Constant pH Molecular Dynamics Simulations Research Articles

Different pKa Shifts of Internal GLU8 in Human β-Endorphin Amyloid Revealing a Coupling of Internal Ionization and Stepwise Fibril Disassembly.

As a functional amyloid, human β-endorphin amyloid fibril features a β-solenoid conformation and store peptide hormones within acidic secretory granules, which would be released into the blood through fibril disassembly when the cellular milieu pH increases from acidic to neutral level on exocytosis. To gain detailed atomic mechanism of β-endorphin amyloid fibrils' pH-responsive disassembly, we conduct constant pH molecular dynamics simulations to investigate the structural and dynamical properties of β-endorphin amyloid fibrils in experiencing the environmental pH changes. Our results demonstrate a clear pKa shift of the internal ionizable residue of GLU8, and this shift becomes even more pronounced when it is buried more deeply in the amyloid fibrils. The unusual pKa of GLU8 reveals that its protonation state changes from the protonated state in the acidic secretory granule to the deprotonated state in the neutral pH conditions in the blood, where the deprotonation of GLU8 leads to unfavorable interactions within the hydrophobic core of the amyloid and subsequent fibril disassembly. The different pKa shifts of GLU8 relative to its positions in the amyloid fibril indicate that the β-endorphin amyloid fibril disassembly is a stepwise process, accounting for the experimental observation that the disassembly always initiates from the outermost layer. This study reveals the critical role of the protonation state of GLU8 in amyloid fibrils' pH-responsive disassembly, and provides clear insights for understanding the structural transitions of amyloids in hormone secretion. This study also provides theoretical basis for designing pH-sensitive biological tools for specific use with precise positioning of ionizable residues into the hydrophobic interior of proteins.

GPU-Accelerated All-Atom Particle-Mesh Ewald Continuous Constant pH Molecular Dynamics in Amber.

Constant pH molecular dynamics (MD) simulations sample protonation states on the fly according to the conformational environment and user specified pH conditions; however, the current accuracy is limited due to the use of implicit-solvent models or a hybrid solvent scheme. Here, we report the first GPU-accelerated implementation, parametrization, and validation of the all-atom continuous constant pH MD (CpHMD) method with particle-mesh Ewald (PME) electrostatics in the Amber22 pmemd.cuda engine. The titration parameters for Asp, Glu, His, Cys, and Lys were derived for the CHARMM c22 and Amber ff14sb and ff19sb force fields. We then evaluated the PME-CpHMD method using the asynchronous pH replica-exchange titration simulations with the c22 force field for six benchmark proteins, including BBL, hen egg white lysozyme (HEWL), staphylococcal nuclease (SNase), thioredoxin, ribonuclease A (RNaseA), and human muscle creatine kinase (HMCK). The root-mean-square deviation from the experimental pKa's of Asp, Glu, His, and Cys is 0.76 pH units, and the Pearson's correlation coefficient for the pKa shifts with respect to model values is 0.80. We demonstrated that a finite-size correction or much enlarged simulation box size can remove a systematic error of the calculated pKa's and improve agreement with experiment. Importantly, the simulations captured the relevant biology in several challenging cases, e.g., the titration order of the catalytic dyad Glu35/Asp52 in HEWL and the coupled residues Asp19/Asp21 in SNase, the large pKa upshift of the deeply buried catalytic Asp26 in thioredoxin, and the large pKa downshift of the deeply buried catalytic Cys283 in HMCK. We anticipate that PME-CpHMD will offer proper pH control to improve the accuracies of MD simulations and enable mechanistic studies of proton-coupled dynamical processes that are ubiquitous in biology but remain poorly understood due to the lack of experimental tools and limitation of current MD simulations.

PH-dependent structural diversity of profilin allergens determines thermal stability.

The family of profilin allergens is a common class of proteins found in plants, viruses and various eukaryotes including mammals. Profilins are characterized by an evolutionary conserved structural fold, which is responsible for their cross-reactive nature of Immunoglobulin E (IgE) antibodies. Despite their high overall structural similarity, they exhibit substantial differences in their biophysical properties, such as thermal and pH stability. To understand the origin of these functional differences of Amb a 8, Art v 4 and Bet v 2, we performed constant pH molecular dynamics simulation in combination with Gaussian accelerated MD simulations. Depending on the respective protonation at different pH levels, we find distinct differences in conformational flexibility, which are consistent with experimentally determined melting temperatures. These variations in flexibility are accompanied by ensemble shifts in the conformational landscape and quantified and localized by residue-wise B-factors and dihedral entropies. These findings strengthen the link between flexibility of profilin allergens and their thermal stability. Thus, our results clearly show the importance of considering protonation dependent conformational ensembles in solution to elucidate biophysical differences between these structurally similar allergens.

