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. 2022 Nov 9;94(46):16113–16121. doi: 10.1021/acs.analchem.2c03352

Linking Gas-Phase and Solution-Phase Protein Unfolding via Mobile Proton Simulations

Charles Eldrid †,‡, Tristan Cragnolini §, Aisha Ben-Younis ‡, Junjie Zou ∥, Daniel P Raleigh ‡,∥, Konstantinos Thalassinos ‡,§,*
PMCID: PMC9685592  PMID: 36350278

Abstract

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Native mass spectrometry coupled to ion mobility (IM-MS) combined with collisional activation (CA) of ions in the gas phase (in vacuo) is an important method for the study of protein unfolding. It has advantages over classical biophysical and structural techniques as it can be used to analyze small volumes of low-concentration heterogeneous mixtures while maintaining solution-like behavior and does not require labeling with fluorescent or other probes. It is unclear, however, whether the unfolding observed during collision activation experiments mirrors solution-phase unfolding. To bridge the gap between in vacuo and in-solution behavior, we use unbiased molecular dynamics (MD) to create in silico models of in vacuo unfolding of a well-studied protein, the N-terminal domain of ribosomal L9 (NTL9) protein. We utilize a mobile proton algorithm (MPA) to create 100 thermally unfolded and coulombically unfolded in silico models for observed charge states of NTL9. The unfolding behavior in silico replicates the behavior in-solution and is in line with the in vacuo observations; however, the theoretical collision cross section (CCS) of the in silico models was lower compared to that of the in vacuo data, which may reflect reduced sampling.

The study of protein unfolding is essential for defining protein stability and provides important insight into protein aggregation in protein misfolding diseases1 such as α1-antitrypsin deficiency,2 transthyretin amyloidosis,3 and β2 microglobulin amyloidosis.4 Many techniques have been developed to study protein unfolding, including circular dichroism (CD),5 nuclear magnetic resonance (NMR) spectroscopy,6 electron paramagnetic resonance (EPR) spectroscopy,7 fluorescence-based methods, and native mass spectrometry (MS) coupled to ion mobility (IM).8−12 They all offer advantages and disadvantages, but with the exception of single-molecule methods, solution-phase methods generally have difficulties characterizing heterogeneous mixtures. While powerful, single-molecule methods require labeling with often large and bulky fluorophores, MS-based methods are particularly well suited to probing heterogeneous mixtures and have the advantage that small amounts of material are required, and modifications or labeling with probes is not required.

Native MS is widely used to probe the native-like state of proteins via soft-ionization techniques, such as nanoelectrospray ionization (nESI)13−15 and enables measurement of the global protein fold,16 ligand binding,17 subunit composition of protein complexes18 and proteoforms.19 Ion mobility (IM) coupled to MS (IM-MS) adds an extra layer of information by separating isobaric protein ions via their 3-dimensional shape or collision cross section (CCS). IM functions by passing analyte ions through an inert buffer gas in a drift region. Ions of the same m/z but different conformation will be separated as more extended ions will experience a greater number of collisions with the buffer gas and so traverse the drift region more slowly than a more compact ion.20,21

IM-MS has been used to study protein dynamics and18,22,23 domain organization24 and to investigate the structural dynamics of disordered proteins.25 Protein ions can be collisionally activated (CA) by increasing the energy by which they are introduced into the mobility region. They are often, but not always,26 unfolded in a process called collision-induced unfolding (CIU). CA can give information on distinguishing features of monoclonal antibodies,12,27,28 the number of domains within a protein,11 and the thermal stability imparted by ligand binding.17 Adding an extra stage of IM separation (tandem-IM), to select out particular conformers, allows even greater disambiguation of the unfolding pathways of proteins by selecting precursor ions.21,26,29,30

