervasive aggregation. The latter phenomena are known to play a role in the case of deadly proteinopathies.

Major trends upon protein refolding from denaturants.

In summary, experimental studies to date show that there are some general trends in protein folding. These include: (a) a higher flux of folding via the framework model (leading to considerable secondary structure formation preceding global chain collapse) for small proteins with high local α-helical propensity, (b) highly populated folding intermediates, some of which are native-like (i.e., foldons), in proteins larger than ca. 100 residues, (c) an inverse correlation between relative contact order CO and folding rate constants for apparent 2-state folders. Finally, small (<50 residues) two-state folders fold quickly (k_f > 12,000 s⁻¹), many larger two-state folders fold more slowly (k_f = 0.13 s⁻¹ −12,000 s⁻¹), and large (>200 residues) multi-state folders fold the slowest (k_f = 0.0004–0.08 s⁻¹) [85]. Yet, despite the above general trends, folding pathways upon refolding from denaturant and in the cell are overall quite diverse, for different protein folds [25, 27, 65].

Computational simulations of protein folding.

Computer simulations were extensively employed to characterize folding pathways and to define leading features of conformational energy landscapes. Computational approaches to simulate protein folding events, including molecular dynamics (MD) and Monte Carlo simulations and genetic algorithms, have been reviewed by Li et al [99]. The main challenge with simulating protein folding paths is that the typical 100 μs – ms timescale for this process is significantly longer than the capabilities of the traditional MD method. The first MD protein-folding simulation, carried out in 1998, required two months. This effort focused on the folding of villin, a 36-residue protein that folds on the microsecond timescale [100]. Advances in simulation algorithms and supercomputer technologies made it possible for MD methods based on unbiased empirical force fields to simulate the folding of ~100-residue proteins on the ms-timescale [99, 101]. Yet, most proteins from eukaryotes and prokaryotes are 300 amino acids or longer [38], so their folding cannot yet be simulated by unbiased MD techniques. One option is to simulate the folding of larger proteins with biased force fields that favor native contacts. This strategy is embraced by Gō models and structure-based models to simulate the folding of multi-domain proteins ranging from 150–400 residues [[76, 101, 102]. Gō models and other native-structure-based models greatly simplify folding landscapes by allowing only native interactions [101]. While the latter methods are successful at predicting the experimental behavior of small and mid-size well-behaved proteins [103, 104], they are likely unreliable in the case of less-well-behaved proteins. In addition, Gō models are typically inadequate to describe protein misfolding in the presence of concurrent aggregation leading to non-native-like self-associated states. Modified Gō models that incorporate misfolding have been developed [105]. These models require knowledge of high-resolution structures of both native and misfolded states. These structures are unfortunately not available in the case of most soluble misfolded states [105]. Another strategy to model the folding of large proteins is to use Markov-state models (MSM), which can model long timescale dynamics [106, 107]. MSMs partition the system into multiple states, and assume that transitions between states are memoryless. In other words, the probability of going from state x to state y only depends on states x and y, not any previously occupied states. Short MD simulations can be used to model small conformational changes within each state and can then be combined to predict long-timescale dynamics [106, 107]. Current challenges with using MSMs to model protein folding include correctly identifying the MSM states and interpreting folding mechanisms from MSMs. Recent machine learning advances can be employed to address the latter challenges [107]. In summary, while more work is necessary to develop simulations that can successfully model the experimentally observed folding and misfolding/aggregation of midsize to large proteins, the future of this area of research holds promise [89, 108, 109].

Energy landscapes.

Experimental data are often consistent with small single-domain proteins folding through a single pathway with either no or few intermediates [25, 110]. Yet, theoretical models suggest that the unfolded states reach the native state via multiple parallel pathways [42, 104, 111–114]. It is worth noting that experimentally observed 2- or 3-state refolding kinetics is not incompatible with multiple parallel folding paths [114]. Further, single-molecule experimental studies were able to unequivocally identify the existence of multiple parallel folding pathways for a single-domain protein that shows fast, two-state folding kinetics in bulk measurements [115]. Therefore, it is likely that many proteins fold via multiple parallel pathways, even when these paths cannot be explicitly resolved in bulk refolding experiments.

One theoretical model that promotes parallel folding pathways is based on the concept of folding funnel. The existence of folding funnels was initially suggested by Dill in 1987 [111], and then thoroughly detailed by Wolynes [103, 104, 112], Dill [41, 113] and coworkers. Folding funnels can be portrayed as two-dimensional diagrams, as shown in Figure 5A [103, 104, 112, 116, 117]. The y-axis describes the protein’s effective potential energy, which includes the potential energy of the protein chain and free-energy contributions arising from interactions with the solvent. The change in effective potential energy is proportional to the fraction of native contacts (Q). The horizontal axis represents the conformational entropy of the protein (S_{prot, conf}), so that the funnel width coincides with S_{prot, conf}. Note that the conformational-entropy term is distinct from the total entropy, and it does not include entropy contributions involving the solvent. The width of the funnel narrows as the effective potential energy decreases, showing that conformational entropy gets smaller (hence disfavoring folding), as the protein approaches the native state. In summary, the model postulates that, as each protein folds, both effective potential energy and conformational entropy decrease in concert, leading to an overall decrease in Helmholtz free energy. This process renders the overall landscape funnel-shaped [104, 116]. The diagram in Figure 5A qualitatively shows that there are typically very few conformations sharing the same effective potential energy and separated by significant local barriers. Hence the landscape is only very weakly “frustrated”. Multiple conformations that share the same energy contribute to increasing the density of states. Thus, entropy contributions due to both density of states and to solvent-related configurations need to be considered, in addition to the conformational entropy illustrated in the diagram of Figure 5A, to compute the total entropy.

Figure 5. — (A) Folding funnel proposed by Wolynes and coworkers, showing how protein conformational entropy decreases in concert with effective potential energy, as a protein folds to its native state [104, 116]. (B) Helmholtz free energy landscape for proteins that do not have a free-energy transition state for folding. (C) Helmholtz free energy landscape for proteins that have a free-energy transition state for folding. In panels A-C, the native state has 100% native contacts (Q = 1), and the unfolded state has Q = 0. (D) Multidimensional energy landscape. The vertical axis represents the potential energy of any given protein conformation plus the free energy of solvation [113]. Figure 1D is reprinted with permission from John Wiley and Sons [41] from figure 37C in Protein Science 4, Dill, K. A.; Bromberg, S.; Yue, K.; Chan, H. S.; Ftebig, K. M.; Yee, D. P.; Thomas, P. D. Principles of Protein Folding — a Perspective from Simple Exact Models. 4, 561–602. Copyright (1995). (E) Standard-state Gibbs free energy landscape of a protein that cannot form aggregates at a given temperature, pressure and solution conditions. (F) Gibbs free energy landscape for a protein that can form aggregates at a given temperature, pressure and solution conditions.

