Constant pH Replica Exchange Molecular Dynamics in Explicit Solvent Using Discrete Protonation States: Implementation, Testing, and Validation

Characterizing Protein Protonation Microstates Using Monte Carlo Sampling

Proteins are polyelectrolytes with acidic and basic amino acids Asp, Glu, Arg, Lys, and His, making up ≈25% of the residues. The protonation state of residues, cofactors, and ligands defines a "protonation microstate". In an ensemble of proteins some residues will be ionized and others neutral, leading to a mixture of protonation microstates rather than in a single one as is often assumed. The microstate distribution changes with pH. The protein environment also modifies residue proton affinity so microstate distributions change in different reaction intermediates or as ligands are bound. Particular protonation microstates may be required for function, while others exist simply because there are many states with similar energy. Here, the protonation microstates generated in Monte Carlo sampling in MCCE are characterized in HEW lysozyme as a function of pH and bacterial photosynthetic reaction centers (RCs) in different reaction intermediates. The lowest energy and highest probability microstates are compared. The ΔG, ΔH, and ΔS between the four protonation states of Glu35 and Asp52 in lysozyme are shown to be calculated with reasonable precision. At pH 7 the lysozyme charge ranges from 6 to 10, with 24 accepted protonation microstates, while RCs have ≈50,000. A weighted Pearson correlation analysis shows coupling between residue protonation states in RCs and how they change when the quinone in the Q_B site is reduced. Protonation microstates can be used to define input MD parameters and provide insight into the motion of protons coupled to reactions.

Prediction of protein pK a with representation learning

The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of pK a are essential to understand the basic functions of proteins. In this work, we develop a new empirical scheme for protein pK a prediction that is based on deep representation learning. It combines machine learning with atomic environment vector (AEV) and learned quantum mechanical representation from ANI-2x neural network potential (J. Chem. Theory Comput. 2020, 16, 4192). The scheme requires only the coordinate information of a protein as the input and separately estimates the pK a for all five titratable amino acid types. The accuracy of the approach was analyzed with both cross-validation and an external test set of proteins. Obtained results were compared with the widely used empirical approach PROPKA. The new empirical model provides accuracy with MAEs below 0.5 for all amino acid types. It surpasses the accuracy of PROPKA and performs significantly better than the null model. Our model is also sensitive to the local conformational changes and molecular interactions.

Explicit solvation thermodynamics in ionic solution: extending grid inhomogeneous solvation theory to solvation free energy of salt-water mixtures

Hydration thermodynamics play a fundamental role in fields ranging from the pharmaceutical industry to environmental research. Numerous methods exist to predict solvation thermodynamics of compounds ranging from small molecules to large biomolecules. Arguably the most precise methods are those based on molecular dynamics (MD) simulations in explicit solvent. One theory that has seen increased use is inhomogeneous solvation theory (IST). However, while many applications require accurate description of salt-water mixtures, no implementation of IST is currently able to estimate solvation properties involving more than one solvent species. Here, we present an extension to grid inhomogeneous solvation theory (GIST) that can take salt contributions into account. At the example of carbazole in 1 M NaCl solution, we compute the solvation energy as well as first and second order entropies. While the effect of the first order ion entropy is small, both the water-water and water-ion entropies contribute strongly. We show that the water-ion entropies are efficiently approximated using the Kirkwood superposition approximation. However, this approach cannot be applied to the water-water entropy. Furthermore, we test the quantitative validity of our method by computing salting-out coefficients and comparing them to experimental data. We find a good correlation to experimental salting-out constants, while the absolute values are overpredicted due to the approximate second order entropy. Since ions are frequently used in MD, either to neutralize the system or as a part of the investigated process, our method greatly extends the applicability of GIST. The use-cases range from biopharmaceuticals, where many assays require high salt concentrations, to environmental research, where solubility in sea water is important to model the fate of organic substances.

A Deep Dive into VDAC1 Conformational Diversity Using All-Atom Simulations Provides New Insights into the Structural Origin of the Closed States

The voltage-dependent anion channel 1 (VDAC1) is a crucial mitochondrial transporter that controls the flow of ions and respiratory metabolites entering or exiting mitochondria. As a voltage-gated channel, VDAC1 can switch between a high-conducting “open” state and a low-conducting “closed” state emerging at high transmembrane (TM) potentials. Although cell homeostasis depends on channel gating to regulate the transport of ions and metabolites, structural hallmarks characterizing the closed states remain unknown. Here, we performed microsecond accelerated molecular dynamics to highlight a vast region of VDAC1 conformational landscape accessible at typical voltages known to promote closure. Conformers exhibiting durable subconducting properties inherent to closed states were identified. In all cases, the low conductance was due to the particular positioning of an unfolded part of the N-terminus, which obstructed the channel pore. While the N-terminal tail was found to be sensitive to voltage orientation, our models suggest that stable low-conducting states of VDAC1 predominantly take place from disordered events and do not result from the displacement of a voltage sensor or a significant change in the pore. In addition, our results were consistent with conductance jumps observed experimentally and corroborated a recent study describing entropy as a key factor for VDAC gating.

Resolving Chemical Dynamics in Biological Energy Conversion: Long-Range Proton-Coupled Electron Transfer in Respiratory Complex I

Biological energy conversion is catalyzed by membrane-bound proteins that transduce chemical or light energy into energy forms that power endergonic processes in the cell. At a molecular level, these catalytic processes involve elementary electron-, proton-, charge-, and energy-transfer reactions that take place in the intricate molecular machineries of cell respiration and photosynthesis. Recent developments in structural biology, particularly cryo-electron microscopy (cryoEM), have resolved the molecular architecture of several energy transducing proteins, but detailed mechanistic principles of their charge transfer reactions still remain poorly understood and a major challenge for modern biochemical research. To this end, multiscale molecular simulations provide a powerful approach to probe mechanistic principles on a broad range of time scales (femtoseconds to milliseconds) and spatial resolutions (10¹–10⁶ atoms), although technical challenges also require balancing between the computational accuracy, cost, and approximations introduced within the model. Here we discuss how the combination of atomistic (aMD) and hybrid quantum/classical molecular dynamics (QM/MM MD) simulations with free energy (FE) sampling methods can be used to probe mechanistic principles of enzymes responsible for biological energy conversion. We present mechanistic explorations of long-range proton-coupled electron transfer (PCET) dynamics in the highly intricate respiratory chain enzyme Complex I, which functions as a redox-driven proton pump in bacterial and mitochondrial respiratory chains by catalyzing a 300 Å fully reversible PCET process. This process is initiated by a hydride (H^–) transfer between NADH and FMN, followed by long-range (>100 Å) electron transfer along a wire of 8 FeS centers leading to a quinone biding site. The reduction of the quinone to quinol initiates dissociation of the latter to a second membrane-bound binding site, and triggers proton pumping across the membrane domain of complex I, in subunits up to 200 Å away from the active site. Our simulations across different size and time scales suggest that transient charge transfer reactions lead to changes in the internal hydration state of key regions, local electric fields, and the conformation of conserved ion pairs, which in turn modulate the dynamics of functional steps along the reaction cycle. Similar functional principles, which operate on much shorter length scales, are also found in some unrelated proteins, suggesting that enzymes may employ conserved principles in the catalysis of biological energy transduction processes.