Extending the Stochastic Titration CpHMD to CHARMM36m.

The impact of pH on proteins is significant but often neglected in molecular dynamics simulations. Constant-pH Molecular Dynamics (CpHMD) is the state-of-the-art methodology to deal with these effects. However, it still lacks widespread adoption by the scientific community. The stochastic titration CpHMD is one of such methods that, until now, only supported the GROMOS force field family. Here, we extend this method's implementation to include the CHARMM36m force field available in the GROMACS software package. We test this new implementation with a diverse group of proteins, namely, lysozyme, Staphylococcal nuclease, and human and E. coli thioredoxins. All proteins were conformationally stable in the simulations, even at extreme pH values. The RMSE values (pKa prediction vs experimental) obtained were very encouraging, in particular for lysozyme and human thioredoxin. We have also identified a few residues that challenged the CpHMD simulations, highlighting scenarios where the method still needs improvement independently of the force field. The CHARMM36m all-atom implementation was more computationally efficient when compared with the GROMOS 54A7, taking advantage of a shorter nonbonded interaction cutoff and a less frequent neighboring list update. The new extension will allow the study of pH effects in many systems for which this force field is particularly suited, i.e., proteins, membrane proteins, lipid bilayers, and nucleic acids.

Scalable Constant pH Molecular Dynamics in GROMACS.

Molecular dynamics (MD) computer simulations are used routinely to compute atomistic trajectories of complex systems. Systems are simulated in various ensembles, depending on the experimental conditions one aims to mimic. While constant energy, temperature, volume, and pressure are rather straightforward to model, pH, which is an equally important parameter in experiments, is more difficult to account for in simulations. Although a constant pH algorithm based on the λ-dynamics approach by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414–2423] was implemented in a fork of the GROMACS molecular dynamics program, uptake has been rather limited, presumably due to the poor scaling of that code with respect to the number of titratable sites. To overcome this limitation, we implemented an alternative scheme for interpolating the Hamiltonians of the protonation states that makes the constant pH molecular dynamics simulations almost as fast as a normal MD simulation with GROMACS. In addition, we implemented a simpler scheme, called multisite representation, for modeling side chains with multiple titratable sites, such as imidazole rings. This scheme, which is based on constraining the sum of the λ-coordinates, not only reduces the complexity associated with parametrizing the intramolecular interactions between the sites but also is easily extendable to other molecules with multiple titratable sites. With the combination of a more efficient interpolation scheme and multisite representation of titratable groups, we anticipate a rapid uptake of constant pH molecular dynamics simulations within the GROMACS user community.

Best Practices in Constant pH MD Simulations: Accuracy and Sampling.

Various approaches have been proposed to include the effect of pH in molecular dynamics (MD) simulations. Among these, the λ-dynamics approach proposed by Brooks and co-workers [Kong, X.; Brooks III, C. L. J. Chem. Phys.1996, 105, 2414−2423] can be performed with little computational overhead and hfor each typeence be used to routinely perform MD simulations at microsecond time scales, as shown in the accompanying paper [Aho, N. et al. J. Chem. Theory Comput.2022, DOI: 10.1021/acs.jctc.2c00516]. At such time scales, however, the accuracy of the molecular mechanics force field and the parametrization becomes critical. Here, we address these issues and provide the community with guidelines on how to set up and perform long time scale constant pH MD simulations. We found that barriers associated with the torsions of side chains in the CHARMM36m force field are too high for reaching convergence in constant pH MD simulations on microsecond time scales. To avoid the high computational cost of extending the sampling, we propose small modifications to the force field to selectively reduce the torsional barriers. We demonstrate that with such modifications we obtain converged distributions of both protonation and torsional degrees of freedom and hence consistent pKa estimates, while the sampling of the overall configurational space accessible to proteins is unaffected as compared to normal MD simulations. We also show that the results of constant pH MD depend on the accuracy of the correction potentials. While these potentials are typically obtained by fitting a low-order polynomial to calculated free energy profiles, we find that higher order fits are essential to provide accurate and consistent results. By resolving problems in accuracy and sampling, the work described in this and the accompanying paper paves the way to the widespread application of constant pH MD beyond pKa prediction.