While collision activation is able to give important information about unfolding and native-like states that are retained in the gas phase31−33 (in vacuo), it is not known whether gas-phase unfolding is comparable to unfolding in-solution (in-solution). As CCS is an inherently low-resolution structural parameter, complementary techniques are required to create a structural model of the unfolded protein in vacuo. Molecular dynamics (MD) simulations (in silico) are uniquely positioned to do so and can be coupled with IM-MS data; they provide atomistic detail, which is complementary to IM and can be benchmarked via comparison of experimental and theoretical CCS values.34

Simulations which replicate the gas-phase environment inside an IM drift cell are not as straightforward as simply simulating proteins without bulk solvent. Without the intramolecular coulombic repulsion brought about by charging, the extended states of gas-phase proteins are liable to collapse.35,36 In positive nESI, charged sites occur on exposed ionizable sites, such as the N-terminus, lysines, arginines, and histidines and can migrate between these sites, maintaining dynamic equilibrium.37,38 To account for these effects, frameworks which allow simulation of the dynamic protonation states of proteins in the gas-phase behavior have been developed.39−42

It is still unclear if unfolding in vacuo mimics the unfolding process in-solution, and it is also difficult to fully validate the assignment of in vacuo structures from in silico methods. To critically validate the methodology, a protein system for which there is detailed in-solution unfolding data must be used. To this end, we chose the N-terminal region of the ribosomal L9 protein from Geobacillus stearothermophilus as a model system.43,44 The L9 protein comprises two distinct globular domains joined by an α-helical linker in a “dumbbell” shape. Both the N-terminal (NTL9)45−51 and C-terminal (CTL9)52−55 domains are stable in isolation and fold cooperatively. Each has had its in-solution unfolding explored in detail through fluorescence studies, CD, and NMR line-shape analysis. The structure of the N-terminal domain has been determined via X-ray crystallography and adopts the same fold in isolation as in the intact protein.55 NTL9 is one of the simplest examples of the split β–α–β motif. The fold consists of a mixed α and β structure, with two α-helices sandwiching antiparallel 3 stranded β-sheet strands. The C-terminal helix of NTL9 forms part of the connection with the C-terminal domain, but there are no contacts between the N and C-terminal domains (Figure 1A–C).44

Figure 1.

Figure 1

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(A) Structure of the ribosomal L9 protein from Geobacillus stearothermophilus with the N-terminal construct (NTL9) colored in blue and the C-terminal construct (CTL9) colored in red. The Q57 residue, which is not included in either construct, is labeled. (B) NTL9 sequence. (C) NTL9 ribbon structure is colored in rainbow from N- to C-terminus, with the α-helices (hX) and β-strands (sY) labeled, as well as a loop that appears to show structure. (D) Location of the charged residues in NTL9. A ribbon structure is shown in gray with charged residues (aspartic acid (D), glutamic acid (E), lysine (K), and arginine (R)) in stick format, with nitrogens and oxygens in blue and red, respectively. Based on structure PDB ID: 1DIV.44

In this study, we combine native IM-MS and in silico unfolding of NTL9 to create an in vacuo model of unfolding. Upward of 100 repeats of an unbiased in silico method of thermal unfolding, using the approach described in Popa et al.,40 were performed. The unfolded models match the in-solution unfolding and are in line with in vacuo data. While the models of unfolding are in good accordance, there are discrepancies between experimental and theoretical CCS values of the final models. Analysis of the deviations provides clues to important factors which may affect the analysis and comparison of in-solution and in vacuo unfolding.

Methods

Sample Preparation

NTL9 was produced and purified as described previously.52 Lyophilized NTL9 was dissolved in 100 mM ammonium acetate pH 7.5 to 50 μM and frozen at −20 °C. On the day of data collection, the sample was desalted by buffer exchange using Amicon ultra centrifugal filtration units (Merck Millipore, U.K.), 6 times centrifuged at 14.0E3 g for 15 min at 4 °C using a Heraeus Fresco 17 centrifuge. The concentration was analyzed by Qubit assay (Thermo Fisher Scientific, U.K.).

