In silico modeling of pH-optimum of protein-protein binding

Online Mixed-Bed Ion Exchange Chromatography for Native Top-Down Proteomics of Complex Mixtures

Native top-down mass spectrometry (nTDMS) allows characterization of protein structure and noncovalent interactions with simultaneous sequence mapping and proteoform characterization. The majority of nTDMS studies utilize purified recombinant proteins, with significant challenges hindering application to endogenous systems. To perform native top-down proteomics (nTDP), where endogenous proteins from complex biological systems are analyzed by nTDMS, it is essential to separate proteins under nondenaturing conditions. However, it remains difficult to achieve high resolution with MS-compatible online chromatography while preserving protein tertiary structure and noncovalent interactions. Herein, we report the use of online mixed-bed ion exchange chromatography (IEC) to enable separation of endogenous proteins from complex mixtures under nondenaturing conditions, preserving noncovalent interactions for nTDP analysis. We have successfully detected large proteins (>146 kDa) and identified endogenous metal-binding and oligomeric protein complexes in human heart tissue lysate. The use of a mixed-bed stationary phase allowed retention and elution of proteins over a wide range of isoelectric points without altering the sample or mobile phase pH. Overall, our method provides a simple online IEC-MS platform that can effectively separate proteins from complex mixtures under nondenaturing conditions and preserve higher-order structure for nTDP applications.

A novel acidic pH-dependent metacaspase governs defense-response against pathogens in tomato

The importance of metacaspases in programmed cell death and tissue differentiation is known, but their significance in disease stress response, particularly in a crop plant, remained enigmatic. We show the tomato metacaspase expression landscape undergoes differential reprogramming during biotrophic and necrotrophic modes of pathogenesis; also, the metacaspase activity dynamics correlate with the disease progression. These stresses have contrasting effects on the expression pattern of SlMC8, a Type II metacaspase, indicating that SlMC8 is crucial for stress response. In accordance, selected biotic stress-related transcription factors repress SlMC8 promoter activity. Interestingly, SlMC8 exhibits maximum proteolysis at an acidic pH range of 5-6. Molecular dynamics simulation identified the low pH-driven protonation event of Glu246 as critical to stabilize the interaction of SlMC8 with its substrate. Mutagenesis of Glu246 to charge-neutral glutamine suppressed SlMC8's proteolytic activity, corroborating the importance of the amino acid in SlMC8 activation. The glutamic acid residue is found in an equivalent position in metacaspases having acidic pH dependence. SlMC8 overexpression leads to heightened ROS levels, cell death, and tolerance to PstDC3000, and SlMC8 repression reversed the phenomena. However, the overexpression of SlMC8 increases tomato susceptibility to necrotrophic Alternaria solani. We propose that SlMC8 activation due to concurrent changes in cellular pH during infection contributes to the basal resistance of the plant by promoting cell death at the site of infection, and the low pH dependence acts as a guard against unwarranted cell death. Our study confirms the essentiality of a low pH-driven Type II metacaspase in tomato biotic stress-response regulation.

How to Personalize General Anesthesia-A Prospective Theoretical Approach to Conformational Changes of Halogenated Anesthetics in Fire Smoke Poisoning

Smoke intoxication is a central event in mass burn incidents, and toxic smoke acts at different levels of the body, blocking breathing and oxygenation. The majority of these patients require early induction of anesthesia to preserve vital functions. We studied the influence of hemoglobin (HMG) and myoglobin (MGB) blockade by hydrochloric acid (HCl) in an interaction model with gaseous anesthetics using molecular docking techniques. In the next part of the study, molecular dynamics (MD) simulations were performed on the top-scoring ligand-receptor complexes to investigate the stability of the ligand-receptor complexes and the interactions between ligands and receptors in more detail. Through docking analysis, we observed that hemoglobin creates more stable complexes with anesthetic gases than myoglobin. Intoxication with gaseous hydrochloric acid produces conformational and binding energy changes of anesthetic gases to the substrate (both the pathway and the binding site), the most significant being recorded in the case of desflurane and sevoflurane, while for halothane and isoflurane, they remain unchanged. According to our theoretical model, the selection of anesthetic agents for patients affected by fire smoke containing hydrochloric acid is critical to ensure optimal anesthetic effects. In this regard, our model suggests that halothane and isoflurane are the most suitable choices for predicting the anesthetic effects in such patients when compared to sevoflurane and desflurane.