In the canonical ensemble, the change in total free energy of folding (ΔF_tot) is defined as the difference between the changes in total internal energy (ΔU_tot) and total entropy (ΔS_tot) terms according to

Δ F_{t o t} = Δ U_{t o t} - T Δ S_{t o t},

(2)

where T is temperature and ΔS_tot includes changes in both protein conformational entropy and entropy related to solvent molecules. The total internal energy is equal to the effective potential energy averaged over all microstates (i.e., protein conformations).

The negative change in conformational entropy as the protein folds is energetically unfavorable and must be compensated by favorable internal energy changes and/or favorable solvent entropy changes, so that the actual folding process ends up being thermodynamically favorable (ΔF < 0) [116]. If the energetically favorable internal-energy and solvent-entropy changes fully compensate or override the unfavorable conformational-entropy changes, the free energy landscape is barrierless, as shown in Figure 5B [112]. When the energetically unfavorable changes exceed the favorable contributions, the free energy landscape bears a barrier, as shown in Figure 5C. [117, 118]. Bryngelson et al. denote barrierless and barrier-containing free-energy folding scenarios as type 0 and type 1, respectively [112].

In the case of proteins whose folding free-energy landscapes bear a thermodynamic barrier (ΔG^‡_tot > 0), experimental studies showed that the thermodynamic activation parameters for folding, including activation enthalpy (ΔH_f^‡) and entropy changes (ΔS_f^‡), can be energetically favorable or unfavorable, as shown in Table 2. The Gibbs activation free energy for folding may be entropy or enthalpy driven, depending on whether TΔS_f^‡ or ΔH_f^‡ has a larger magnitude [119–121] (Table 2).

Table 2.

List of experimentally determined thermodynamic activation parameters for the folding of several proteins (unfolded, U → transition state, TS).

Protein name	TΔS_f^‡ (U → TS) (kJ/mol)	ΔH_f^‡ (U → TS) (kJ/mol)	ΔG_f^‡ (U → TS) (kJ/mol)	Reaction is driven by:	Reference
Chymotrypsinogen	−155	−66.7	88	Entropy	[121]
Soybean trypsin inhibitor	−111	−8.0	103	Entropy	[121]
Chymotrypsin Inhibitor 2 (CI2)	27.4	59.0	31.6	Enthalpy	[119]
N-terminal domain of Ribosomal Protein L9	29.5	54.0	24.5	Enthalpy	[119]
Ig binding domain of Y43W point mutation of protein L	7.3	40.2	32.9	Enthalpy	[119]
Immunophilin protein FKBP12	15.1	53.1	38.0	Enthalpy	[119]
Transcriptional activator protein M2V GCN4-pl	4.0	23.4	19.4	Enthalpy	[119]
Cold shock protein B	19.9	44.8	24.9	Enthalpy	[119]
Cold shock protein B	−24 ± 2	31.6 ± 2.2	55.7 ± 1.0	Enthalpy	[329]
Common-type acylphosphatase	−49.3 ± −4.9	23.6 ± 2.4	72.9 ± 0.4	Entropy	[330]
Muscle acylphosphatase	−41.4 ± 4.1	40.7 ± 4.1	82.1 ± 0.4	Neither	[330]
Apocytochrome b5	−20.6 ± 5.5	42.4 ± 5.5	63 ± 8	Enthalpy	[331]
Heart cytochrome c	22 ± 9	59 ± 9	37 ± 13	Enthalpy	[332]

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Protein-folding landscapes can also be visualized according to Dill et al. [41, 113] as three-dimensional curves, as shown in Figure 5D. In this case, the y axis is denoted as internal free energy, and is essentially equivalent to the effective potential energy of the landscapes by Wolynes et al. These landscapes are generated in the isothermal-isobaric ensemble, which is particularly relevant to biological systems, and assume constant temperature and pressure. It is worth noting that protein free energy landscapes are highly temperature [118], as well as pressure-dependent [122, 123].

In the isothermal-isobaric ensemble, free-energy landscapes describe changes in the Gibbs free energy of the system (G) as a function of protein conformational coordinates. However, G varies before a chemical process reaches equilibrium (or before an irreversible reaction is complete), while standard-state free energy per mole (G°) does not [124]. Therefore, Cavagnero et al. proposed to plot protein folding free-energy landscapes as G° instead of G, as shown in Figure 5E [92, 93]. The standard-state free energy of the system G° is expressed on a per-mole-of-monomer basis, so that both monomeric and aggregated protein states can be reliably plotted within the same landscape [92, 93] (Figure 5, panels E and F). As discussed in a previous section, standard-state chemical-potential landscapes were recently employed to show that most bacterial proteins are kinetically trapped relative to a variety of aggregates [92, 93]. We have also adopted this type of representation in Figure 4 of the present review.

Getting back to effective-potential energy landscapes, the folding-funnel concept has also been employed to explain the folding of multi-domain proteins [76, 125]. Experimental studies show that some large multi-domain proteins are able to successfully and independently refold from denaturant. These proteins include titin [77], fibronectin [78], and the double B domain of protein A (BBdpA) [79, 80]. Computational studies proposed that these types of multi-domain proteins fold successfully via a “divide and conquer strategy,” according to which each domain folds independently [76, 125]. If each domain is able to fold independently, then several smaller folding funnels can be combined into a single large funnel. Therefore, the folding process is characterized by significantly fewer degrees of freedom than if interactions between domains were to play a role during the folding process (Figure 6) [76].

Figure 6. — Some multi-domain proteins show independent folding of domains, and their folding can be described by combining the independent folding funnels for each domain [76]. (A) Cartoon of three-domain protein. (B) Folding funnel for multidomain protein with non-independently folding domains. The total number of degrees of freedom of the full-length protein is equal to the product of the degrees of freedom of each individual domain [76]. (C) Folding funnels for individual domains and combined folding funnel for multi-domain protein whose domains fold independently. In this case, the total number of degrees of freedom for the full-length protein is equal to the sum of the degrees of freedom for each individual domain [76].

Moving from simple model systems to more complex environments.

The studies described above focused on proteins refolding from denaturant. Physiologically relevant systems, however, not only include larger proteins but also involve more complex and crowded solution environments compared to buffered solutions. These environments can affect protein aggregation propensity.