Data Collection

Samples were directly infused into the mass spectrometer using nESI from gold-coated capillaries prepared in-house using a Flaming Brown P97 needle puller (Sutter Instruments Co) and a Q150R S sputter coater (Quorum Tech, U.K.). Single-stage IM data was collected on a Synapt G1 (Waters Corp, U.K.) using the parameters presented in Table S1. CCS measuring by TWIMS (TWCCSN2→He2, following notation described in Gabelica et al.56) calibration was performed using melittin (Sigma, U.K.), human insulin (Sigma, U.K.), ubiquitin (Sigma, U.K.), equine cytochrome C (Merck Millipore, U.K.), and β-Lactoglobulin (Sigma, U.K). (Figure S2).

Simulations

The NTL9 structural model was created from residues 1–56 of the full L9 protein (PDB ID: 1DIV).44 MD simulations were performed using Gromacs v2018.4. The simulation pipeline (see Figure S1) is as follows: the initial structure is checked for completeness, i.e., all residues contain all atoms. The version of the mobile proton algorithm40,41 (MPA) used during simulations requires nonchargeable side-chain residues at the N- and C-termini, so a C-terminal glycine residue was added via Modeller (v9.23); however, both the N- and C-termini remain chargeable. The Avidin model was supplied by the Konermann group and is based on PDB ID: 5IRU.57 Initial protonated and deprotonated topology files were created using pdb2gmx with the OPLSAA force field;58 the input values for the state of lysine, arginine, glutamate, aspartate, histidine, and termini were created using a python script (available at https://github.com/ThalassinosLab/charge_site_calculator). A charge library was created by copying the residue information from both the protonated and deprotonated topology files. A GROMACS and structure file was then created for the specific charge state. The MPA was used to distribute the protons across the structure, before equilibration. After equilibration and minimization (Tables S2–S4), a 20 ps simulation at the set temperature was run, after which the protons were rearranged. This cycle continued until the full time period had elapsed, which was either 4 ns at a set temperature (for thermal unfolding) or 100 ns (for coulombic unfolding) (Figure S1).

Data Analysis

IM-MS spectra were analyzed using MassLynx v4.1 (Waters Corp, U.K.) and DriftScope v2.1 (Waters Corp, U.K.). CCS calculations were performed using the spreadsheet available from http://www.homepages.ucl.ac.uk/~ucbtkth/resources.html, which uses the method of Thalassinos et al.59,60 and TWCCSN2→He2 values derived experimentally from first principles by Bush et al.61 CCS distribution (CCSD) metrics intensity weight mean (IWM) and intensity weight standard deviation (IWSD) were calculated using the following equation

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where I is the intensity for each arrival time, t is the arrival time, and n is the number of data points in the arrival time axis.62

For the simulations, the theoretical CCS (THCCS) of each frame was calculated every 2 ps for the heating simulations and every 10 ps for the charging simulations using the IMPACT63 pseudo-trajectory method, and the final structure was calculated using Collidoscope.64 Salt-bridge analysis was performed using VMD (v1.9.2)65 using a distance cutoff of 4 Å between the oxygen of an acidic residue and the nitrogen of a basic residue to identify salt-bridge pairings. Ensemble cluster analysis was performed using Chimera (v1.1.3).66