PKAD-2: New entries and expansion of functionalities of the database of experimentally measured pKa's of proteins

Almost all biological reactions are pH dependent and understanding the origin of pH dependence requires knowledge of the pKa's of ionizable groups. Here we report a new edition of PKAD, the PKAD-2, which is a database of experimentally measured pKa's of proteins, both wild type and mutant proteins. The new additions include 117 wild type and 54 mutant pKa values, resulting in total 1742 experimentally measured pKa's. The new edition of PKAD-2 includes 8 new wild type and 12 new mutant proteins, resulting in total of 220 proteins. This new edition incorporates a visual 3D image of the highlighted residue of interest within the corresponding protein or protein complex. Hydrogen bonds were identified, counted, and implemented as a search feature. Other new search features include the number of neighboring residues <4A from the heaviest atom of the side chain of a given amino acid. Here, we present PKAD-2 with the intention to continuously incorporate novel features and current data with the goal to be used as benchmark for computational methods.

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.

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.

Computational Investigation of the pH Dependence of Stability of Melanosome Proteins: Implication for Melanosome formation and Disease

Intravesicular pH plays a crucial role in melanosome maturation and function. Melanosomal pH changes during maturation from very acidic in the early stages to neutral in late stages. Neutral pH is critical for providing optimal conditions for the rate-limiting, pH-sensitive melanin-synthesizing enzyme tyrosinase (TYR). This dramatic change in pH is thought to result from the activity of several proteins that control melanosomal pH. Here, we computationally investigated the pH-dependent stability of several melanosomal membrane proteins and compared them to the pH dependence of the stability of TYR. We confirmed that the pH optimum of TYR is neutral, and we also found that proteins that are negative regulators of melanosomal pH are predicted to function optimally at neutral pH. In contrast, positive pH regulators were predicted to have an acidic pH optimum. We propose a competitive mechanism among positive and negative regulators that results in pH equilibrium. Our findings are consistent with previous work that demonstrated a correlation between the pH optima of stability and activity, and they are consistent with the expected activity of positive and negative regulators of melanosomal pH. Furthermore, our data suggest that disease-causing variants impact the pH dependence of melanosomal proteins; this is particularly prominent for the OCA2 protein. In conclusion, melanosomal pH appears to affect the activity of multiple melanosomal proteins.

Measuring how two proteins affect each other's net charge in a crowded environment

Theory predicts that the net charge (Z) of a protein can be altered by the net charge of a neighboring protein as the two approach one another below the Debye length. This type of charge regulation suggests that a protein's charge and perhaps function might be affected by neighboring proteins without direct binding. Charge regulation during protein crowding has never been directly measured due to analytical challenges. Here, we show that lysine specific protein crosslinkers (NHS ester-Staudinger pairs) can be used to mimic crowding by linking two non-interacting proteins at a maximal distance of ~7.9 Å. The net charge of the regioisomeric dimers and preceding monomers can then be determined with lysine-acyl "protein charge ladders" and capillary electrophoresis. As a proof of concept, we covalently linked myoglobin (Z_monomer  = -0.43 ± 0.01) and α-lactalbumin (Z_monomer  = -4.63 ± 0.05). Amide hydrogen/deuterium exchange and circular dichroism spectroscopy demonstrated that crosslinking did not significantly alter the structure of either protein or result in direct binding (thus mimicking crowding). Ultimately, capillary electrophoretic analysis of the dimeric charge ladder detected a change in charge of ΔZ = -0.04 ± 0.09 upon crowding by this pair (Z_dimer  = -5.10 ± 0.07). These small values of ΔZ are not necessarily general to protein crowding (qualitatively or quantitatively) but will vary per protein size, charge, and solvent conditions.