Simulation of pH-Dependent Conformational Transitions in Membrane Proteins: The CLC-ec1 Cl-/H+ Antiporter

Intracellular transport of chloride by members of the CLC transporter family involves a coupled exchange between a Cl⁻ anion and a proton (H⁺), which makes the transport function dependent on ambient pH. Transport activity peaks at pH 4.5 and stalls at neutral pH. However, a structure of the WT protein at acidic pH is not available, making it difficult to assess the global conformational rearrangements that support a pH-dependent gating mechanism. To enable modeling of the CLC-ec1 dimer at acidic pH, we have applied molecular dynamics simulations (MD) featuring a new force field modification scheme—termed an Equilibrium constant pH approach (ECpH). The ECpH method utilizes linear interpolation between the force field parameters of protonated and deprotonated states of titratable residues to achieve a representation of pH-dependence in a narrow range of physiological pH values. Simulations of the CLC-ec1 dimer at neutral and acidic pH comparing ECpH-MD to canonical MD, in which the pH-dependent protonation is represented by a binary scheme, substantiates the better agreement of the conformational changes and the final model with experimental data from NMR, cross-link and AFM studies, and reveals structural elements that support the gate-opening at pH 4.5, including the key glutamates Glu_in and Glu_ex.

Constant pH molecular dynamics of porcine circovirus 2 capsid protein reveals a mechanism for capsid assembly

Spatiotemporal regulation of viral capsid assembly ensures the selection of the viral genome for encapsidation. The porcine circovirus 2 is the smallest autonomously replicating pathogenic virus, yet how PCV2 capsid assembly is regulated to occur within the nucleus remains unknown. We report that pure PCV2 capsid proteins, in the absence of nucleic acids, require acidic conditions to assemble into empty capsids in vitro. By employing constant pH replica exchange molecular dynamics, we unveil the atomistic mechanism of pH-dependency for capsid assembly. The results show that an appropriate protonation configuration for a cluster of acidic amino acids is necessary to appropriately position the GH-loop for driving the capsid assembly. We demonstrate that assembly is prohibited at neutral pH because deprotonation of these residues results in their electrostatic repulsion, shifting the GH-loop to a position incompatible with capsid assembly. We propose that encapsulation of nucleic acids overcomes this repulsion to suitably position the GH-loop. Our findings provide the first atomic resolution mechanism of capsid assembly regulation. These findings are useful for the development of therapeutics that inhibit PCV2 self-assembly.

Unravelling Constant pH Molecular Dynamics in Oligopeptides with Explicit Solvation Model

An accurate description of the protonation state of amino acids is essential to correctly simulate the conformational space and the mechanisms of action of proteins or other biochemical systems. The pH and the electrochemical environments are decisive factors to define the effective pKa of amino acids and, therefore, the protonation state. However, they are poorly considered in Molecular Dynamics (MD) simulations. To deal with this problem, constant pH Molecular Dynamics (cpHMD) methods have been developed in recent decades, demonstrating a great ability to consider the effective pKa of amino acids within complex structures. Nonetheless, there are very few studies that assess the effect of these approaches in the conformational sampling. In a previous work of our research group, we detected strengths and weaknesses of the discrete cpHMD method implemented in AMBER when simulating capped tripeptides in implicit solvent. Now, we progressed this assessment by including explicit solvation in these peptides. To analyze more in depth the scope of the reported limitations, we also carried out simulations of oligopeptides with distinct positions of the titratable amino acids. Our study showed that the explicit solvation model does not improve the previously noted weaknesses and, furthermore, the separation of the titratable amino acids in oligopeptides can minimize them, thus providing guidelines to improve the conformational sampling in the cpHMD simulations.

Beating Heart of Cytochrome c Oxidase: The Shared Proton of Heme a3 Propionates

Cytochrome c oxidase (CcO) pumps protons from the N-side to the P-side and consumes electrons from the P-side of the mitochondrial membrane driven by energy gained from reduction of dioxygen to water. ATP synthesis uses the resulting proton gradient and electrostatic potential difference. Since the distance a proton travels through CcO is too large for a one-step transfer process, proton-loading sites (PLS) that can carry protons transiently are necessary. One specific pump-active PLS couples to the redox reaction, thus energizing the proton to move across the membrane against electric potential and proton gradient. The PLS should also prevent proton backflow. Therefore, the propionates of the two redox-active hemes in CcO were suggested as PLS candidates although, according to CcO crystal structures, none of the four propionates can be protonated on account of strong H-bonds. Here, we show that modeling the local structure around heme a₃ propionates enhances significantly their capability of carrying a proton jointly. This was not possible for the propionates of heme a. The modeled structures are stable in molecular dynamics simulations (MDS) and are energetically similar to the crystal structure. Precise electrostatic energy computations of MDS data are used to estimate the pK_A values of all titratable residues in CcO. For the modeled structures, the heme a₃ propionates have pK_A values high enough to host a proton transiently but not too high to fix the proton permanently. The change in pK_A throughout the redox reaction is sufficient to push the proton to the P-side of the membrane and to provide the protons with the necessary amount of energy for ATP synthesis.