Results

Summary of Structure and Solution Unfolding of NTL9

The folding and unfolding of NTL9 in solution occurs in a two-step process, progressing from a folded globular to a transition state, onward to the unfolded state.46,47 During folding, 60–65% of the total native solvent accessible area is buried in the transition state.45 The C-terminal helix, hB, does not form any electrostatic salt bridges to the globular structure, although there are potential intra-helical salt bridges. The last few residues of the C-terminal helix are frayed in solution, and the helix likely undergoes additional fraying during thermal denaturation prior to full unfolding of the globular structure. The C-terminal helix is also partially populated in isolation.48 Removal of the final 5 residues, 51KQKEQR56, destabilizes the domain.51 Residues D8, E17, and D23 (Figure 1D) form interactions that are perturbed during unfolding.48 D8 is in a partially ordered loop which includes 5 lysine residues (7KDVKGKGKK16) and may form electrostatic contacts with several different side chains, E17 contacts the amide group of K14, and D23 forms a strong salt bridge with the N-terminal amino group.48 During thermal unfolding, the core of the structure consisting of the first helix and β-sheet likely, comprising the first 39 residues, unfolds after the unfolding/fraying of the C-terminal helix. The first 39 residues of the protein can fold in isolation but are prone to aggregate in solution.48

TWCCS Analysis

NTL9 displays charge states ranging from +4 to +8, and there appear to be two overlapping charge state distributions (CSDs) (Figure 2A,B). IM-MS analysis shows that charge states from +4 to +6 have a compact form, and states from +5 to +8 have an extended form. This suggests that NTL9 occupies a compact state of approximately 840 Å2, which is retained between +4 to +6, and extended states, which are maintained by intramolecular coulombic repulsion, going from 1030–1430 Å2. The compact peaks for +5 and +6 show large FWHMs, suggesting that they contain a multiplicity of conformations.(Table 1)

Figure 2.

Figure 2

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Representative experimental IM-MS data for NTL9. (A) Mass spectrum with charge states labeled; (B) average CCS peak top values; and (C) an example of CCS distributions.

Table 1. Experimental CCS Values.

+z TWCCSN2→He2 (Å2)
4 829 ± 3.24
5 837 ± 5.47, 1036 ± 2.15
6 850 ± 20.69, 1243 ± 2.65
7 1319 ± 6.97
8 1356 ± 5.01, 1433 ± 15.12
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Activation of the NTL9 charge states leads to the +4 state coming slightly more extended; every other charge state either transitioned into a previously present extended state or did not unfold (Figure S3).

Thermal Unfolding Simulations

Initial thermal unfolding simulations were performed in triplicate on the +5 charge state, as it is experimentally the most intense and is the lowest charge state to display a clear conformational change during collision activation (Figure S3). The thermostat was increased by 50 K over 4 ns for a total simulation time of 20 ns.40 One of the triplicates unfolded after approximately 8 ns once the temperature had increased to 400 K (Figure 3A,B); however, it recompacted under increased heating after the initial unfolding event.

Figure 3.

Figure 3

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Simulation outcomes of the unfolding of the +5 charge state by heating. (A) Initial triplicate of simulations, with the different temperature transitions marked out with dotted lines. (B) Simulation of the +5 state, which displayed unfolding, with histograms of the states at each temperature shown to the right. The shading represents the unsmoothed data, and the solid line represents smoothed with a mean window of 50, once. (C–I) various unfolded states of the +5 model with the time elapsed, theoretical CCS, and simulation temperature.

The simulation suggests an unfolding pathway where there is C-terminal unfolding, which, for example, can be shown by the formation of a salt bridge between E38 and K2 at later frames in the simulation (Figure S4). Other salt bridges which are diagnostic of particular in silico conformations are E48/54 to K51, which are characteristic of the C-terminal helix (hB).

The in silico thermal unfolding simulations of the +5 charge state were repeated 100 times (Figure 4). Only 24 outcome structures had THCCS values >1100 Å2 (Table S5), and cluster analysis suggests that 80% remain compact (Figure S6A,B Tables S5, and S7). Inspection of unique structures (structures that did not fall into a cluster ensemble) shows that only 13 structures display dissociation of hB from the protein core. This data suggests low reproducibility of in silico thermal unfolding. In many of the final states, structural rearrangement occurs to create a “flattened” structure, where there is a rearrangement of hA, sA, and sB, creating a set of interactions between a series of charged residues, including D8, K12, K14, K15, D48, D54, and R56.