In principle, crowded environments could decrease protein aggregation propensity. Molecular crowding from large inert cosolutes tends to stabilize the native state [117]. The crowding molecules decrease the volume available to the protein, pushing the protein toward a compact state, and thus reducing the total entropy of the system [1, 117] Yet, at high protein concentrations (> 100 g/L), interactions between the crowding molecules and proteins can decrease protein stability [1]. In some cases, crowding increases aggregation rate. For instance, the aggregation rate of α-synuclein is 10-fold greater in the presence of crowding agents than in plain buffer [126].

In general, crowding alone cannot fully explain the effect of many types of cosolutes on protein folding because it does not account for electrostatic and hydrophobic interactions. Some cosolutes, including osmolytes, stabilize the native state via repulsive interactions with the protein [117]. Site-specific ligand binding can also increase the stability of the native state. Other cosolutes, including denaturants, destabilize the native state via non-specific binding to the protein surface. Salt ions also affect protein stability, and their effect differs depending on the ion concentration as well as the location of charge in the folded and unfolded protein [117, 127–129]. Interestingly, the presence of other proteins with different sequences does not significantly change protein aggregation propensity, within complex mixtures. For instance, when bovine serum albumin and consensus tetratricopeptide repeat are mixed in solution, the solubility of each protein depends on its individual concentration and is minimally affected by the concentration of the other protein [130].

The refoldability of proteins after chemical denaturation within a complex protein collection from E. coli lysate was recently analyzed [108]. This study employed limited proteolysis via proteinase K to determine whether proteins refold to the native state. The results showed that 33% of E coli proteins do not refold to the native state after denaturation. Even more proteins may exhibit this characteristic behavior, upon taking soluble aggregates into account. Proteins with many domains are more likely to misfold than proteins with a single domain [108].

In a different study, Niwa et al. measured the solubility of all E. coli proteins within an E. coli cell-free system upon release from the ribosome in the absence of molecular chaperones [89]. Their results showed that only 28% of non-membrane proteins are ≥80% soluble (see eSol database: http://www.tanpaku.org/tp-esol/index.php?lang=en) [89, 131]. Larger proteins are more likely to form insoluble aggregates [89]. Namely, while 42% of small proteins (<30 kDa) are soluble, only 14% of large proteins are soluble [89]. Although the above experiments were carried out in the presence of the strong T7 promoter, the expressed-protein concentration range (2–100 μg/mL, average = 33 μg/mL) is comparable to the endogenous concentration range of most proteins in E. coli (4.7 – 153 μg/mL [0.11–4.30 μM], excluding outliers, median = 29.6 μg/mL [0.87 μM]) [132]. E. coli cellular concentrations were estimated from experimental copy numbers, assuming an E. coli cell volume of 10⁻¹² mL. Proteins with concentrations larger than the third quartile value plus the quartile range (> 153 μg/mL, [>4.30 μM]) were considered outliers. This category included 17% of the 1,103 proteins quantified in this study [132]. In principle, higher concentration could decrease solubility relative to the results by Niwa et al. Apart from these selected high-abundance proteins, we expect the concentration of individual proteins generated via the above two methods to be similar, given the similar concentration ranges. Codon usage is not an issue, and any differences in translation rates in cell-free systems vs in vivo expression is would likely affect all proteins to a similar degree. The cell-free experiments performed by Niwa employ the strong T7 promoter, which leads to overexpression of bacterial proteins relative to conventional cellular production levels. However, cell-free systems have a lower percent of active ribosomes compared to live cells. Specifically, in the PURE system employed by Niwa et al., approximately 40% of ribosomes are active in protein synthesis at any given time, compared to 80% in E. coli cells [133, 134]. The lower ribosome activity of cell-free systems explains why the protein concentration in these experiments is comparable to cellular protein concentrations, despite the use of the T7 promoter. Note that the cell-free system employed in the study by Niwa et al. does not perfectly represent the cellular environment. This system lacks heme and other cofactors that may be required for correct folding of some proteins [135]. In addition, each protein was expressed individually. Therefore, proteins that give rise to hetero-complexes in live cells might show higher than regular aggregation levels in the work by Niwa et al. This outcome is likely in cases when the concentration of these proteins is higher than that of their complexation counterparts. Importantly, while the all aggregates detected by Niwa et al. were insoluble, it is known that some proteins can also form soluble aggregates upon release form the ribosome, if chaperones are not present [86]. Hence, the results by Niwa et al. may underestimate the actual extent of protein aggregation upon release from the ribosome, in the absence of molecular chaperones.

Once formed, soluble and insoluble aggregates are often kinetically trapped in E. coli, relative to the native state [92]. This phenomenon is responsible for the persistence of long-lived aggregation-free bioactive conformations. Further, amyloid aggregates are typically highly thermodynamically stable [42, 88]. While cellular quality-control systems can disaggregate and degrade misfolded proteins later in life, these processes are energetically costly [136–139]. Thus, correct folding in the early stages of protein life, including cotranslational and immediately post-translational folding, is critical for long-term protein solubility and function. The above experimental studies, performed in the absence of molecular chaperones, show significant levels of aggregation. Yet, in living cells molecular chaperones are present and enable the correct refolding of the numerous proteins that would otherwise aggregate, upon release from the ribosome.

Protein folding in the cell: The role of the ribosome.

The ribosome alters the folding energy landscape because many proteins begin folding cotranslationally, before they are fully synthesized and while they are still bound to the ribosome [24, 140–148]. Translation is vectorial and enables some proteins to fold sequentially, with N-terminal regions folding before C-terminal regions [149–152] [145], and sometimes enabling separate domains to fold independently. Independent folding of domains decreases the protein’s number of degrees of freedom (Figure 6) [76] and could prevent inter-domain misfolding interactions [153]. Rare codons clusters that slow down translation may provide more time for cotranslational folding [154, 155]. Rare codons often appear within protein domains and separate small structural units [154]. Synonymous codon substitution that alters translation rate can cause proteins to misfold, suggesting that Nature has optimized codon usage for correct folding [156].