Structure and Dynamics of the Isozymes II and IX of Human Carbonic Anhydrase.

Human carbonic anhydrases (HCAs) are responsible for the pH control and sensing in our body and constitute key components in the central pH paradigm connected to cancer therapeutics. However, little or no molecular level studies are available on the pH-dependent stability and functional dynamics of the known isozymes of HCA. The main objective of this Article is to report the first bench-marking study on the structure and dynamics of the two most efficient isozymes, HCA II and IX, at neutral pH using classical molecular dynamics (MD) and constant pH MD (CpHMD) simulations combined with umbrella sampling, transition path sampling, and Markov state models. Starting from the known crystal structures of HCA II and the monomeric catalytic domain of HCA IX (labeled as HCA IX-c), we have generated classical MD and CpHMD trajectories (of length 1 μs each). In all cases, the overall stability, RMSD, and secondary structure segments of the two isozymes are found to be quite similar. Functionally important dynamics of these two enzymes have been probed in terms of active site hydration, coordination of the Zn(II) ion to a transient excess water, and the formation of putative proton transfer paths. The most important difference between the two isozymes is observed for the side-chain fluctuations of His-64 that is expected to shuttle an excess proton out of the active site as a part of the rate-determining intramolecular proton transfer reaction. The relative stability of the stable inward and outward conformations of the His-64 side-chain and the underlying free energy surfaces are found to depend strongly on the isozyme. In each case, a lower free energy barrier is detected between predominantly inward conformations from predominantly outward ones when simulated under constant pH conditions. The kinetic rate constants of interconversion between different free energy basins are found to span 107–108 s–1 with faster conformational transitions predicted at constant pH condition. The estimated rate constants and free energies are expected to validate if the fluctuation of the His-64 side-chain in HCA IX may have a significance similar to that known in the multistep catalytic cycle of HCA II.

A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pKa Predictions in Proteins.

Existing computational methods for estimating pKa values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pKa shifts to train deep learning models, which are shown to rival the physics-based predictors. These neural networks managed to infer the electrostatic contributions of different chemical groups and learned the importance of solvent exposure and close interactions, including hydrogen bonds. Although trained only using theoretical data, our pKAI+ model displayed the best accuracy in a test set of ∼750 experimental values. Inference times allow speedups of more than 1000× compared to physics-based methods. By combining speed, accuracy, and a reasonable understanding of the underlying physics, our models provide a game-changing solution for fast estimations of macroscopic pKa values from ensembles of microscopic values as well as for many downstream applications such as molecular docking and constant-pH molecular dynamics simulations.

Changes in Protonation States of In-Pathway Residues can Alter Ligand Binding Pathways Obtained From Spontaneous Binding Molecular Dynamics Simulations.

Protein-ligand binding processes often involve changes in protonation states that can be key to recognize and orient the ligand in the binding site. The pathways through which (bio)molecules interplay to attain productively bound complexes are intricate and involve a series of interconnected intermediate and transition states. Molecular dynamics (MD) simulations and enhanced sampling techniques are commonly used to characterize the spontaneous binding of a ligand to its receptor. However, the effect of protonation state changes of in-pathway residues in spontaneous binding MD simulations remained mostly unexplored. Here, we used molecular dynamics simulations to reconstruct the trypsin-benzamidine binding pathway considering different protonation states of His57. This residue is part of the trypsin catalytic triad and is located more than 10 Å away from Asp189, which is responsible for benzamidine binding in the trypsin S1 pocket. Our MD simulations showed that the binding pathways that benzamidine follow to target the S1 binding site are critically dependent on the His57 protonation state. Binding of benzamidine frequently occurs when His57 is protonated in the delta nitrogen while the binding process is significantly less frequent when His57 is positively charged. Constant-pH MD simulations retrieved the equilibrium populations of His57 protonation states at trypsin active pH offering a clearer picture of benzamidine recognition and binding. These results indicate that properly accounting for protonation states of distal residues can be important in spontaneous binding MD simulations.