Electrostatic features for nucleocapsid proteins of SARS-CoV and SARS-CoV-2

COVID-19 is increasingly affecting human health and global economy. Understanding the fundamental mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is highly demanded to develop treatments for COVID-19. SARS-CoV and SARS-CoV-2 share 92.06% identity in their N protein RBDs' sequences, which results in very similar structures. However, the SARS-CoV-2 is more easily to spread. Utilizing multi-scale computational approaches, this work studied the fundamental mechanisms of the nucleocapsid (N) proteins of SARS-CoV and SARS-CoV-2, including their stabilities and binding strengths with RNAs at different pH values. Electrostatic potential on the surfaces of N proteins show that both the N proteins of SARS-CoV and SARS-CoV-2 have dominantly positive potential to attract RNAs. The binding forces between SARS-CoV N protein and RNAs at different distances are similar to that of SARS-CoV-2, both in directions and magnitudes. The electric filed lines between N proteins and RNAs are also similar for both SARS-CoV and SARS-CoV-2. The folding energy and binding energy dependence on pH revealed that the best environment for N proteins to perform their functions with RNAs is the weak acidic environment.

The Electrostatic Features of Dengue Virus Capsid Assembly

Dengue virus causes serious diseases and deaths in the world. Understanding the fundamental mechanisms of dengue virus is highly demanded to develop treatments for dengue virus caused diseases. Here, we present a computational work which focused on the stability of dengue viral capsid. The interactions among E proteins on the dengue viral capsid were studied using several computational approaches. It was found that the electrostatic distribution on a single E protein monomer is highly inhomogeneous, which makes an E protein strongly binding with another E protein. This is the reason why all the E proteins form homodimers as the basic units on the whole dengue viral capsids. The pKa calculations of E proteins demonstrated that the folding energy of an E protein is low and stable in the range of pH 6–10, which is different from many other proteins that are stable at certain pH. The pH dependence of binding energy of E protein homodimer shows that the binding energy is low and independent from pH when the pH is also in the range of 6–10. This finding implies that the dengue virus can survive in a wide range of pH, which can explain why the dengue virus is so widely distributed in the world and spreads fast.

pH-Dependent Interactions of Apolipophorin-III with a Lipid Disk

Apolipophorin-III (ApoLp-III) is required for stabilization of molecular shuttles of lipid fuels in insects and is found to contribute to the insect immune reaction. Rearrangement of its five [Formula: see text]-helices enables ApoLp-III to reversibly associate with lipids. We investigate computationally the conformational changes of ApoLp-III and the pH-dependence of the binding free energy of ApoLp-III association with a lipid disk. A dominant binding mode along with several minor, low population, modes of the ApoLp-III binding to a lipid disk was identified. The pH-dependence of the binding energy for ApoLp-III with the lipid disk is predicted to be significant, with the pH-optimum at pH[Formula: see text]. The calculations suggest that there are no direct interactions between the lipid head groups and titratable residues of ApoLp-III. In the physiological pH range from 6.0 to 9.0, the binding free energy of ApoLp-III with the lipid disk decreases significantly with respect to its optimal value at pH 8.0 (at pH[Formula: see text], it is 1.02[Formula: see text]kcal/mol and at pH[Formula: see text] it is 0.23[Formula: see text]kcal/mol less favorable than at the optimal pH[Formula: see text]), indicating that the pH is an important regulator of ApoLp-III lipid disk association.

A Newton-like iterative method implemented in the DelPhi for solving the nonlinear Poisson-Boltzmann equation

DelPhi is a popular scientific program which numerically solves the Poisson-Boltzmann equation (PBE) for electrostatic potentials and energies of biomolecules immersed in water via finite difference method. It is well known for its accuracy, reliability, flexibility, and efficiency. In this work, a new edition of DelPhi that uses a novel Newton-like method to solve the nonlinear PBE, in addition to the already implemented Successive Over Relaxation (SOR) algorithm, is introduced. Our tests on various examples have shown that this new method is superior to the SOR method in terms of stability when solving the nonlinear PBE, being able to converge even for problems involving very strong nonlinearity.

DelPhi Suite: New Developments and Review of Functionalities

Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object-oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large-scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

Modeling pKas of unfolded proteins to probe structural models of unfolded state

Modeling unfolded states of proteins has implications for protein folding and stability. Since in unfolded state proteins adopt multiple conformations, any experimentally measured quantity is ensemble averaged, therefore the computed quantity should be ensemble averaged as well. Here, we investigate the possibility that one can model an unfolded state ensemble with the coil model approach, algorithm such as “flexible-meccano” [Ozenne V et al., Flexible-meccano: A tool for the generation of explicit ensemle descriptions of intrinsically disordered proteins and their associated experimental observables, Bioinformatics 28:1463–1470, 2012], developed to generate structures for intrinsically disordered proteins. We probe such a possibility by using generated structures to calculate pKas of titratable groups and compare with experimental data. It is demonstrated that even with a small number of representative structures of unfolded state, the average calculated pKas are in very good agreement with experimentally measured pKas. Also, predictions are made for titratable groups for which there is no experimental data available. This suggests that the coil model approach is suitable for generating 3D structures of unfolded state of proteins. To make the approach suitable for large-scale modeling, which requires limited number of structures, we ranked the structures according to their solvent accessible surface area (SASA). It is shown that in the majority of cases, the top structures with smallest SASA are enough to represent unfolded state.