The ribosome also reduces the number of accessible conformations by interacting with the nascent chain and spatially confining nascent chains motions. [141, 148, 157, 158] Nascent chains can interact with the ribosomal tunnel [159, 160] or surface [161–165]. The ribosomal tunnel is approximately 100 Å long and 10–20 Å wide [166, 167]. The tunnel can hold approximately 30–40 amino acids, depending on the nascent protein structure [158, 168–170], and it can fit more residues if the protein forms tertiary structure within the tunnel [171, 172]. Nascent proteins can form alpha-helical secondary structure [141, 158, 173, 174], tertiary interactions [142, 175], and even fully folded structures [171, 172, 176] within the tunnel. Nascent-chain compaction is a prerequisite for the folding of globular proteins and typically occurs after 54–59 nascent-chain residues have been synthesized [145, 171]. Larger tertiary structures can form within the vestibule [144, 177, 178] and outside the ribosomal tunnel [145, 179]. Ribosome-bound conformations may be dynamic and flexible [145, 175] The ribosome can destabilize full-length ribosome-bound protein structures outside the ribosome tunnel compared to released folded proteins [180].

Most single-domain proteins cannot fully fold into the native state until they are released from the ribosome and their C-terminal residues are available for folding. The C-terminal residues are usually important for folding because they bear key interaction counterparts, including sometimes residues expected to establish contacts with N-terminal regions of the protein chain [37, 181, 182]. Indeed, protein fragments lacking C-terminal residues are often insoluble [182]. Fortunately, the ribosome grants solubility to partially synthesized nascent chains [86]. Immediately post-translational folding sometimes involves structure formation by significant portions of the protein. For instance, apomyoglobin must incorporate at least 60 residues (40% of the total number of residues) into the native structure post-translationally [146]. Immediately after release from the ribosome, the nascent-protein region that becomes solvent-exposed may include a significant fraction of nonpolar residues. These nonpolar residues can either be buried intra- or inter-molecularly, giving rise to folding or aggregation, respectively. Therefore, the immediately post-translational steps are critical for the kinetic channeling of the nascent chain towards intramolecular folding, as opposed to intermolecular aggregation [86, 145, 146]. Once formed, most native and aggregated states in bacteria are kinetically trapped from each other, rendering later interconversion between these states highly unlikely [92].

Translation through the ribosome is sometimes sufficient to grant solubility to released proteins [86, 89]. Many proteins, however, require additional assistance from molecular chaperones to reach their soluble native structure [86, 89, 131].

Protein folding in the cell: The role of molecular chaperones.

Molecular chaperones act both co- and post-translationally and are able to prevent, and in some cases reverse, protein aggregation. Importantly, only 28% of the proteins synthesized with an E. coli cell-free system lacking molecular chaperones is soluble (excluding membrane proteins) [89]. Remarkably, molecular chaperones increase the solubility of 97% of these aggregation-prone proteins [131]. Correct folding and solubility are promoted by chaperones via a variety of mechanisms. Chaperones can catalyze conformational changes, including folding of unfolded states and unfolding of misfolded states, utilizing energy from ATP hydrolysis [183, 184]. Chaperones can also simply bind proteins and, in so doing, bury solvent exposed nonpolar regions and transiently decreases free-protein concentration. This chaperone action does not typically require ATP hydrolysis [183, 184]. Chaperones may be especially important to promote the folding of large and multi-domain proteins which tend to fold more slowly [36, 185–188], and are more likely to aggregate than two-state folding proteins [89, 108].

Bacterial cells contain a wide variety of chaperones that effectively mitigate the detrimental effect of misfolding and aggregation, including trigger factor (TF), the Hsp70 system, and GroEL/GroES [138, 189–191]. TF associates with nascent chains as they emerge from ribosomes, thus contributing to the prevention of aggregation and to the protein’s folding efficiency [192–196]. The affinity of TF for unfolded proteins is lower than the affinity of other chaperones. This thermodynamic property is accompanied by the rapid binding and release of client proteins [197], compatible with efficient translation [198]. DnaK, which is a prominent Hsp70 protein in prokaryotes, interacts with its substrates co- and post-translationally [199]. GroEL/GroES, a prokaryotic Hsp60 chaperone, acts downstream of DnaK upon de novo protein folding and facilitates correct folding through several functions including isolating proteins within its chamber (a.k.a. Anfinsen cage), catalyzing protein folding, and unfolding misfolded states [200–202]. ClpB, a prokaryotic heat-shock protein belonging to the Hsp100 class, solubilizes protein aggregates by threading protein chains through its central hexameric channel, thus facilitating disaggregation either alone [203, 204] or in combination with the Hsp70 chaperone network [205]. Figure 7 shows a graphical representation of the molecular chaperones that have currently been identified in E. coli. Given that the early stages of protein life are vital for long-term solubility and function, we focus on chaperones that act cotranslationally and immediately post-translationally. These chaperones include trigger factor and the Hsp70 system.

Figure 7. — This scheme applies to gram-negative bacteria, e.g., *E. coli*. Abbreviations: SRP = signal recognition particle, REMPs = redox enzyme maturation protein, TatABC = twin-arginine translocation, TF = trigger factor.

Protein folding in the presence of the trigger factor chaperone.

Trigger factor (TF) is the only known ribosome-associated chaperone in bacteria (Figure 8). It was discovered by Crooke & Wickner who demonstrated that TF promotes the folding of the pro-OmpA protein to its membrane-assembly-competent form [206]. TF is both a chaperone and a cis/trans prolyl-isomerase [207], and it binds the ribosome with a 1:1 stoichiometry (Figure 8A). The cellular TF concentration is ~50 μM [208, 209]. This value is comparable to the ribosome concentration, though the latter varies as a function of cell growth rate. Ribosome-unbound TF undergoes a monomer-dimer equilibrium [209]. TF does not bind ATP and interacts with nascent chains cotranslationally (Figure 8B) [184, 192]. Deletion of TF in E. coli under regular growth conditions is not lethal, but the combined deletion of TF and DnaK causes protein aggregation and cell death [191, 199, 210]. TF binds to ribosome-bound nascent chains of most cytosolic proteins, outer membrane proteins, and periplasmic proteins [192, 211]. TF was also found to assist the refolding of some denatured proteins in vitro [197, 212–214]. Upon binding nascent proteins, TF delays acquisition of the fully native state and increases the ultimate yield of bioactive protein [137, 215]. Off the ribosome, TF binds client proteins in a predominantly unfolded conformation [216]. On the ribosome, TF reduces the force exerted by a cotranslationally folding chain, suggesting that it increases the population of unfolded nascent protein [217]. TF was also proposed to generate a “protected” space where nascent chains may be shielded from degradation and aggregation and may potentially fold cotranslationally (Figure 8B) [192, 218–220]. See additional comments in the section titled “Structure and dynamics of trigger factor client proteins”.

Trigger factor structure and function.