Poor Person's pH Simulation of Membrane Proteins

pH conditions are central to the functioning of all biomolecules. However, implications of pH changes are nontrivial on a molecular scale. Though a rigorous microscopic definition of pH exists, its implementation in classical molecular dynamics (MD) simulations is cumbersome, and more so in large integral membrane systems. In this chapter, an integrative pipeline is described that combines Multi-Conformation Continuum Electrostatics (MCCE) computations with MD simulations to capture the effect of transient protonation states on the coupled conformational changes in transmembrane proteins. The core methodologies are explained, and all the software required to set up this pipeline are outlined with their key parameters. All associated analyses of structure and function are provided using two case studies, namely those of bioenergetic complexes: NADH dehydrogenase (complex I) and V_o domain of V-type ATPase. The hybrid MCCE-MD pipeline has allowed the discovery of hydrogen bond networks, ligand binding pathways, and disease-causing mutations.

pH Effects and Cooperativity among Key Titratable Residues for Escherichia coli Glycinamide Ribonucleotide Transformylase

Human glycinamide ribonucleotide transformylase (GAR Tfase) is a regulatory enzyme in the de novo purine biosynthesis pathway that has been extensively studied as an anticancer target. To some extent, inhibition of GAR Tfase selectively targets cancer cells over normal cells and inhibits purine formation and DNA replication. In this study, we investigated E. coli GAR Tfase, which shares high sequence similarity with the human GAR Tfase, and most functional residues are conserved. Herein, we aim to predict the pH-activity curve through a computational approach. We carried out pH-replica exchange molecular dynamics (pH-REMD) simulations to investigate pH-dependent functions such as structural changes, ligand binding, and catalytic activity. To compute the pH-activity curve, we identified the catalytic residues in specific protonation states, referred to as the catalytic competent protonation states (CCPS), which maintain the structure, keep ligands bound, and facilitate catalysis. Our computed population of CCPS with respect to pH matches well with the experimental pH-activity curve. To compute the microscopic pK_a values in the catalytically active conformation, we devised a thermodynamic model that considers the coupling between protonation states of CCPS residues and conformational states. These results allow us to correctly identify the general acid and base catalysts and interpret the pH-activity curve at an atomistic level.

Determinants of conductance of a bacterial voltage-gated sodium channel

Through molecular dynamics (MD) and free energy simulations in electric fields, we examine the factors influencing conductance of bacterial voltage-gated sodium channel Na_vMs. The channel utilizes four glutamic acid residues in the selectivity filter (SF). Previously, we have shown, through constant pH and free energy calculations of pKa values, that fully deprotonated, singly protonated, and doubly protonated states are all feasible at physiological pH, depending on how many ions are bound in the SF. With 173 MD simulations of 450 or 500 ns and additional free energy simulations, we determine that the conductance is highest for the deprotonated state and decreases with each additional proton bound. We also determine that the pKa value of the four glutamic residues for the transition between deprotonated and singly protonated states is close to the physiological pH and that there is a small voltage dependence. The pKa value and conductance trends are in agreement with experimental work on bacterial Na_v channels, which show a decrease in maximal conductance with lowering of pH, with pKa in the physiological range. We examine binding sites for Na⁺ in the SF, compare with previous work, and note a dependence on starting structures. We find that narrowing of the gate backbone to values lower than the crystal structure's backbone radius reduces the conductance, whereas increasing the gate radius further does not affect the conductance. Simulations with some amount of negatively charged lipids as opposed to purely neutral lipids increases the conductance, as do simulations at higher voltages.

A study on the secondary structure of the metalloregulatory protein CueR: effect of pH, metal ions and DNA

The response of CueR towards environmental changes in solution was investigated. CueR is a bacterial metal ion selective transcriptional metalloregulator protein, which controls the concentration of copper ions in the cell. Although several articles have been devoted to the discussion of the structural and functional features of this protein, CueR has not previously been extensively characterized in solution. Here, we studied the effect of change in pH, temperature, and the presence of specific or non-specific binding partners on the secondary structure of CueR with circular dichroism (CD) spectroscopy. A rather peculiar reversible pH-dependent secondary structure transformation was observed, elucidated and supplemented with pK_a estimation by PROPKA and CpHMD simulations suggesting an important role of His(76) and His(94) in this process. CD experiments revealed that the presence of DNA prevents this structural switch, suggesting that DNA locks CueR in the α-helical-rich form. In contrast to the non-cognate metal ions Hg^II, Cd^II and Zn^II, the presence of the cognate Ag^I ion affects the secondary structure of CueR, most probably by stabilizing the metal ion and DNA-binding domains of the protein.

Theoretical Studies of the Acid-Base Equilibria in a Model Active Site of the Human 20S Proteasome

The 20S proteasome is a macromolecule responsible for the chemical step in the ubiquitin-proteasome system of degrading unnecessary and unused proteins of the cell. It plays a central role both in the rapid growth of cancer cells and in viral infection cycles. Herein, we present a computational study of the acid-base equilibria in an active site of the human proteasome (caspase-like), an aspect which is often neglected despite the crucial role protons play in the catalysis. As example substrates, we take the inhibition by epoxy- and boronic acid-containing warheads. We have combined cluster quantum mechanical calculations, replica exchange molecular dynamics, and Bayesian optimization of nonbonded potential terms in the inhibitors. In relation to the latter, we propose an easily scalable approach for the reevaluation of nonbonded potentials making use of the hybrid quantum mechanics molecular mechanics dynamics information. Our results show that coupled acid-base equilibria need to be considered when modeling the inhibition mechanism. The coupling between a neighboring lysine and the reacting threonine is not affected by the presence of the studied inhibitors.

Molecular Transfer Model for pH Effects on Intrinsically Disordered Proteins: Theory and Applications

We present a theoretical method to study how changes in pH shape the heterogeneous conformational ensemble explored by intrinsically disordered proteins (IDPs). The theory is developed in the context of coarse-grained models, which enable a fast, accurate, and extensive exploration of conformational space at a given protonation state. In order to account for pH effects, we generalize the molecular transfer model (MTM), in which conformations are re-weighted using the transfer free energy, which is the free energy necessary for bringing to equilibrium in a new environment a "frozen" conformation of the system. Using the semi-grand ensemble, we derive an exact expression of the transfer free energy, which amounts to the appropriate summation over all the protonation states. Because the exact result is computationally too demanding to be useful for large polyelectrolytes or IDPs, we introduce a mean-field (MF) approximation of the transfer free energy. Using a lattice model, we compare the exact and MF results for the transfer free energy and a variety of observables associated with the model IDP. We find that the precise location of the charged groups (the sequence), and not merely the net charge, determines the structural properties. We demonstrate that some of the limitations previously noted for MF theory in the context of globular proteins are mitigated when disordered polymers are studied. The excellent agreement between the exact and MF results poises us to use the method presented here as a computational tool to study the properties of IDPs and other biological systems as a function of pH.

Not only in silico drug discovery: Molecular modeling towards in silico drug delivery formulations

The use of methods at molecular scale for the discovery of new potential active ligands, as well as previously unknown binding sites for target proteins, is now an established reality. Literature offers many successful stories of active compounds developed starting from insights obtained in silico and approved by Food and Drug Administration (FDA). One of the most famous examples is raltegravir, a HIV integrase inhibitor, which was developed after the discovery of a previously unknown transient binding area thanks to molecular dynamics simulations. Molecular simulations have the potential to also improve the design and engineering of drug delivery devices, which are still largely based on fundamental conservation equations. Although they can highlight the dominant release mechanism and quantitatively link the release rate to design parameters (size, drug loading, et cetera), their spatial resolution does not allow to fully capture how phenomena at molecular scale influence system behavior. In this scenario, the "computational microscope" offered by simulations at atomic scale can shed light on the impact of molecular interactions on crucial parameters such as release rate and the response of the drug delivery device to external stimuli, providing insights that are difficult or impossible to obtain experimentally. Moreover, the new paradigm brought by nanomedicine further underlined the importance of such computational microscope to study the interactions between nanoparticles and biological components with an unprecedented level of detail. Such knowledge is a fundamental pillar to perform device engineering and to achieve efficient and safe formulations. After a brief theoretical background, this review aims at discussing the potential of molecular simulations for the rational design of drug delivery systems.