Reproducing the Ensemble Average Polar Solvation Energy of a Protein from a Single Structure: Gaussian-Based Smooth Dielectric Function for Macromolecular Modeling

Typically, the ensemble average polar component of solvation energy (ΔG_polar^solv) of a macromolecule is computed using molecular dynamics (MD) or Monte Carlo (MC) simulations to generate conformational ensemble and then single/rigid conformation solvation energy calculation is performed on each snapshot. The primary objective of this work is to demonstrate that Poisson-Boltzmann (PB)-based approach using a Gaussian-based smooth dielectric function for macromolecular modeling previously developed by us (Li et al. J. Chem. Theory Comput. 2013, 9 (4), 2126-2136) can reproduce that ensemble average (ΔG_polar^solv) of a protein from a single structure. We show that the Gaussian-based dielectric model reproduces the ensemble average ΔG_polar^solv(⟨ΔG_polar^solv⟩) from an energy-minimized structure of a protein regardless of the minimization environment (structure minimized in vacuo, implicit or explicit waters, or crystal structure); the best case, however, is when it is paired with an in vacuo-minimized structure. In other minimization environments (implicit or explicit waters or crystal structure), the traditional two-dielectric model can still be selected with which the model produces correct solvation energies. Our observations from this work reflect how the ability to appropriately mimic the motion of residues, especially the salt bridge residues, influences a dielectric model's ability to reproduce the ensemble average value of polar solvation free energy from a single in vacuo-minimized structure.

Computational investigation of proton transfer, pKa shifts and pH-optimum of protein-DNA and protein-RNA complexes

Protein-nucleic acid interactions play a crucial role in many biological processes. This work investigates the changes of pKa values and protonation states of ionizable groups (including nucleic acid bases) that may occur at protein-nucleic acid binding. Taking advantage of the recently developed pKa calculation tool DelphiPka, we utilize the large protein-nucleic acid interaction database (NPIDB database) to model pKa shifts caused by binding. It has been found that the protein's interfacial basic residues experience favorable electrostatic interactions while the protein acidic residues undergo proton uptake to reduce the energy cost upon the binding. This is in contrast with observations made for protein-protein complexes. In terms of DNA/RNA, both base groups and phosphate groups of nucleotides are found to participate in binding. Some DNA/RNA bases undergo pKa shifts at complex formation, with the binding process tending to suppress charged states of nucleic acid bases. In addition, a weak correlation is found between the pH-optimum of protein-DNA/RNA binding free energy and the pH-optimum of protein folding free energy. Overall, the pH-dependence of protein-nucleic acid binding is not predicted to be as significant as that of protein-protein association. Proteins 2017; 85:282-295. © 2016 Wiley Periodicals, Inc.

Electrostatic component of binding energy: Interpreting predictions from poisson-boltzmann equation and modeling protocols

Macromolecular interactions are essential for understanding numerous biological processes and are typically characterized by the binding free energy. Important component of the binding free energy is the electrostatics, which is frequently modeled via the solutions of the Poisson-Boltzmann Equations (PBE). However, numerous works have shown that the electrostatic component (ΔΔGelec ) of binding free energy is very sensitive to the parameters used and modeling protocol. This prompted some researchers to question the robustness of PBE in predicting ΔΔGelec . We argue that the sensitivity of the absolute ΔΔGelec calculated with PBE using different input parameters and definitions does not indicate PBE deficiency, rather this is what should be expected. We show how the apparent sensitivity should be interpreted in terms of the underlying changes in several numerous and physical parameters. We demonstrate that PBE approach is robust within each considered force field (CHARMM-27, AMBER-94, and OPLS-AA) once the corresponding structures are energy minimized. This observation holds despite of using two different molecular surface definitions, pointing again that PBE delivers consistent results within particular force field. The fact that PBE delivered ΔΔGelec values may differ if calculated with different modeling protocols is not a deficiency of PBE, but natural results of the differences of the force field parameters and potential functions for energy minimization. In addition, while the absolute ΔΔGelec values calculated with different force field differ, their ordering remains practically the same allowing for consistent ranking despite of the force field used. © 2016 Wiley Periodicals, Inc.