TF is a 48 kDa (432 residues) protein comprising a ribosome-binding N-terminal domain, a peptidyl-prolyl isomerase (PPIase) domain, and a C-terminal domain [219, 221]. TF was described as having a dragon-shaped structure, with the N-terminal domain as the tail, the PPIase domain as the head, and the C-terminal domain, located in the central portion of the structure, forming the two arms [219]. The N-terminal domain binds ribosomal protein L23 and can also interact with L29 [219, 222–224]. TF’s PPIase activity has been demonstrated in vitro [207, 225]. Interestingly, this domain is not necessary for TF’s in vivo chaperone function [210, 213, 226]. The C-terminal domain performs the main chaperone function and TF fragments containing only the C-terminal domain prevent aggregation and promote folding in vitro [226] while fragments lacking the C-terminal domain show decreased chaperone activity [227].

Structure and dynamics of trigger factor client proteins.

Nascent proteins can interact with all three domains of TF [216]. TF typically binds nonpolar regions of nascent proteins [198, 228], though it can also interact with hydrophilic regions [192, 193]. The PPIase domain of TF binds eight-residue sequences enriched in aromatic and basic amino acids (Figure 8D) [229]. In vitro experiments featuring purified TF-client protein complexes revealed that this chaperone binds proteins with 40 or more residues [160, 220, 230]. On the other hand, in vivo investigations showed that TF binds ribosome-bound nascent proteins of 100 or more residues [211]. It has a higher affinity for ribosomes carrying nascent chains than for empty ribosomes, supporting its cotranslational role [198, 231]. TF’s affinity for the ribosome [145, 231] and nascent chains [232] increases with chain length, likely due to increased interactions between nascent chains and TF. Nascent proteins appear to move along the TF structure as they emerge from the ribosome. For instance, ribosome-bound isocitrate dehydrogenase interacts with the N-terminal domain first and, as the nascent chain elongates, it proceeds through the TF’s arms and then reaches the PPIase domain [220]. Some client proteins bind concurrently bind multiple TF proteins. For instance, PhoA can bind to up to three TF proteins (Figure 8C, E–G) [216].

Crystal [223, 224] and a cryoEM structures [232] show that, when TF binds the ribosome, its N-terminal ribosome binding domain undergoes a conformational change that exposes a nonpolar region to the ribosomal tunnel. This conformational transition enables TF to interact with nonpolar regions of unfolded nascent proteins [216, 217, 228, 233, 234]. TF was also proposed to create a shielded environment supporting aggregation-free cotranslational folding [218]. A crystal structure of an E. coli TF bound to Haloarcula marismortui ribosomes [219] show there is sufficient space between TF and the ribosome for a small to medium single-domain nascent protein to fold. This potential folding cavity was also shown in cryoEM structure of an E. coli TF and ribosome complex [220]. Yet, another crystal structure of the D. radiodurans TF and ribosome shows a much smaller space underneath TF that may not accommodate cotranslational folding [224].

Fluorescence anisotropy-decay showed that ribosome-bound nascent proteins form a compact structure both in the absence and presence of TF [145]. Hence TF is not necessary for nascent-chain compaction, though it could affect its population [145]. The average residence time for TF binding to ribosomes is at least 10 s [198, 209, 228, 231, 235]. This time is sufficient for the translation 100–200 amino acids in E. coli [192] and for the concurrent binding and unbinding of TF to nascent chains as they elongate, which occurs on the ms timescale [197, 236].

The two TF modes of action outline above, namely enhancing the population of unfolded clients and providing a protected environment supporting some nascent-chain compaction, are not mutually exclusive.

The known conformational flexibility of TF [219, 220, 223, 224, 232] is consistent with its ability to interact with a variety of nascent chains [237]. Finally, TF can also act post-translationally, by binding ribosome-released proteins [197] or by remaining bound to nascent chains after they are released from the ribosome [193, 228]. Post-translational interaction with TF may help stabilize protein monomers until they are assembled into complexes. This proposed role of TF in complex assembly is supported by the observation that cells lacking TF show a ribosome assembly defect under heat-stress conditions [193].

Protein folding in the presence of the Hsp70 chaperone.

In 1962, Ferruccio Ritossa observed that Drosophila larvae under heat stress show a “puffing pattern” around chromosomes that was later shown to result from an upregulation of the heat shock protein now known as Hsp70[238–245]. Later, the presence of Hsp70 chaperones was identified within wide a variety of organisms [244].

Hsp70 chaperones are ATP-dependent proteins that are routinely produced within the cell cytosol under non-stress conditions and that are also upregulated upon heat stress. Hsp70s are highly conserved and very important for maintaining cellular life [246, 247]. Hsp70s and Hsp70-like proteins are found across a wide variety of organisms, including prokaryotes, eukaryotes and even most archaea [149, 246], which are missing several other classes of chaperones (e.g., Hsp100 and Hsp90/83 [248]. The only chaperone more universally represented than Hsp70 is the Hsp60 chaperonin (known as GroEL in bacteria), which evolved first and is present in all living organisms [249]. While Hsp70 is widespread and generally ubiquitous, it is not universally represented and is missing from the genome of most hyperthermophiles [248, 250, 251] and two specific classes of bacteria [252].

The Hsp70 chaperone system includes Hsp70 and its cochaperones. In E. coli, this system includes DnaK (E. coli Hsp70) and cochaperones DnaJ (Hsp40) and GrpE. The latter is a nucleotide exchange factor (NEF). The Hsp70 chaperone system is considered a “central hub” in E. coli cells (Figure 7) due to its ability to interact with a wide variety of client proteins and due to its capability to influence a variety of cellular processes, spanning from de novo protein folding to protein transport and disaggregation [199, 253]. The concentration of Hsp70 within an E. coli cell is approximately 30–50 μM [254], and the total E. coli protein concentration is 5–8 mM [255]. Therefore, not all proteins in a cell can associate with the Hsp70 chaperone at once [256, 257]. DnaK displays a preference for 30–75 kDa client proteins and binds ~20% of newly synthesized proteins in the E. coli proteome (Figure 9A) [199, 258]. The Hsp70 chaperone system maintains cell homeostasis by holding unfolded proteins to prevent aggregation and by unfolding misfolded client proteins, so they can fold correctly [257, 259].

Figure 9. — (A) Structure of ADP-bound (or nucleotide-free) Hsp70 chaperone (DnaK from *E. coli*). PDB ID: 2KHO. (B) Structure of ATP-bound DnaK chaperone. PDB ID: 4B9Q. (C) Client-protein binding motif for interaction with the *E. coli* Hsp70 chaperone DnaK, defined according to [284, 333]. Note that the positively charged residues flanking the central nonpolar core are progressively less important, as the sequence separation from the core increases.