Characterizing the Water Wire in the Gramicidin Channel Found by Monte Carlo Sampling Using Continuum Electrostatics and in Molecular Dynamics Trajectories with Conventional or Polarizable Force Fields

Water molecules play a key role in all biochemical processes. They help define the shape of proteins, and they are reactant or product in many reactions and are released as ligands are bound. They facilitate the transfer of protons through transmembrane proton channel, pump and transporter proteins. Continuum electrostatics (CE) force fields used by program Multiconformation CE (MCCE) capture electrostatic interactions in biomolecules with an implicit solvent, which captures the averaged solvent water equilibrium properties. Hybrid CE methods can use explicit water molecules within the protein surrounded by implicit solvent. These hybrid methods permit the study of explicit hydrogen bond networks within the protein and allow analysis of processes such as proton transfer reactions. Yet hybrid CE methods have not been rigorously tested. Here, we present an explicit treatment of water molecules in the Gramicidin A (gA) channel using MCCE and compare the resulting distributions of water molecules and key hydration features against those obtained with explicit solvent Molecular Dynamics (MD) simulations with the nonpolarizable CHARMM36 and polarizable Drude force fields. CHARMM36 leads to an aligned water wire in the channel characterized by a large absolute net water dipole moment; the MCCE and Drude analysis lead to a small net dipole moment as the water molecules change orientation within the channel. The correct orientation is not as yet known, so these calculations identify an open question.

How does α1Histidine102 affect the binding of modulators to α1β2γ2 GABAA receptors? molecular insights from in silico experiments

The activation of GABAA receptors by the neurotransmitter gamma-aminobutyric acid mediates the rapid inhibition response in the central nervous system of mammals. Many neurological and mental health disorders arise from alterations in the structure or function of these pentameric ion channels. GABAA receptors are targets for numerous drugs, including benzodiazepines, which bind to α1β2γ2 GABAA receptors with high affinity to a site in the extracellular domain, between subunits α1 and γ2. It has been established experimentally that the binding of these drugs depends on the presence of one particular amino acid in the α1 subunit: histidine 102. However, the specific role it plays in the intermolecular interaction has not been elucidated. In this work, we applied in silico methods to understand whether certain protonation and rotamer states of α1His102 facilitate the binding of modulators. We analysed diazepam binding, a benzodiazepine, and the antagonist flumazenil to the GABAA receptor using molecular dynamics simulations and adaptive biasing force simulations. The binding free energy follows changes in the protonation state for both ligands, and rotameric states of α1His102 were specific for the different compounds, suggesting distinct preferences for positive allosteric modulators and antagonists. Moreover, in the presence of diazepam and favoured by a neutral tautomer, we identified a water molecule that links loops A, B, and C and may be relevant to the modulation mechanism.

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 pK_a 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.

Frontiers in Multiscale Modeling of Photoreceptor Proteins

This perspective article highlights the challenges in the theoretical description of photoreceptor proteins using multiscale modeling, as discussed at the CECAM workshop in Tel Aviv, Israel. The participants have identified grand challenges and discussed the development of new tools to address them. Recent progress in understanding representative proteins such as green fluorescent protein, photoactive yellow protein, phytochrome, and rhodopsin is presented, along with methodological developments.

pH-Induced Local Unfolding of the Phl p 6 Pollen Allergen From cpH-MD

Susceptibility to endosomal degradation is a decisive contribution to a protein's immunogenicity. It is assumed that the processing kinetics of structured proteins are inherently linked to their probability of local unfolding. In this study, we quantify the impact of endosomal acidification on the conformational stability of the major timothy grass pollen allergen Phl p 6. We use state of the art sampling approaches in combination with constant pH MD techniques to profile pH-dependent local unfolding events in atomistic detail. Integrating our findings into the current view on type 1 allergic sensitization, we characterize local protein dynamics in the context of proteolytic degradation at neutral and acidic pH for the wild type protein and point mutants with varying proteolytic stability. We analyze extensive simulation data using Markov state models and retrieve highly reliable thermodynamic and kinetic information at varying pH levels. Thereby we capture the impact of endolysosomal acidification on the structure and dynamics of the Phl p 6 mutants. We find that upon protonation at lower pH values, the conformational flexibilities in key areas of the wild type protein, i.e., T-cell epitopes and early proteolytic cleavage sites, increase significantly. A decrease of the pH even leads to local unfolding in otherwise stable secondary structure elements, which is a prerequisite for proteolytic cleavage. This effect is even more pronounced in the destabilized mutant, while no unfolding was observed for the stabilized mutant. In summary, we report detailed structural models which rationalize the experimentally observed cleavage pattern during endosomal acidification.

On the Use of the Discrete Constant pH Molecular Dynamics to Describe the Conformational Space of Peptides

Solvent pH is an important property that defines the protonation state of the amino acids and, therefore, modulates the interactions and the conformational space of the biochemical systems. Generally, this thermodynamic variable is poorly considered in Molecular Dynamics (MD) simulations. Fortunately, this lack has been overcome by means of the Constant pH Molecular Dynamics (CPHMD) methods in the recent decades. Several studies have reported promising results from these approaches that include pH in simulations but focus on the prediction of the effective pKa of the amino acids. In this work, we want to shed some light on the CPHMD method and its implementation in the AMBER suitcase from a conformational point of view. To achieve this goal, we performed CPHMD and conventional MD (CMD) simulations of six protonatable amino acids in a blocked tripeptide structure to compare the conformational sampling and energy distributions of both methods. The results reveal strengths and weaknesses of the CPHMD method in the implementation of AMBER18 version. The change of the protonation state according to the chemical environment is presumably an improvement in the accuracy of the simulations. However, the simulations of the deprotonated forms are not consistent, which is related to an inaccurate assignment of the partial charges of the backbone atoms in the CPHMD residues. Therefore, we recommend the CPHMD methods of AMBER program but pointing out the need to compare structural properties with experimental data to bring reliability to the conformational sampling of the simulations.

pH-Dependent Conformational Changes Lead to a Highly Shifted pKa for a Buried Glutamic Acid Mutant of SNase