Ionization of D571 Is Coupled with SARS-CoV-2 Spike Up/Down Equilibrium Revealing the pH-Dependent Allosteric Mechanism of Receptor-Binding Domains.

As a type I viral fusion protein, SARS-CoV-2 spike undergoes a pH-dependent switch to mediate the endosomal positioning of the receptor-binding domain to facilitate viral entry into cells and immune evasion. Gaps in our knowledge concerning the conformational transitions and key intramolecular motivations have hampered the development of effective therapeutics against the virus. To clarify the pH-sensitive elements on spike-gating the receptor-binding domain (RBD) opening and understand the details of the RBD opening transition, we performed microsecond-time scale constant pH molecular dynamics simulations in this study. We identified the deeply buried D571 with a clear pKa shift, suggesting a potential pH sensor, and showed the coupling of ionization of D571 with spike RBD-up/down equilibrium. We also computed the free-energy landscape for RBD opening and identified the crucial interactions that influence RBD dynamics. The atomic-level characterization of the pH-dependent spike activation mechanism provided herein offers new insights for a better understanding of the fundamental mechanisms of SARS-CoV-2 viral entry and infection and hence supports the discovery of novel therapeutics for COVID-19.

Novel US-CpHMD Protocol to Study the Protonation-Dependent Mechanism of the ATP/ADP Carrier.

We have designed a protocol combining constant-pH molecular dynamics (CpHMD) simulations with an umbrella sampling (US) scheme (US-CpHMD) to study the mechanism of ADP/ATP transport (import and export) by their inner mitochondrial membrane carrier protein [ADP/ATP carrier (AAC)]. The US scheme helped overcome the limitations of sampling the slow kinetics involved in these substrates' transport, while CpHMD simulations provided an unprecedented realism by correctly capturing the associated protonation changes. The import of anionic substrates along the mitochondrial membrane has a strong energetic disadvantage due to a smaller substrate concentration and an unfavorable membrane potential. These limitations may have created an evolutionary pressure on AAC to develop specific features benefiting the import of ADP. In our work, the potential of mean force profiles showed a clear selectivity in the import of ADP compared to ATP, while in the export, no selectivity was observed. We also observed that AAC sequestered both substrates at longer distances in the import compared to the export process. Furthermore, only in the import process do we observe transient protonation of both substrates when going through the AAC cavity, which is an important advantage to counteract the unfavorable mitochondrial membrane potential. Finally, we observed a substrate-induced disruption of the matrix salt-bridge network, which can promote the conformational transition (from the C- to M-state) required to complete the import process. This work unraveled several important structural features where the complex electrostatic interactions were pivotal to interpreting the protein function and illustrated the potential of applying the US-CpHMD protocol to other transport processes involving membrane proteins.

Approach to Study pH-Dependent Protein Association Using Constant-pH Molecular Dynamics: Application to the Dimerization of β-Lactoglobulin.

Protein-protein association is often mediated by electrostatic interactions and modulated by pH. However, experimental and computational studies have often overlooked the effect of association on the protonation state of the protein. In this work, we present a methodological approach based on constant-pH molecular dynamics (MD), which aims to provide a detailed description of a pH-dependent protein-protein association, and apply it to the dimerization of β-lactoglobulin (BLG). A selection of analyses is performed using the data generated by constant-pH MD simulations of monomeric and dimeric forms of bovine BLG, in the pH range 3-8. First, we estimate free energies of dimerization using a computationally inexpensive approach based on the Wyman-Tanford linkage theory, calculated in a new way through the use of thermodynamically based splines. The individual free energy contribution of each titratable site is also calculated, allowing for identification of relevant residues. Second, the correlations between the proton occupancies of pairs of sites are calculated (using the Pearson coefficient), and extensive networks of correlated sites are observed at acidic pH values, sometimes involving distant pairs. In general, strongly correlated sites are also slow proton exchangers and contribute significantly to the pH-dependency of the dimerization free energy. Third, we use ionic density as a fingerprint of protein charge distribution and observe electrostatic complementarity between the monomer faces that form the dimer interface, more markedly at the isoionic point (where maximum dimerization occurs) than at other pH values, which might contribute to guide the association. Finally, the pH-dependent dimerization modes are inspected using PCA, among other analyses, and two states are identified: a relaxed state at pH 4-8 (with the typical alignment of the crystallographic structure) and a compact state at pH 3-4 (with a tighter association and rotated alignment). This work shows that an approach based on constant-pH MD simulations can produce rich detailed pictures of pH-dependent protein associations, as illustrated for BLG dimerization.