The Hsp70 chaperone interacts with client proteins both co- and post-translationally to promote correct de novo folding of nascent chains [145, 191, 253]. Hsp70 assists the disassembly of protein complexes during bacteriophage replication [260], protein transport across membranes [261], and promotes the assembly of tail-anchored proteins within the cell membrane [262]. The Hsp70 chaperone system was reported to help disaggregating small aggregates and, in conjunction with other chaperones (e.g., ClpB), it was shown to assist the disaggregation of large aggregates [263–266]. The Hsp70 chaperone system can either take over client proteins from other chaperone systems and/or transfer them to other chaperone networks. Relevant chaperone networks include GroEL/ES [189], heat shock protein 90 (Hsp90) [267] and other small heat shock proteins like IbpA, IbpB, or inclusion-body binding proteins [268, 269].

Interestingly, Hsp70 is capable of preventing harm arising from deleterious mutations, thus granting key benefits to the parent organism in terms of both health and evolutionary rates [270, 271]. Therefore, Hsp70 and other chaperones allow organisms to experience greater genetic variation without harmful effects on fitness and could increase the species’ ability to evolve [270, 272]. In proteo-bacteria, client proteins with a high binding affinity for Hsp70 evolve faster than client proteins with low binding affinities for this chaperone [270, 271]. Given that Hsp70 increases protein evolution rate, overexpression of this chaperones may promote the efficiency of directed evolution [271]. Yet, chaperones do not always promote evolution, and other studies showed that Hsp70 and other chaperones sometimes decrease the client-protein evolution rate [273]. Additional future studies are necessary to fully understand the link between Hsp70 and evolutionary rates.

Not surprisingly, suboptimal Hsp70 function is linked to disease. If genes encoding the trigger factor (TF) and Hsp70 chaperones are concurrently knocked out, E. coli cells are no longer viable. This finding implies that the combination of TF and DnaK is essential for E. coli life. If only one of the two chaperone systems is knocked out, cells can survive but are more susceptible to stress [191, 210, 246]. Eukaryotic Hsp70 knockout or downregulation leads to increased levels of amyloid plaques in neurodegenerative diseases including Alzheimer’s and Huntington’s. Interestingly, while Hsp70 upregulation reduces the aggregation of plaque-forming proteins (a favorable effect), it also disfavors the apoptosis of cancerous cells (a deleterious effect) [274, 275]. Therefore, a carefully balanced chaperone concentration is required to support optimal health.

Hsp70 structure and function.

DnaK, the E. coli Hsp70 chaperone, consists of two domains comprising a 45 kDa nucleotide-binding domain (NBD) and a 25 kDa substrate-binding domain (SBD) [276]. The NBD contains two lobes that form a cleft that contains a binding site for a nucleotide (ATP or ADP) and specific cations (Mg²⁺ and 2 K⁺) [277–279]. The nucleotide state determines the conformation of Hsp70. If the chaperone is nucleotide-free or bound to ADP, then the two domains behave independently (Figure 9B). If Hsp70 is bound to ATP, then the chaperone lobes within the NBD rotate which subsequently cause the NBD and SBD domains dock to each other (Figure 9C) [279, 280].

The SBD contains two subdomains: the alpha-helical lid (SBDα), and the β-sheet pocket (SBDβ) [281]. SBDβ contains two beta sheets and two loops that form the pocket where the client protein binds [281, 282]. The conformation of the SBD varies between an open state when ATP is bound to the NBD and a closed state when the NBD is ADP-bound or nucleotide-free [278]. Crystal structures show that the binding pocket of ATP-bound DnaK exists in multiple open conformations and is likely dynamic and flexible [279, 282, 283]. The binding pocket preferentially interacts with a 4–5 residue long client-protein motif comprising aromatic (Phe, Tyr) or aliphatic (Val, Leu, Ile) nonpolar residues flanked by ca. four positively charged amino acids (Figure 9D). The characteristics of the amino acids towards the center of this motif are more important than the outer amino acids, for predicting binding to Hsp70 [284]. Interestingly, this binding motif occurs on average every 36 residues in most client proteins [284, 285].

The Hsp70 chaperone cycle.

Hsp70 chaperone activity proceeds via a functional cycle, which includes the Hsp40 (a J-domain protein) and nucleotide exchange factor (NEF) cochaperones. [286–288]. This cycle can be split into four main steps, as shown in Figure 10. The different stages of the Hsp70 chaperone cycle are briefly outlined below.

Figure 10. — Hsp70 cooperates with co-chaperones DnaJ and GrpE through an ATP-dependent cycle to promote the folding of client proteins.

In the first step, the ATP-bound Hsp70 binds the client protein via hydrogen bonds and van der Waals interactions. However, that binding is very transient unless accompanied by ATP hydrolysis shown in step two [281]. ATP hydrolysis can occur in the absence and presence of client protein. However, the reaction is significantly slower in the absence of a bound client protein. To achieve maximal rate of ATP hydrolysis, a J protein (Hsp40s, DnaJ in E. coli) is needed. The J protein binds the client and transfers it to Hsp70 [247, 289–291]. J proteins have slightly different binding motifs to client proteins when compared to Hsp70. The J-protein binding motif enables the ultimate binding of Hsp70 to a wider range of misfolded or aggregated proteins, and likely targets this class of client proteins to Hsp70 because they may not be able to directly bind the Hsp70 binding motif described above (Figure 9D) [247, 290, 292, 293].

In the second step, ATP hydrolysis causes the SBD and NBD of ADP-Hsp70 to undock and behave independently, while staying covalently connected to the inter-domain linking region [294, 295]. The alpha-helical lid lowers towards the client protein, leading to increased client-protein affinity and slower client dissociation rate [247].

The third step involves departure of the ADP nucleotide from Hsp70. In bacteria, this step is rate-limiting and requires a nucleotide-exchange factor (NEF), e.g., GrpE (in E. coli) to promote the release of ADP, leaving Hsp70 in a nucleotide-free state [296, 297]. Upon nucleotide removal, NEF remains associated with Hsp70 and is thought to prevent ADP from rebinding [278, 298, 299]. While the α-helical lid is considered “closed” when DnaK is either ADP-bound or nucleotide-free, several studies showed that the lid is subject to slow dynamics and it occasionally reopens, thus allowing client proteins to bind/unbind the chaperone [279, 288, 300].