Figure 4.

Figure 4

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100 replicates of the +5 in silico unfolding, showing the trace of the CCS of each frame as calculated by IMPACT, and then histograms of the final states calculated by IMPACT and Collidoscope.

The question of whether the low variability of unfolded outcomes was due to the system or the method was explored. NTL9 is from a thermophilic organism, and the domain is thermally stable, with a melting temperature of 78 °C at pH 5.4 in solution44 and therefore in silico thermal unfolding may not be appropriate. We attempted to replicate some of the observations from the original publication, which showcased in silico thermal of unfolding using the MPA. In the original publication, the homotetramer transthyretin (TTR) unfolding was simulated, and the authors were able to demonstrate charge-mediated subunit ejection, which is consistent with the observation of subunit dissociation and charge stripping observed in IM-MS studies,8,67−69 within 20 ns of simulations. Consequently, we explored the thermal in silico unfolding of a tetrameric protein of similar mass and structure, Avidin. None of the 100 × 20 ns simulations displayed subunit ejection; however, a partial unfolding of subunits was observed, presumably as a precursor to ejection (Figure S5 Tables S6, and S7). This suggests that the original MPA workflow does not reproduce thermal unfolding in silico in a reliable manner.

Coulombic Unfolding Simulations

Since the in silico thermal unfolding showed low reproducibility, we next investigated whether increasing the charging of the protein would lead to a better match with the in vacuo experimental observations. Simulations were performed for the +4 to +8 charge states at a steady temperature of 300 K, for 100 ns. As the charge increases, the theoretical CCS (THCCS) of the final state increases in line with the native IM-MS data (Figures 5 and S7–S11). While the THCCS of the in silico model increases, not all runs lead to an extended state, for instance, for the +7 state, run 1 retains a compact structure (Figures 5 and S9A). For each run, the root-mean-square deviation of the Cα atoms between the frame and the final state decreased and became mostly stable (Figure S12).

Figure 5.

Figure 5

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Theoretical CCS values of initial simulations, beginning from the same set with IMPACT analysis of full simulations in solid lines, with a separate plot showing the IMPACT value of the final structure shown as a dotted line and Collidoscope value as dots. Triplicate simulations of (A) +4, (B) +5, (C) +6, (D) +7, and (E) +8 charge state. The gray shading represents the unsmoothed data, and the solid line represents smoothed with a mean window of 5, once. (F) Mean THCCS values of the final states of the simulations calculated by IMPACT and Collidoscope.

The coulombic simulations show a consistent evolution of in vacuo structures (Figure 6), with +4 and +5 forming compact structures and +6 to +8 forming more unfolded structures, with the thermally unfolded +5 and coulombically unfolded +8 in silico models having comparable structures.

Figure 6.

Figure 6

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Selected final structures for the charging simulations after 100 ns for charges (A) +4, (B) +5, (C) +6, (D) +7, and (E) +8, with the theoretical CCS value calculated by Collidoscope.

To properly compare the in silico and in vacuo data, each of the coulombic simulations was repeated 100x, to create a theoretical CCS distribution (THCCSD) from a kernel density estimation of the final in silico models, which could then be compared to the experimental data. Replication instead of increasing the length of the simulations was chosen, as it was clear that 100 ns was a long-enough time period for a conformational sampling of an extended state to occur from the initial triplicates.

A comparison of the THCCSDs (Figure 7) shows that increasing the replicates creates a better likelihood of a model matching the in vacuo data. Interestingly, all of the THCCSDs appear to show some degree of multimodal behavior. Ensemble cluster analysis of the 100 final structures of each simulated charge state support this (Figure S18). Comparison of the theoretical and experimental CCSDs shows that while a high replication number is not able to reproduce something which exactly matches the experimental distribution, an overlap exists between the THCCS of some models and experimental values, meaning a subset of models reproduce the experimental data.