DelPhiPKa web server: predicting pKa of proteins, RNAs and DNAs

A new pKa prediction web server is released, which implements DelPhi Gaussian dielectric function to calculate electrostatic potentials generated by charges of biomolecules. Topology parameters are extended to include atomic information of nucleotides of RNA and DNA, which extends the capability of pKa calculations beyond proteins. The web server allows the end-user to protonate the biomolecule at particular pH based on calculated pKa values and provides the downloadable file in PQR format. Several tests are performed to benchmark the accuracy and speed of the protocol.

IMPLEMENTATION

The web server follows a client-server architecture built on PHP and HTML and utilizes DelPhiPKa program. The computation is performed on the Palmetto supercomputer cluster and results/download links are given back to the end-user via http protocol. The web server takes advantage of MPI parallel implementation in DelPhiPKa and can run a single job on up to 24 CPUs.

AVAILABILITY AND IMPLEMENTATION

The DelPhiPKa web server is available at http://compbio.clemson.edu/pka_webserver.

Computation of pH-dependent binding free energies

Protein-ligand binding accompanies changes in the surrounding electrostatic environments of the two binding partners and may lead to changes in protonation upon binding. In cases where the complex formation results in a net transfer of protons, the binding process is pH-dependent. However, conventional free energy computations or molecular docking protocols typically employ fixed protonation states for the titratable groups in both binding partners set a priori, which are identical for the free and bound states. In this review, we draw attention to these important yet largely ignored binding-induced protonation changes in protein-ligand association by outlining physical origins and prevalence of the protonation changes upon binding. Following a summary of various theoretical methods for pKa prediction, we discuss the theoretical framework to examine the pH dependence of protein-ligand binding processes.

Conformational Dynamics and Binding Free Energies of Inhibitors of BACE-1: From the Perspective of Protonation Equilibria

BACE-1 is the β-secretase responsible for the initial amyloidogenesis in Alzheimer’s disease, catalyzing hydrolytic cleavage of substrate in a pH-sensitive manner. The catalytic mechanism of BACE-1 requires water-mediated proton transfer from aspartyl dyad to the substrate, as well as structural flexibility in the flap region. Thus, the coupling of protonation and conformational equilibria is essential to a full in silico characterization of BACE-1. In this work, we perform constant pH replica exchange molecular dynamics simulations on both apo BACE-1 and five BACE-1-inhibitor complexes to examine the effect of pH on dynamics and inhibitor binding properties of BACE-1. In our simulations, we find that solution pH controls the conformational flexibility of apo BACE-1, whereas bound inhibitors largely limit the motions of the holo enzyme at all levels of pH. The microscopic pK_a values of titratable residues in BACE-1 including its aspartyl dyad are computed and compared between apo and inhibitor-bound states. Changes in protonation between the apo and holo forms suggest a thermodynamic linkage between binding of inhibitors and protons localized at the dyad. Utilizing our recently developed computational protocol applying the binding polynomial formalism to the constant pH molecular dynamics (CpHMD) framework, we are able to obtain the pH-dependent binding free energy profiles for various BACE-1-inhibitor complexes. Our results highlight the importance of correctly addressing the binding-induced protonation changes in protein-ligand systems where binding accompanies a net proton transfer. This work comprises the first application of our CpHMD-based free energy computational method to protein-ligand complexes and illustrates the value of CpHMD as an all-purpose tool for obtaining pH-dependent dynamics and binding free energies of biological systems.

Formation of insoluble amyloid plaques in the vascular and hippocampal areas of the brain characterizes Alzheimer’s disease, a devastating neurodegenerative disorder causing dementia. Site-specific hydrolytic catalysis of β-secretase, or BACE-1, is responsible for production of oligomerative amyloid β-peptide. As the catalytic activity of BACE-1 is pH-dependent and its structural dynamics are intrinsic to the catalysis, we examine the dependence of dynamics of BACE-1 on solution pH and its implications on the catalytic mechanism of BACE-1. Also, we highlight the importance of accurate description of protonation states of the titratable groups in computer-aided drug discovery targeting BACE-1. We hope the understanding of pH dependence of the dynamics and inhibitor binding properties of BACE-1 will aid the structure-based inhibitor design efforts against Alzheimer’s disease.