In the fourth step, ATP binds Hsp70 within the NBD cleft. The highly conserved nonpolar linker between the NBD and SBD upon ATP-binding, pulls the two domains together until they are firmly docked (Figure 9C) [280]. In addition, nucleotide-binding causes the α-helical lid to lift, enabling the client-protein to be released and the cycle to start anew [247, 278, 286, 295, 298, 299].

Structure and dynamics of Hsp70-bound client proteins.

The client protein can also change conformation during the Hsp70 cycle. The first NMR study of Hsp70-bound peptides showed that client proteins bound to nucleotide-free bacterial Hsp70 have a more extended conformation than in chaperone-free solution [301]. Later studies using electron paramagnetic resonance spectroscopy (EPR) confirmed these results for peptides bound to nucleotide-free, ADP-bound, or ATP-bound Hsp70 [302]. NMR studies on N-terminal fragments of apoMb alone showed that this protein has some helical structure [303]. Binding to Hsp70 unwinds the local helix structure of residues in the Hsp70 binding site [303]. Yet, regions distant from the binding site form non-native α-helical structure [304]. Single-molecule Főrster resonance energy transfer (FRET) experiments showed that the protein rhodanese (296 residues) lacks stable tertiary structure when bound to bacterial ADP-Hsp70 [305].

NMR studies by Lee and coworkers [306] showed that the drkN SH3 client protein, an N-terminal SH3 domain from Drosophila, populates multiple globally unfolded interconverting states while bound to ADP-Hsp70. The bound protein also populates additional spectroscopically undetectable states that account for 43% of the entire chaperone-bound population. This result is important because it shows that conformational sampling takes place while the client protein is bound to the ADP-Hsp70 chaperone [306]. Therefore, Hsp70-bound client proteins are dynamic chains that are able to sample distinct conformational states (and potentially fold, partially fold or unfold) while chaperone bound. Clearly, additional research needs to be performed to define the nature of the Hsp70-bound client more accurately-protein states and how they depend on client-protein amino-acid sequence.

Hsp70 promotes folding and prevents aggregation via two mechanisms. First, this chaperone “holds” (i.e., binds) client proteins in a predominantly unfolded or partially folded state, thus preventing aggregation by effectively lowering the concentration of client-protein conformations in solution. Second, Hsp70 promotes conformational changes within the bound client proteins that enable the conversion of misfolded client proteins to the folded state. In this review, we denote the first mechanism as “hold-only” and the second as “fold-promoting” behavior (Figure 11). Note that we use these terms instead of the more common “holdase” and “foldase” descriptors. A brief justification follows. In the biochemical literature, the “ase” suffix is typically employed to denote enzymes that catalyze reactions that lead to bond breaking or covalent scission of substrates into smaller components. While the Hsp70 chaperone system can lead to faster generation of the client-protein native state [259], in some cases, Hsp70 slows down native-state formation [307]. In both cases no covalent cuts are introduced. Therefore, we opted not to use the “ase” terminology.

Figure 11. — The diagrams in this figure are consistent with experimental results achieved with distinct classes of client proteins. (A) Hold-only model consistent with both computational and experimental results on non-aggregation-prone proteins bearing one Hsp70 binding site [307, 315]. (B) Fold-promoting models consistent with experimental results obtained with aggregation-prone client proteins bearing multiple chaperone binding sites per molecule. For instance, firefly luciferase (fluc) populate their native states more quickly and avoid generating aggregates in the presence of the Hsp70 chaperone system [259]. According to this fold-promoting model, the Hsp70 chaperone system catalyzes the conversion of misfolded monomers (M*) to the native state and, in so doing, increases the yields and observed rates of native-structure formation.

According to the “hold-only” mechanism, Hsp70 transiently binds client proteins whether or not they are aggregation-prone, thus decreasing the concentration of free proteins in solution (Figure 11A) [88, 246, 275]. This mode of action prevents aggregation because the nucleation and elongation rates of individual molecules undergoing nucleated-polymerization-like aggregation are concentration dependent. On the other hand, the rate of folding of monomeric proteins is not concentration dependent [308–310].

Several chaperone systems can adopt a hold-only-type mechanism, including the Hsp40, Hsp70 and GroEL/ES machineries [311–314]. Note that the Hsp70 and GroEL/ES chaperones may also facilitate folding and prevent aggregation via a fold-promoting-type mechanism [200]. The hold-only mode of Hsp70 action is supported by size-exclusion chromatography experiments that determined the degree of chaperone association of three distinct non-aggregation-prone model client proteins [315]. This study showed that, during folding away from equilibrium, Hsp70 interacts mostly with slow-folding proteins. In contrast, at equilibrium Hsp70 interacts preferentially with thermodynamically unstable proteins [315]. Similar conclusions were reached in separate experimental studies focusing on the apparent folding rate of RNAse H in the absence and presence of the Hsp70 chaperone system [307].

Optical tweezer experiments showed that Hsp70 binds and stabilizes unfolded maltose binding protein [316]. Similar results were obtained for Hsp70-bound drkN SH3 by NMR, except that additional conformations were found [306]. Earlier NMR studies with client peptides showed an effectively unfolded (conformationally expanded, β-sheet-like) population of Hsp70-bound peptides [301]. Similar results were later obtained by electron paramagnetic resonance (EPR) [302]. Single molecule FRET studies showed that the large client protein rhodanese concurrently binds several Hsp70 molecules [305]. Hsp70 binding to unfolded client proteins (e.g., RNAse H) slows down the observed rate of native-state acquisition [307]. Chaperone binding can occur directly to the ADP-bound state of Hsp70, suggesting that the hold-only mechanism does not require ATP hydrolysis for client binding [302, 306]. Therefore, interestingly, Hsp70 can both accelerate or slow-down acquisition of native structure via the fold-promoting and hold-only mechanisms, respectively. The experimental evidence available so far is consistent with the fact that Hsp70 uses the fold-promoting mechanism when interacting with aggregation-prone proteins that proceed via one or more misfolded intermediates [259]. Conversely, Hsp70 employs the hold-only mechanism upon interacting with non-aggregation-prone proteins [315, 317], which are characterized by free-energy landscapes similar to those of Figure 4A. The latter scenario is facilitated in the case of thermodynamic unstable and(or) slow-folding client proteins [315]. Additional work in this area is necessary, to more comprehensively characterize all viable scenarios. For instance, aggregation-prone client proteins that do not significantly populate misfolded intermediates may interact with Hsp70 via the hold-only mode.