Figure 7.

Figure 7

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Comparison of simulated and experimental results. (A–E) Results of 100 replicates of the stable temperature simulations showing histograms of the final states calculated by IMPACT and Collidoscope and experimental CCS distributions, with the IWMCCS denoted by the dotted line, and the standard deviation shown by the faint dotted lines for (A) +4, (B) +5, (C) +6, (D) +7, and (E) +8 charge states. (F–I) Comparison of theoretical and experimental values. Mean THCCS value from initial triplicate simulation final states plotted against (A) experimental peak top CCS values and (B) IWMCCS. Mean THCCS value from X100 simulation replicate final states plotted against (C) experimental peak top CCS values and (D) IWMCCS.

Discussion

Creating robust pipelines for modeling in vacuo unfolding in silico would be highly advantageous for understanding protein behavior during IM-MS analysis. It would also be advantageous to be able to objectively evaluate if the unfolding observed in vacuo is relevant to in-solution behavior. This would help CA become a more informative structural biology technique: in much the same vein as how the demonstrations that soft ionization retains the solution-like structure and behavior70 facilitated the use of mass spectrometry a valuable tool for structural biologists.

NTL9, a well-studied system in-solution,45−51 appears to adopt a mixture of compact and extended states during ionization, which are observable in vacuo (Figure 2B,C). Trying to reproduce these states in an unbiased manner in silico, and hence create a model of unfolding, to allow comparison of in-solution and in vacuo data has had variable success. Both thermal and coulombic unfolding in silico matched the expected in-solution model: release of the C-terminal α-helix (hB), with dissociation of the central β-sheet and the first α-helix (hA). Replications of the in silico thermal unfolding did not show good reproducibility, and the original method for thermal unfolding40,41 produced unfolded structures only 13/100 times. This is most likely due to the lack of conformational sampling present in unbiased simulations and was not limited to the thermostable system NTL9.71−73 While the test system previously used in Popa et al., to demonstrate the utility of the MPA workflow was transthyretin (TTR),40 another well-studied homotetrameric system, Avidin, was used here. Avidin has a slightly higher intact mass than TTR, 64.0 kDa compared to 55.0 kDa; the structure used in our studies is truncated at the C-terminus, making it 54.8 kDa and similar in mass and size to TTR, meaning they should have a similar internal temperature when heated.57 In the original in silico studies, charge-mediated subunit ejection was observed for TTR; however, Avidin displayed no charge-mediated subunit ejection on the timescale of our simulations. The TTR structure used previously (3GRG) is in fact a mutant known as M-TTR (F87M/L110M).74 M-TTR shows a reduced self-association constant due to the substitution of F87 and L110 with the bulkier M residues at subunit interfaces,74,75 which may have affected the outcome of the simulations. The lack of subunit ejection observed in Avidin suggests that the thermal unfolding workflow has limited conformational sampling, which may be exacerbated by the proton hopping. Energy minimization studies64,76 show that 106–107 proton rearrangements need to be performed to reach an energy minima, which adds an extra layer of complexity outside of standard structural dynamics. While in this study, we opted for more replicates of shorter simulations, fewer numbers of longer, biased simulations may be required. Longer simulations may also allow the creation of more accurate theoretical CCSDs: while experimental CCSDs are a product of gas-phase conformations which do not interconvert on the timescales of drift separation, they are the result of dynamics of timescales exceeding the simulation time.

Comparison of TWCCS and THCCS values is an important part of bridging the gap between in vacuo and in silico studies. Here, we have compared several metrics which are used experimentally for IM-MS analysis: the peak top value and the intensity weighted mean (IWM) (Figure 7G,I). Comparison of the peak top values to the mean THCCS values is poor (Figure 7F,H) due to the fact that the average is unable to capture distinct populations. Using a method like IWMCCS gives better overlap as it better captures the weighting of multi-conformer ensembles.