Computational Tools for Interpreting Ion Channel pH-Dependence

Activity in many biological systems is mediated by pH, involving proton titratable groups with pKas in the relevant pH range. Experimental analysis of pH-dependence in proteins focusses on particular sidechains, often with mutagenesis of histidine, due to its pKa near to neutral pH. The key question for algorithms that predict pKas is whether they are sufficiently accurate to effectively narrow the search for molecular determinants of pH-dependence. Through analysis of inwardly rectifying potassium (Kir) channels and acid-sensing ion channels (ASICs), mutational effects on pH-dependence are probed, distinguishing between groups described as pH-coupled or pH-sensor. Whereas mutation can lead to a shift in transition pH between open and closed forms for either type of group, only for pH-sensor groups does mutation modulate the amplitude of the transition. It is shown that a hybrid Finite Difference Poisson-Boltzmann (FDPB) – Debye-Hückel continuum electrostatic model can filter mutation candidates, providing enrichment for key pH-coupled and pH-sensor residues in both ASICs and Kir channels, in comparison with application of FDPB alone.

Protein-protein docking with dynamic residue protonation states

Protein-protein interactions depend on a host of environmental factors. Local pH conditions influence the interactions through the protonation states of the ionizable residues that can change upon binding. In this work, we present a pH-sensitive docking approach, pHDock, that can sample side-chain protonation states of five ionizable residues (Asp, Glu, His, Tyr, Lys) on-the-fly during the docking simulation. pHDock produces successful local docking funnels in approximately half (79/161) the protein complexes, including 19 cases where standard RosettaDock fails. pHDock also performs better than the two control cases comprising docking at pH 7.0 or using fixed, predetermined protonation states. On average, the top-ranked pHDock structures have lower interface RMSDs and recover more native interface residue-residue contacts and hydrogen bonds compared to RosettaDock. Addition of backbone flexibility using a computationally-generated conformational ensemble further improves native contact and hydrogen bond recovery in the top-ranked structures. Although pHDock is designed to improve docking, it also successfully predicts a large pH-dependent binding affinity change in the Fc–FcRn complex, suggesting that it can be exploited to improve affinity predictions. The approaches in the study contribute to the goal of structural simulations of whole-cell protein-protein interactions including all the environmental factors, and they can be further expanded for pH-sensitive protein design.

Protein-protein interactions are fundamental for biological function and are strongly influenced by their local environment. Cellular pH is tightly controlled and is one of the critical environmental factors that regulates protein-protein interactions. Three-dimensional structures of the protein complexes can help us understand the mechanism of the interactions. Since experimental determination of the structures of protein-protein complexes is expensive and time-consuming, computational docking algorithms are helpful to predict the structures. However, none of the current protein-protein docking algorithms account for the critical environmental pH effects. So we developed a pH-sensitive docking algorithm that can dynamically pick the favorable protonation states of the ionizable amino-acid residues. Compared to our previous standard docking algorithm, the new algorithm improves docking accuracy and generates higher-quality predictions over a large dataset of protein-protein complexes. We also use a case study to demonstrate efficacy of the algorithm in predicting a large pH-dependent binding affinity change that cannot be captured by the other methods that neglect pH effects. In principle, the approaches in the study can be used for rational design of pH-dependent protein inhibitors or industrial enzymes that are active over a wide range of pH values.

Ion binding to biological macromolecules

Biological macromolecules carry out their functions in water and in the presence of ions. The ions can bind to the macromolecules either specifically or non-specifically, or can simply to be a part of the water phase providing physiological gradient across various membranes. This review outlines the differences between specific and non-specific ion binding in terms of the function and stability of the corresponding macromolecules. Furthermore, the experimental techniques to identify ion positions and computational methods to predict ion binding are reviewed and their advantages compared. It is indicated that specifically bound ions are relatively easier to be revealed while non-specifically associated ions are difficult to predict. In addition, the binding and the residential time of non-specifically bound ions are very much sensitive to the environmental factors in the cells, specifically to the local pH and ion concentration. Since these characteristics differ among the cellular compartments, the non-specific ion binding must be investigated with respect to the sub-cellular localization of the corresponding macromolecule.