The second mechanism adopted by Hsp70 to prevent client protein aggregation is denoted here as “fold-promoting” behavior. According to this mechanism, Hsp70 binds and unfolds misfolded proteins. In this way, Hsp70 enables misfolded states to bypass kinetic trapping relative to the native state give rise to the native conformation [318]. Fluorescence studies in the bulk and at the single-molecule level showed that ATP- and client-protein-bound Hsp70 undergoes ATP hydrolysis concurrently with unfolding of misfolded client proteins. Further, the unfolding of the misfolded state does not take place in the absence of ATP [259, 318]. This combined evidence strongly suggests that Hsp70 uses energy from ATP hydrolysis to unfold misfolded luciferase [259, 318]. Upon release from the chaperone, luciferase can then fold to the native state. Hydrolysis of five ATPs is required to enable the correct folding of one single luciferase protein, suggesting that one out of five unfolded proteins folds correctly to the native state, while the others misfold. Therefore, multiple cycles of chaperone binding and release are required for the client protein to fold correctly [259, 318].

Client proteins bearing more than one Hsp70 binding site may interact with multiple Hsp70 chaperones at once [305, 319]. Binding multiple chaperones causes steric repulsion that causes client proteins to adopt an expanded state [259, 305]. When multiple chaperones are bound to a single client protein, release from the chaperone is likely asynchronous. This causes the client protein to spend more time in a chaperone-associated state (bearing at least one bound chaperone) than if it were bound to one single chaperone [305, 320, 321]. Asynchronous release may allow different regions of the client protein to fold independently, similar to the foldon mechanism, and prevent misfolding interactions.

It was reported that Hsp70 can also assist protein disaggregation. This process has low efficiency in the presence of Hsp70 alone [265] and is much more effective when Hsp70 cooperates with other chaperones including Hsp100-type disaggregases including bacterial ClpB [138, 322, 323].

Conclusions.

A critical event in protein folding is the burial of most nonpolar residues away from the hydrophilic solvent. This process is accompanied by the formation of a spatially organized 3D structure, which is in most cases kinetically trapped from a variety of aggregated states, under physiologically relevant conditions. Some proteins successfully fold fast and independently. This category includes small, mid-size and large proteins. On the other hand, many proteins, especially some mid-size and most large multi-state-folding proteins, fold independently but slowly. Many mid-size and large proteins, bearing molecular-weight ranges highly represented in bacteria, are very aggregation-prone upon refolding from denaturant or as they emerge from the ribosome. These proteins require the cellular machinery in the early stages of their life, so that they populate bioactive states that remain kinetically trapped from misfolded aggregates under physiologically relevant conditions. In bacteria, the relevant machinery responsible for the formation of the native state at birth includes the ribosome and the molecular chaperones trigger factor and Hsp70. It is therefore clear that the early steps in protein folding in the cell, including co- and immediately post-translational folding, are essential for long-term protein solubility and function. They key aspects of this process are schematically illustrated in Figure 12.

Figure 12. — (A) Protein folding *in vitro* upon dilution from denaturant, and (B) ribosome and chaperone-assisted protein folding within the bacterial cell.

In conclusion, while some proteins are capable of folding independently when diluted from denaturant or upon release from the ribosome, many proteins need assistance from the cellular machinery in the early stages of their life. In this way, they remain bioactive and kinetically trapped from harmful aggregates over extremely long time spans.

Supplementary Material

NIHMS1839739-supplement-Supplementary_Material.pdf^{(157.9KB, pdf)}

HIGHLIGHTS.

Many proteins misfold and aggregate upon refolding from denaturant
Ribosome and chaperones kinetically channel aggregation-prone clients to native state
This review highlights the roles of ribosome, trigger factor and Hsp70 chaperones
The native state of bacterial proteins is often kinetically trapped relative to aggregates

ACKNOWLEDGMENTS

We are thankful to Xuhui Huang for helpful discussions, and we are grateful to Dmitry Ivankov for sharing the compiled data for protein folding rates that we used to generate Figure 3, and Supplementary Tables S1 and S2. We thank the National Science Foundation (NSF) for funding (grants MCB-2124672 and CBET 1912259 to S.C.). In addition, R.H. thanks the National Institute of General Medical Sciences of the National Institutes of Health for a TEAM-Science Fellowship (award number R25GM083252).

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PERMALINK

Protein folding in vitro and in the cell: from a solitary journey to a team effort

Miranda F Mecha

Rachel B Hutchinson

Jung Ho Lee

Silvia Cavagnero

Abstract

Overview.

Protein folding and misfolding are intimately connected processes.

Refolding of small purified proteins into buffer: experimental studies.

Figure 1. Overview of protein folding mechanisms upon dilution from denaturant or upon recovery from temperature jumps.

Refolding of mid-size to large purified proteins into buffer: experimental studies.

Figure 2. Foldons and protein folding mechanisms.

Folding of large purified proteins: experimental studies in vitro and in cell-like environments.

Table 1.

Figure 3. Effect of size and folding mechanism on protein folding rates.

Kinetic trapping of the native state relative to aggregates: experimental studies in vitro and in cell-like environments.

Figure 4. Kinetic trapping of E. coli proteome relative to insoluble aggregates.

Major trends upon protein refolding from denaturants.

Computational simulations of protein folding.

Energy landscapes.

Figure 5. Representative protein folding energy landscapes.

Table 2.

Figure 6. Funnel landscapes of multi-domain proteins.

Moving from simple model systems to more complex environments.

Protein folding in the cell: The role of the ribosome.

Protein folding in the cell: The role of molecular chaperones.

Figure 7. Pictorial representation of major pathways leading to protein native-structure formation in prokaryotes.

Protein folding in the presence of the trigger factor chaperone.

Figure 8. Protein folding in the presence of the trigger factor (TF) chaperone.

Trigger factor structure and function.

Structure and dynamics of trigger factor client proteins.

Protein folding in the presence of the Hsp70 chaperone.

Figure 9. Key structural features required for the interactions of a client protein with the Hsp70 chaperone system.

Hsp70 structure and function.

The Hsp70 chaperone cycle.

Figure 10. Scheme illustrating the major steps of the E. coli Hsp70 (a.k.a. DnaK) chaperone cycle.

Structure and dynamics of Hsp70-bound client proteins.

Figure 11. Simplified schemes illustrating chaperone-assisted protein folding.

Conclusions.

Figure 12. Summary of protein folding standard-state Gibbs free energy landscapes.

Supplementary Material

HIGHLIGHTS.

ACKNOWLEDGMENTS

REFERENCES

Associated Data

Supplementary Materials

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