Ionizable residues are rarely present in the hydrophobic interior of proteins, but when they are, they play important roles in biological processes such as energy transduction and enzyme catalysis. Internal ionizable residues have anomalous experimental pK_a values with respect to their pK_a in bulk water. This work investigates the atomistic cause of the highly shifted pK_a of the internal Glu23 in the artificially mutated variant V23E of Staphylococcal Nuclease (SNase) using pH replica exchange molecular dynamics (pH-REMD) simulations. The pK_a of Glu23 obtained from our calculations is 6.55, which is elevated with respect to the glutamate pK_a of 4.40 in bulk water. The calculated value is close to the experimental pK_a of 7.10. Our simulations show that the highly shifted pK_a of Glu23 is the product of a pH-dependent conformational change, which has been observed experimentally and also seen in our simulations. We carry out an analysis of this pH-dependent conformational change in response to the protonation state change of Glu23. Using a four-state thermodynamic model, we estimate the two conformation-specific pK_a values of Glu23 and describe the coupling between the conformational and ionization equilibria.

Unravelling the mechanism of pH-regulation in dinoflagellate luciferase

Dinoflagellates are the dominant source of bioluminescence in coastal waters. The luminescence reaction involves the oxidation of luciferin by a luciferase enzyme, which only takes place at low pH. The pH-dependence has previously been linked to four conserved histidines. It has been suggested that their protonation might induce a conformational change in the enzyme, thereby allowing substrate access to the binding pocket. Yet, the precise mechanism of luciferase activation has remained elusive. Here, we use computational tools to predict the open structure of the luciferase in Lingulodinium polyedra and to decipher the nature of the opening mechanism. Through accelerated molecular dynamics simulations, we demonstrate that the closed-open conformational change likely takes place via a tilt of the pH-regulatory helix-loop-helix domain. Moreover, we propose that the molecular basis for the transition is electrostatic repulsion between histidine-cation pairs, which destabilizes the closed conformation at low pH. Finally, by simulating truncated mutants, we show that eliminating the C-terminus alters the shape of the active site, effectively inactivating the luciferase.

Constant-pH Brownian Dynamics Simulations of a Protein near a Charged Surface

We have developed a rigid-body Brownian dynamics algorithm that allows for simulations of a globular protein suspended in an ionic solution confined by a charged planar boundary, with an explicit treatment of pH-dependent protein protonation equilibria and their couplings to the electrostatic potential of the plane. Electrostatic interactions are described within a framework of the continuum Poisson-Boltzmann model, whereas protein-plane hydrodynamic interactions are evaluated based on analytical expressions for the position- and orientation-dependent near-wall friction tensor of a spheroid. The algorithm was applied to simulate near-surface diffusion of lysozyme in solutions having pH in the range 4-10 and ionic strengths of 10 and 150 mM. As a reference, we performed Brownian dynamics simulations in which the protein is assigned a fixed, most probable protonation state, appropriate for given solution conditions and unaffected by the presence of the charged plane, and Brownian dynamics simulations in which the protein probes possible protonation states with the pH-dependent probability, but these variations are not coupled to the electric field generated by the boundary. We show that electrostatic interactions with the negatively charged plane substantially modify probabilities of different protonation states of lysozyme and shift protonation equilibria of both acidic and basic amino acid side chains toward higher pH values. Consequently, equilibrium energy distributions, equilibrium position-orientation distributions, and functions that characterize rotational dynamics, which for a protein with multiple ionization sites, such as lysozyme, in the presence of a charged obstacle are pH-dependent, are significantly affected by the approach taken to incorporate the solution pH into simulations.

Conformational Ensembles of Antibodies Determine Their Hydrophobicity

A major challenge in the development of antibody biotherapeutics is their tendency to aggregate. One root cause for aggregation is exposure of hydrophobic surface regions to the solvent. Many current techniques predict the relative aggregation propensity of antibodies via precalculated scales for the hydrophobicity or aggregation propensity of single amino acids. However, those scales cannot describe the nonadditive effects of a residue’s surrounding on its hydrophobicity. Therefore, they are inherently limited in their ability to describe the impact of subtle differences in molecular structure on the overall hydrophobicity. Here, we introduce a physics-based approach to describe hydrophobicity in terms of the hydration free energy using grid inhomogeneous solvation theory (GIST). We apply this method to assess the effects of starting structures, conformational sampling, and protonation states on the hydrophobicity of antibodies. Our results reveal that high-quality starting structures, i.e., crystal structures, are crucial for the prediction of hydrophobicity and that conformational sampling can compensate errors introduced by the starting structure. On the other hand, sampling of protonation states only leads to good results when combined with high-quality structures, whereas it can even be detrimental otherwise. We conclude by pointing out that a single static homology model may not be adequate for predicting hydrophobicity.

Isofunctional Clustering and Conformational Analysis of the Arsenate Reductase Superfamily Reveals Nine Distinct Clusters

Arsenate reductase (ArsC) is a superfamily of enzymes that reduce arsenate. Due to active site similarities, some ArsC can function as low-molecular weight protein tyrosine phosphatases (LMW-PTPs). Broad superfamily classifications align with redox partners (Trx- or Grx-linked). To understand this superfamily's mechanistic diversity, the ArsC superfamily is classified on the basis of active site features utilizing the tools TuLIP (two-level iterative clustering process) and autoMISST (automated multilevel iterative sequence searching technique). This approach identified nine functionally relevant (perhaps isofunctional) protein groups. Five groups exhibit distinct ArsC mechanisms. Three are Grx-linked: group 4AA (classical ArsC), group 3AAA (YffB-like), and group 5BAA. Two are Trx-linked: groups 6AAAAA and 7AAAAAAAA. One is an Spx-like transcriptional regulatory group, group 5AAA. Three are potential LMW-PTP groups: groups 7BAAAA, and 7AAAABAA, which have not been previously identified, and the well-studied LMW-PTP family group 8AAA. Molecular dynamics simulations were utilized to explore functional site details. In several families, we confirm and add detail to literature-based mechanistic information. Mechanistic roles are hypothesized for conserved active site residues in several families. In three families, simulations of the unliganded structure sample specific conformational ensembles, which are proposed to represent either a more ligand-binding-competent conformation or a pathway toward a more binding-competent state; these active sites may be designed to traverse high-energy barriers to the lower-energy conformations necessary to more readily bind ligands. This more detailed biochemical understanding of ArsC and ArsC-like PTP mechanisms opens possibilities for further understanding of arsenate bioremediation and the LMW-PTP mechanism.