Constant-Ph molecular dynamics simulations of a proton-gated ion channel

Conformational cycling in many macromolecules is modulated by pH. Molecular dynamics (MD) simulations are used routinely to study a variety of structural transitions, for example between functional states of an ion channel. However, in classical MD simulations titratable residues such as aspartic and glutamic acid are assigned fixed protonation states. In reality, such charge states can change dynamically, and may play an important role in functionally relevant mechanisms. Although various MD algorithms have been previously proposed to capture dynamic protonation, their implementations can be computationally intensive or restricted in features, rendering them of limited applicability to large complex systems.

Exploring the trimerization process of a transmembrane helix with an ionizable residue by molecular dynamics simulations: a case study of transmembrane domain 5 of LMP-1

The oligomerization of membrane proteins is an important biological process that plays a critical role in the initialization of membrane protein receptor signaling. Unveiling how transmembrane segments oligomerize is critical for understanding the mechanism of membrane receptor signaling activation. Owing to the complicated membrane environment and the extraordinary dynamic properties of the ionizable residues in the transmembrane segment, it is extremely challenging to thoroughly understand the oligomerization process of the transmembrane domain. In this study, transmembrane domain 5 (TMD5) of latent membrane protein-1 from Epstein-Barr virus was used as a prototype model to investigate the trimerization process of the transmembrane segment with ionizable residues. The trimerization process of TMD5 was rebuilt and investigated via conventional molecular dynamics simulations and constant-pH molecular dynamics simulations. When TMD5s approached each other, the tilting angles of the TMD5 monomer decreased. TMD5s formed stable trimers until two interacting sites (D150s and Q139s) along each transmembrane helix were created to lock the TMD5s. The pKa values of D150 shifted toward neutral states in the membrane environment. When TMD5s were monomers, the pKa shift of D150 was mainly influenced by its microenvironment in the lipid bilayer. When TMD5s were moving close to each other, protein-protein interactions became the main contributing factor for the pKa shift of D150s. Overall, this work elucidates the behavior of the TMD5 helix and the pKa shift of ionizable residue D150 in the process of TMD5 oligomerization. This study may provide insight into the development of agents for targeting the oligomerization of membrane proteins.

Protein pK a Prediction with Machine Learning.

Protein pKa prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pKa predictor DeepKa, which is trained and validated with the pKa values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (HuangY.J. Chem. Inf. Model.2018, 58, 1372−138329949356). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pKa predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pKa predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein–ligand binding affinity prediction.

Binding interaction of a potential statin with β-lactoglobulin: An in silico approach

This article reports the interaction between a synthetic statin, fluvastatin with bovine milk protein, β-lactoglobulin (BLG) through docking, constant pH molecular dynamics simulation (cpHMD) and binding free energy calculations. Docking provides the best fitted binding mode of the ligand with the receptor. We have carried out MD simulations of the protein and protein-ligand complex at two different pH viz. 7.0 and 1.5. We have found that the protein shows more compact behavior at pH 1.5 and this behavior is more prominent on complexation with the ligand. In support of this we have utilized the properties viz. root mean square deviations, root mean square fluctuations, radius of gyration, protein-ligand hydrogen bond and binding free energy calculations. Calculation of radius of gyration shows that the value decreases from 14.51 Å to 14.03 Å on complexation at pH 1.5. Calculations of hydrogen bonds at pH 1.5 confirms that hydrogen bonding interactions of the binding residues of the protein with the ligand provides stability to the complex. We have used molecular mechanics-generalized Born surface area (MMGBSA) method to estimate binding free energies of the protein with the ligand. MMGBSA calculations suggest that there is favorable binding interactions between the protein and the ligand with major contributions from Van der Waals interactions. We have found that the net average binding free energy is −29.394 kcal/mol that reveals a favorable binding interactions of BLG with the ligand. This study suggests that in spite of the acidic environment in the stomach BLG can act as a carrier for the acid-sensitive drug molecules such as fluvastatin because of its highly stable conformational behavior in the acidic pH.

Intramolecular interactions play key role in stabilization of pHLIP at acidic conditions.