From the simulations, high charging, i.e., +7 and +8, produce highly unfolded in silico structures; however, TWCCS values suggest that in vacuo, more compact conformers are favored. The discrepancy between the TWCCS and THCCS may be due to the difficulty of calculating THCCS of linear ions, as described by Kulesza et al.,77 which is highlighted in our study by the differences between the THCCS values calculated by IMPACT and Collidoscope for the same extended structures of the +7 and+8 charge states (Figures 5E and S18G–J). It may also be a function of TWCCS calibrant class: to get an accurate TWIMS CCS calibration, molecules of the same class as the experimental molecule must be used, i.e., native protein calibrants for globular proteins and denatured proteins for disordered or denatured proteins. The in silico structures suggest that the higher charge states of NTL9 have both ordered and disordered structural regions, meaning that neither calibrant class would be fully comparable. Other possible avenues for future exploration to close the gap between the THCCS and TWCCS include using different force fields during in silico model creation, as studies have shown that certain force fields, which are designed to replicate in-solution behavior, commonly produce models which favor either compaction or extension compared to experimentally derived CCS values.78 Furthermore, different methods to produce simulated ions could also be employed, as the complexity of comparing the molecular dynamics to experimental data is compounded by the differing behavior of folded and unfolded protein ions during desolvation. While folded proteins are regarded as ionizing via the charged residue model (CRM),79−83 unfolded proteins are believed to ionize via the chain ejection model (CEM).84,85 The two models could imply differing unfolding mechanisms, which the MPA may not be able to replicate fully. Desolvation simulations function by steadily removing solvent from a charged droplet containing the protein structure to create a gas-phase ion.31,32,86−90 While the simulation of the droplet itself is more computationally expensive, it may produce a more accurate initial structure for further simulation.

By combining in vacuo, in silico and in-solution data, we have shown it is possible to create coherent models of unfolding that link gas-phase and solution-phase behaviors. This approach shows promise and importantly, highlights multiple experimental and theoretical avenues to explore to further improve the methodology. We believe that the data and the analysis presented here both illustrate the power of the hybrid experimental computational approach and point the way for future developments.

Acknowledgments

The authors thank the Konermann group for the algorithm files and the Avidin structure and for responding to queries regarding troubleshooting of the MPA algorithm.

Glossary

Abbreviations Used

CA

collision activation

CCS

collision cross-section

CD

circular dichroism

CIU

collision induced unfolding

CTL9

C-terminal construct of the L9 protein

EPR

electron paramagnetic resonance

IM

ion mobility

IM-MS

ion mobility mass spectrometry

IWM

intensity weighted mean

IWSD

intensity weighted standard deviation

MD

molecular dynamics

MPA

mobility proton algorithm

MS

mass-spectrometry

NMR

nuclear magnetic resonance

NTL9

N-terminal construct of the L9 protein

THCCS

theoretical collision cross section

TTR

transthyretin

TWCCS

experimental collision cross section

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.2c03352.

  • in silico pipeline, in silico parameter tables, experimental parameter tables, CCS calibration charts, salt-bridge analysis, triplicate final structures of coulombic simulations, RMSD charts, full tables of in silico CCS values for 100× coulombic and thermal simulations, and ensemble analysis for simulations (PDF)

Author Contributions

C.E.: experimental design, data collection, simulations, analysis, and preparation of the manuscript. T.C.: simulations, experimental design, A.B.-Y.: data collection, J.Z.: sample preparation, D.R.: experimental design, writing. K.T.: experimental design, writing.

Funding from the Wellcome Trust, 107927/Z/15/Z, is acknowledged. CE is funded by BBSRC iCASE Award with Waters BB/L015382/1; T.C. is funded by Wellcome Trust Collaborative Awards in Science 209250/Z/17/Z.

The authors declare no competing financial interest.

Supplementary Material

ac2c03352_si_001.pdf (2.9MB, pdf)

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