Titration of Adenine in a GA Mismatch with Grand Canonical Simulations

Computational prediction of the pKa of ionizable groups remains a central challenge in biomolecular modeling. Although all-atom fixed-charge force fields could be accurate to describe the interaction network within the biomolecules, proper sampling techniques are required to obtain the thermodynamic information in the (de)protonation event. Sufficient sampling requires an ensemble of structures from simulations with proper treatments of the acid-base equilibria, and the grand canonical simulation technique could be used to model the growth/annihilation of hydrogen atoms by merging Hamiltonians of different protonation states into one simulation ensemble. The electrostatic feature of nucleotide systems is especially difficult to model, and the situation becomes more challenging when the ionizable site is highly perturbed. Although there are many successful predictions obtained from the grand canonical constant pH simulations, few reports focus on highly perturbed nucleotide systems with unconventional base-pair features. In this work, with the discrete constant pH method, we investigate the titration thermodynamics of an adenine in the catalytic triad in a 35-nucleotide single-stranded RNA hairpin, featuring an unconventional GA mismatch and a substantially shifted pKa value. Validation tests are performed with two system setups, both of which provide pKa predictions in good agreement with the experimental value. A single-configuration-based technique is used to calculate the pKa for comparison. The current success indicates the predictive power of the current nucleotide modeling framework.

pH-Dependent absorption spectrum of oxyluciferin analogues in the active site of firefly luciferase

In the quest for the identification of the light emitter(s) responsible for the firefly bioluminescence, the study of oxyluciferin analogues with controlled chemical and electronic structures is of particular importance. In this article, we report the results of our experimental and computational investigation of the pH-dependent absorption spectra characterizing three analogues bound into the luciferase cavity, together with adenosine-monophosphate (AMP). While the analogue microscopic pKa values do not differ much from their reference values, it turns out that the AMP protonation state is analogue-dependent and never doubly-deprotonated. A careful analysis of the interactions evidences the main role of E344 glutamic acid, as well as the flexibility of the cavity which can accommodate any oxyluciferin analogue. The consideration of the absorption spectra suggests that the oxyluciferin enolate form has to be excluded from the list of the bioluminescence reaction products.

Computing pKa Shifts Using Traditional Molecular Dynamics: Example of Acid-Base Indicator Dyes in Organized Solutions

A compound's acidity constant (K_a) in a given medium determines its protonation state and, thus, its behavior and physicochemical properties. Therefore, it is among the key characteristics considered during the design of new compounds for the needs of advanced technology, medicine, and biological research, a notable example being pH sensors. The computational prediction of K_a for weak acids and bases in homogeneous solvents is presently rather well developed. However, it is not the case for more complex media, such as microheterogeneous solutions. The constant-pH molecular dynamics (MD) method is a notable contribution to the solution of the problem, but it is not commonly used. Here, we develop an approach for predicting K_a changes of weak small-molecule acids upon transfer from water to colloid solutions by means of traditional classical molecular dynamics. The approach is based on free energy (ΔG) computations and requires limited experiment data input during calibration. It was successfully tested on a series of pH-sensitive acid-base indicator dyes in micellar solutions of surfactants. The difficulty of finite-size effects affecting ΔG computation between states with different total charges is taken into account by evaluating relevant corrections; their impact on the results is discussed, and it is found non-negligible (0.1-0.4 pK_a units). A marked bias is found in the ΔG values of acid deprotonation, as computed from MD, which is apparently caused by force-field issues. It is hypothesized to affect the constant-pH MD and reaction ensemble MD methods as well. Consequently, for these methods, a preliminary calibration is suggested.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.

Advances in Molecular Dynamics Simulations and Enhanced Sampling Methods for the Study of Protein Systems

Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can be translated into useful statistics that can be exploited to sieve thermodynamics and kinetics crucial for the elucidation of mechanisms responsible for the modulation of biological processes such as protein-ligand binding and protein-protein association. Continuous modernization of simulation tools enables accurate prediction and characterization of the aforementioned mechanisms and these qualities are highly beneficial for the expedition of drug development when effectively applied to structure-based drug design (SBDD). In this review, current all-atom MD simulation methods, with focus on enhanced sampling techniques, utilized to examine protein structure, dynamics, and functions are discussed. This review will pivot around computer calculations of protein-ligand and protein-protein systems with applications to SBDD. In addition, we will also be highlighting limitations faced by current simulation tools as well as the improvements that have been made to ameliorate their efficiency.

Scalable molecular dynamics on CPU and GPU architectures with NAMD

NAMDis a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity clusters commonly found in academic environments. It is written in C++ and leans on Charm++ parallel objects for optimal performance on low-latency architectures. NAMD is a versatile, multipurpose code that gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force fields. Here, we review the main features of NAMD that allow both equilibrium and enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe the underlying concepts utilized by NAMD and their implementation, most notably for handling long-range electrostatics; controlling the temperature, pressure, and pH; applying external potentials on tailored grids; leveraging massively parallel resources in multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical descriptions. We detail the variety of options offered by NAMD for enhanced-sampling simulations aimed at determining free-energy differences of either alchemical or geometrical transformations and outline their applicability to specific problems. Last, we discuss the roadmap for the development of NAMD and our current efforts toward achieving optimal performance on GPU-based architectures, for pushing back the limitations that have prevented biologically realistic billion-atom objects to be fruitfully simulated, and for making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD is distributed free of charge with its source code at www.ks.uiuc.edu.

Effect of pH and Molecular Length on the Structure and Dynamics of Linear and Short-Chain Branched Poly(ethylene imine) in Dilute Solution: Scaling Laws from Detailed Molecular Dynamics Simulations

Atomistic molecular dynamics (MD) simulations are carried out to examine the effect of molecular weight M_w (= 0.6, 0.86, 1.12, and 2.15 kDa) and pH (or equivalently, degree of ionization, α⁺ = 0, 50, and 100%) on the structure, state of hydration, and dynamics of linear and branched poly(ethylene imine) (PEI) chains in infinitely dilute salt-free aqueous solutions. It is found that the degree of ionization is the key factor determining the type of molecular conformation adopted by PEI, regardless of molecular architecture and chain length, resulting in a stable trans conformation for fully ionized solutions and in a stable gauche⁺/gauche^- state for neutral or alternate ionized ones; in the latter case, a strong electrolyte behavior is verified for both linear and branched PEI. Linear PEI is observed to be significantly stiffer than branched PEI of the same molecular weight at 100% degree of ionization, but the effect subsides as the degree of ionization decreases. Also, linear PEI diffuses markedly slower than branched PEI of the same M_w. From the MD results, scaling exponents are deduced and reported for the conformation, solvent-accessible surface area, and dynamics of the two different PEI structures with M_w.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R²) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