The pH-Low Insertion Peptide (pHLIP) is a membrane-active peptide that spontaneously folds into a transmembrane α-helix upon acidification. This activity enables pHLIP to potentially act as a vector for drugs related to diseases characterized by acidosis such as cancer or heart ischemia. Presently, due to aggregation-based effects, formulations of pHLIP are only viable at near-μM concentrations. In addition, since most of pHLIP's measurable qualities involve a membrane, probing the details of pHLIP in the interstitial region is difficult. In attempts to shed light on these issues, we performed constant pH molecular dynamics simulations on pHLIP as well as P20G, a variant with increased helicity, in solution at 0 and 150 mM NaCl over a broad range of pHs. In general, the addition of ions reduced the effective pKa of the acidic residues in pHLIP. P20G exhibits a higher helicity than pHLIP in general and is more compact than pHLIP at pH values under 4. In terms of charge effects, sodium cations localized predominantly to the C-terminus of the peptide with a high density of acidic residues. Additionally, the salt bridge between R11 and D14 is by far the most favored and particularly so with pHLIP at 150 mM NaCl. We expect that this approach will be a valuable tool to screen variants of pHLIP for favorable properties in solution, an aspect of pHLIP design that to this point has largely been neglected.

Unveiling the molecular mechanism of pH-dependent interactions of human serum albumin with chemotherapeutic agent doxorubicin: A combined spectroscopic and constant-pH molecular dynamics study

Doxorubicin (DOX) is a potent and clinically approved chemotherapeutic agent for cancers. Although human serum albumin (HSA) has been frequently used as a promising drug vehicle for DOX to reduce its doses and toxicities, the pH-dependent binding mechanism of HSA with DOX is still poorly understood. This hampers the rational design of smart HSA-based cancer microenvironment-responsive DOX delivery systems for clinical applications. Here, the molecular interactions of DOX with HSA at various pH levels were systematically investigated using spectroscopic and in silico techniques. Our experiments showed that the binding of DOX to HSA was governed by static quenching mechanism and highly affected by environmental pH. The change of pH altered the conformational states of HSA, which may regulate the binding of DOX onto HSA. In agreement with experimental observations, 1000 ns-long constant-pH molecular dynamics simulations elucidated the structural basis of DOX bound to HSA at different pH. The computational results revealed that the favored binding of DOX at slight alkaline pH may be attributed to the intact structure of HSA and increased intermolecular hydrogen bonding. In contrast, at acidic conditions, the extension of HSA global conformation and drop of local structural identity reduced the number of intermolecular hydrogen bonds. Our study provided novel structural insights into the molecular mechanism of pH-dependent interactions of HSA with DOX, which may be helpful for the design of novel HSA-based microenvironment-responsive drug delivery systems for anticancer therapeutic applications.

Charge Interactions in a Highly Charge-Depleted Protein.

Electrostatic forces are important for protein folding and are favored targets of protein engineering. However, interactions between charged residues are difficult to study because of the complex network of interactions found in most proteins. We have designed a purposely simple system to investigate this problem by systematically introducing individual and pairs of charged and titratable residues in a protein otherwise free of such residues. We used constant pH molecular dynamics simulations, NMR spectroscopy, and thermodynamic double mutant cycles to probe the structure and energetics of the interaction between the charged residues. We found that the partial burial of surface charges contributes to a shift in pKa value, causing an aspartate to titrate in the neutral pH range. Additionally, the interaction between pairs of residues was found to be highly context dependent, with some pairs having no apparent preferential interaction, while other pairs would engage in coupled titration forming a highly stabilized salt bridge. We find good agreement between experiments and simulations and use the simulations to rationalize our observations and to provide a detailed mechanistic understanding of the electrostatic interactions.

Continuous Constant pH Molecular Dynamics Simulations of Transmembrane Proteins.

Many membrane channels, transporters, and receptors utilize a pH gradient or proton coupling to drive functionally relevant conformational transitions. Conventional molecular dynamics simulations employ fixed protonation states, thus neglecting the coupling between protonation and conformational equilibria. Here we describe the membrane-enabled hybrid-solvent continuous constant pH molecular dynamics method for capturing atomic details of proton-coupled conformational dynamics of transmembrane proteins. Example protocols from our recent application studies of proton channels and ion/substrate transporters are discussed.