On the Catalytic Activity of the Engineered Coiled-Coil Heptamer Mimicking the Hydrolase Enzymes: Insights from a Computational Study

Recently major advances were gained on the designed proteins aimed to generate biomolecular mimics of proteases. Although such enzyme-like catalysts must still suffer refinements for improving the catalytic activity, at the moment, they represent a good example of artificial enzymes to be tested in different fields. Herein, a de novo designed homo-heptameric peptide assembly (CC-Hept) where the esterase activity towards p-nitro-phenylacetate was obtained for introduction of the catalytic triad (Cys-His-Glu) into the hydrophobic matrix, is the object of the present combined molecular dynamics and quantum mechanics/molecular mechanics investigation. Constant pH Molecular Dynamics simulations on the apoform of CC-Hept suggested that the Cys residues are present in the protonated form. Molecular dynamics (MD) simulations of the enzyme-substrate complex evidenced the attitude of the enzyme-like system to retain water molecules, necessary in the hydrolytic reaction, in correspondence of the active site, represented by the Cys-His-Glu triad on each of the seven chains, without significant structural perturbations. A detailed reaction mechanism of esterase activity of CC-Hept-Cys-His-Glu was investigated on the basis of the quantum mechanics/molecular mechanics calculations employing a large quantum mechanical (QM) region of the active site. The proposed mechanism is consistent with available esterases kinetics and structural data. The roles of the active site residues were also evaluated. The deacylation phase emerged as the rate-determining step, in agreement with esterase activity of other natural proteases.

Predicting Reactive Cysteines with Implicit-Solvent-Based Continuous Constant pH Molecular Dynamics in Amber

Cysteines existing in the deprotonated thiolate form or having a tendency to become deprotonated are important players in enzymatic and cellular redox functions and frequently exploited in covalent drug design; however, most computational studies assume cysteines as protonated. Thus, developing an efficient tool that can make accurate and reliable predictions of cysteine protonation states is timely needed. We recently implemented a generalized Born (GB) based continuous constant pH molecular dynamics (CpHMD) method in Amber for protein pKa calculations on CPUs and GPUs. Here we benchmark the performance of GB-CpHMD for predictions of cysteine pKa's and reactivities using a data set of 24 proteins with both down- and upshifted cysteine pKa's. We found that 10 ns single-pH or 4 ns replica-exchange CpHMD titrations gave root-mean-square errors of 1.2-1.3 and correlation coefficients of 0.8-0.9 with respect to experiment. The accuracy of predicting thiolates or reactive cysteines at physiological pH with single-pH titrations is 86 or 81% with a precision of 100 or 90%, respectively. This performance well surpasses the traditional structure-based methods, particularly a widely used empirical pKa tool that gives an accuracy less than 50%. We discuss simulation convergence, dependence on starting structures, common determinants of the pKa downshifts and upshifts, and the origin of the discrepancies from the structure-based calculations. Our work suggests that CpHMD titrations can be performed on a desktop computer equipped with a single GPU card to predict cysteine protonation states for a variety of applications, from understanding biological functions to covalent drug design.

Hydrogen bond network analysis reveals the pathway for the proton transfer in the E-channel of T. thermophilus Complex I

Complex I, NADH-ubiquinone oxidoreductase, is the first enzyme in the mitochondrial and bacterial aerobic respiratory chain. It pumps four protons through four transiently open pathways from the high pH, negative, N-side of the membrane to the positive, P-side driven by the exergonic transfer of electrons from NADH to a quinone. Three protons transfer through subunits descended from antiporters, while the fourth, E-channel is unique. The path through the E-channel is determined by a network analysis of hydrogen bonded pathways obtained by Monte Carlo sampling of protonation states, polar hydrogen orientation and water occupancy. Input coordinates are derived from molecular dynamics trajectories comparing oxidized, reduced (dihydro) and no menaquinone-8 (MQ). A complex proton transfer path from the N- to the P-side is found consisting of six clusters of highly connected hydrogen-bonded residues. The network connectivity depends on the presence of quinone and its redox state, supporting a role for this cofactor in coupling electron and proton transfers. The N-side is more organized with MQ-bound complex I facilitating proton entry, while the P-side is more connected in the apo-protein, facilitating proton exit. Subunit Nqo8 forms the core of the E channel; Nqo4 provides the N-side entry, Nqo7 and then Nqo10 join the pathway in the middle, while Nqo11 contributes to the P-side exit.

Fully Atomistic Multiscale Approach for pKa Prediction

The ionization state of titratable amino acids strongly affects proteins structure and functioning in a large number of biological processes. It is therefore essential to be able to characterize the pK_a of ionizable groups inside proteins and to understand its microscopic determinants in order to gain insights into many functional properties of proteins. A big effort has been devoted to the development of theoretical approaches for the prediction of deprotonation free energies, yet the accurate theoretical/computational calculation of pK_a values is recognized as a current challenge. A methodology based on a hybrid quantum/classical approach is here proposed for the computation of deprotonation free energies. The method is applied to calculate the pK_a of formic acid, methylammonium, and methanethiol, providing results in good agreement with the corresponding experimental estimates. The pK_a is also calculated for aspartic acid and lysine as single residues in solution and for three aspartic/glutamic acids inside a well-characterized protein: hen egg white lysozyme. While for small molecules the method is able to deal with multiple protonation states of all titratable groups, this becomes computationally very expensive for proteins. The calculated pK_a values for the single amino acids (except for the zwitterionic aspartic acid) and inside the protein display a systematic shift with respect to the experimental values that suggests that the fine balance between hydrophobic and polar interactions might be not accurately reproduced by the usual classical force-fields, thus affecting the computation of deprotonation free energies. The calculated pK_a shifts inside the protein are in good agreement with the corresponding experimental ones (within 1 pK_a unit), well reproducing the pK_a changes due to the protein environment even in the case of large pK_a shifts.

Catalytic Site pKa Values of Aspartic, Cysteine, and Serine Proteases: Constant pH MD Simulations

Enzymatic function and activity of proteases is closely controlled by the pH value. The protonation states of titratable residues in the active site react to changes in the pH value, according to their pK_a, and thereby determine the functionality of the enzyme. Knowledge of the titration behavior of these residues is crucial for the development of drugs targeting the active site residues. However, experimental pK_a data are scarce, since the systems’ size and complexity make determination of these pK_a values inherently difficult. In this study, we use single pH constant pH MD simulations as a fast and robust tool to estimate the active site pK_a values of a set of aspartic, cysteine, and serine proteases. We capture characteristic pK_a shifts of the active site residues, which dictate the experimentally determined activity profiles of the respective protease family. We find clear differences of active site pK_a values within the respective families, which closely match the experimentally determined pH preferences of the respective proteases. These shifts are caused by a distinct network of electrostatic interactions characteristic for each protease family. While we find convincing agreement with experimental data for serine and aspartic proteases, we observe clear deficiencies in the description of the titration behavior of cysteines within the constant pH MD framework and highlight opportunities for improvement. Consequently, with this work, we provide a concise set of active site pK_a values of aspartic and serine proteases, which could serve as reference for future theoretical as well as experimental studies.

Probing the Accuracy of Explicit Solvent Constant pH Molecular Dynamics Simulations for Peptides

Protonation states of titratable amino acids play a key role in many biomolecular processes. Knowledge of protonatable residue charges at a given pH is essential for a correct understanding of protein catalysis, inter- and intramolecular interactions, substrate binding, and protein dynamics for instance. However, acquiring experimental values for individual amino acid protonation states of complex systems is not straightforward; therefore, several in silico approaches have been developed to tackle this issue. In this work, we assess the accuracy of our previously developed constant pH MD approach by comparing our theoretically obtained pK_a values for titratable residues with experimental values from an equivalent NMR study. We selected a set of four pentapeptides, of adequately small size to ensure comprehensive sampling, but concurrently, due to their charge composition, posing a challenge for protonation state calculation. The comparison of the pK_a values shows good agreement of the experimental and the theoretical approach with a largest difference of 0.25 pK_a units. Further, the corresponding titration curves are in fair agreement, although the shift of the Hill coefficient from a value of 1 was not always reproduced in simulations. The phase space overlap in Cartesian space between trajectories generated in constant pH and standard MD simulations is fair and suggests that our constant pH MD approach reasonably well preserves the dynamics of the system, allowing dynamic protonation MD simulations without introducing structural artifacts.

Structure and mechanism of monoclonal antibody binding to the junctional epitope of Plasmodium falciparum circumsporozoite protein

Lasting protection has long been a goal for malaria vaccines. The major surface antigen on Plasmodium falciparum sporozoites, the circumsporozoite protein (PfCSP), has been an attractive target for vaccine development and most protective antibodies studied to date interact with the central NANP repeat region of PfCSP. However, it remains unclear what structural and functional characteristics correlate with better protection by one antibody over another. Binding to the junctional region between the N-terminal domain and central NANP repeats has been proposed to result in superior protection: this region initiates with the only NPDP sequence followed immediately by NANP. Here, we isolated antibodies in Kymab mice immunized with full-length recombinant PfCSP and two protective antibodies were selected for further study with reactivity against the junctional region. X-ray and EM structures of two monoclonal antibodies, mAb667 and mAb668, shed light on their differential affinity and specificity for the junctional region. Importantly, these antibodies also bind to the NANP repeat region with equal or better affinity. A comparison with an NANP-only binding antibody (mAb317) revealed roughly similar but statistically distinct levels of protection against sporozoite challenge in mouse liver burden models, suggesting that junctional antibody protection might relate to the ability to also cross-react with the NANP repeat region. Our findings indicate that additional efforts are necessary to isolate a true junctional antibody with no or much reduced affinity to the NANP region to elucidate the role of the junctional epitope in protection.

The circumsporozoite protein (CSP) of Plasmodium falciparum malaria has been the foundation for the design of transmission blocking malaria vaccines. To date, the most promising CSP-based vaccine candidate is RTS,S, which consists of the central repeating NANP amino-acid sequence and the C-terminal domain of CSP fused to hepatitis B surface antigen that assembles into virus-like particles. Potential shortcomings of RTS,S includes the lack of other potential CSP epitopes such as the junctional epitope, which is located between the N-terminal domain of CSP and the start of the NANP repeat region. Here, we elicited antibodies against full-length CSP and screened for junctional epitope binding. We then used an array of biophysical techniques to elucidate the nature of the binding and tested the level of two protective antibodies in a mouse challenge model. Although the antibodies were able to bind both junctional and NANP epitopes, the in vivo data showed distinct levels of protection between themselves and also to an NANP-only binder. Our data suggest that their protection ability may be related to the strong cross-reactivity with NANP epitopes. Since all reported junctional antibodies to date have dual-specificity, we suggest that a true junctional binder with no or very low NANP affinity, if one can be found, is essential to evaluate the contribution of the junctional epitope to protection.

Simulating ion channel activation mechanisms using swarms of trajectories

Atomic-level studies of protein activity represent a significant challenge as a result of the complexity of conformational changes occurring on wide-ranging timescales, often greatly exceeding that of even the longest simulations. A prime example is the elucidation of protein allosteric mechanisms, where localized perturbations transmit throughout a large macromolecule to generate a response signal. For example, the conversion of chemical to electrical signals during synaptic neurotransmission in the brain is achieved by specialized membrane proteins called pentameric ligand-gated ion channels. Here, the binding of a neurotransmitter results in a global conformational change to open an ion-conducting pore across the nerve cell membrane. X-ray crystallography has produced static structures of the open and closed states of the proton-gated GLIC pentameric ligand-gated ion channel protein, allowing for atomistic simulations that can uncover changes related to activation. We discuss a range of enhanced sampling approaches that could be used to explore activation mechanisms. In particular, we describe recent application of an atomistic string method, based on Roux's "swarms of trajectories" approach, to elucidate the sequence and interdependence of conformational changes during activation. We illustrate how this can be combined with transition analysis and Brownian dynamics to extract thermodynamic and kinetic information, leading to understanding of what controls ion channel function. © 2019 Wiley Periodicals, Inc.

Exploring Coupled Redox and pH Processes with a Force-Field-Based Approach: Applications to Five Different Systems

Coupled redox and pH-driven processes are at the core of many important biological mechanisms. As the distribution of protonation and redox states in a system is associated with the pH and redox potential of the solution, having efficient computational tools that can simulate under these conditions becomes very important. Such tools have the potential to provide information that complement and drive experiments. In previous publications we have presented the implementation of the constant pH and redox potential molecular dynamics (C(pH,E)MD) method in AMBER and we have shown how multidimensional replica exchange can be used to significantly enhance the convergence efficiency of our simulations. In the current work, after an improvement in our C(pH,E)MD approach that allows a given residue to be simultaneously pH- and redox-active, we have employed our methodologies to study five different systems of interest in the literature. We present results for capped tyrosine dipeptide, two maquette systems containing one pH- and redox-active tyrosine (α₃Y and peptide A), and two proteins that contain multiple heme groups (diheme cytochrome c from Rhodobacter sphaeroides and Desulfovibrio vulgaris Hildenborough cytochrome c₃). We show that our results can provide new insights into previous theoretical and experimental findings by using a fully force-field-based and GPU-accelerated approach, which allows the simulations to be executed with high computational performance.