Protocols utilizing constant pH molecular dynamics to compute pH-dependent binding free energies

In protein–ligand binding, the electrostatic environments of the two binding partners may vary significantly in bound and unbound states, which may lead to protonation changes upon binding. In cases where ligand binding results in a net uptake or release of protons, the free energy of binding is pH-dependent. Nevertheless, conventional free energy calculations and molecular docking protocols typically do not rigorously account for changes in protonation that may occur upon ligand binding. To address these shortcomings, we present a simple methodology based on Wyman’s binding polynomial formalism to account for the pH dependence of binding free energies and demonstrate its use on cucurbit[7]uril (CB[7]) host–guest systems. Using constant pH molecular dynamics and a reference binding free energy that is taken either from experiment or from thermodynamic integration computations, the pH-dependent binding free energy is determined. This computational protocol accurately captures the large pK_a shifts observed experimentally upon CB[7]:guest association and reproduces experimental binding free energies at different levels of pH. We show that incorrect assignment of fixed protonation states in free energy computations can give errors of >2 kcal/mol in these host–guest systems. Use of the methods presented here avoids such errors, thus suggesting their utility in computing proton-linked binding free energies for protein–ligand complexes.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.

Designing molecular dynamics simulations to shift populations of the conformational states of calmodulin

We elucidate the mechanisms that lead to population shifts in the conformational states of calcium-loaded calmodulin (Ca²⁺-CaM). We design extensive molecular dynamics simulations to classify the effects that are responsible for adopting occupied conformations available in the ensemble of NMR structures. Electrostatic interactions amongst the different regions of the protein and with its vicinal water are herein mediated by lowering the ionic strength or the pH. Amino acid E31, which is one of the few charged residues whose ionization state is highly sensitive to pH differences in the physiological range, proves to be distinctive in its control of population shifts. E31A mutation at low ionic strength results in a distinct change from an extended to a compact Ca²⁺-CaM conformation within tens of nanoseconds, that otherwise occur on the time scales of microseconds. The kinked linker found in this particular compact form is observed in many of the target-bound forms of Ca²⁺-CaM, increasing the binding affinity. This mutation is unique in controlling C-lobe dynamics by affecting the fluctuations between the EF-hand motif helices. We also monitor the effect of the ionic strength on the conformational multiplicity of Ca²⁺-CaM. By lowering the ionic strength, the tendency of nonspecific anions in water to accumulate near the protein surface increases, especially in the vicinity of the linker. The change in the distribution of ions in the vicinal layer of water allows N- and C- lobes to span a wide variety of relative orientations that are otherwise not observed at physiological ionic strength. E31 protonation restores the conformations associated with physiological environmental conditions even at low ionic strength.

Calmodulin (CaM) is involved in calcium signaling pathways in eukaryotic cells as an intracellular Ca²⁺ receptor. Exploiting pH differences in the cell, CaM performs a variety of functions by conveniently adopting different conformational states. We aim to reveal pH and ionic strength (IS) dependent shifts in the populations of conformational substates by modulating electrostatic interactions amongst the different regions of the protein and with its vicinal water. For this purpose, we design extensive molecular dynamics simulations to classify the effects that are responsible for adopting different conformations exhibited in the ensemble of NMR structures reported. Lowering the IS or pH, CaM experiences higher inter-lobe orientational flexibility caused by extreme change in the non-specific ion distribution in the vicinal solvent. Amongst the titratable groups sensitive to pH variations, E31 is unique in that its protonation has the same effect on the vicinal layer as increasing the IS. Furthermore, E31A mutation causes a large, reversible conformational change compatible with NMR ensemble structures populating the linker-kinked conformations. The mutation in the N lobe, at a significant distance, both modulates the electrostatic interactions in the central linker and alters the EF-hand helix orientations in the C lobe.

Molecular origins of binding affinity: seeking the Archimedean point

Connecting three dimensional structure and affinity is analogous to seeking the 'Archimedean point', a vantage point from where any observer can quantitatively perceive the subject of inquiry. Here we review current knowledge and challenges that lie ahead of us in the quest for this Archimedean point. We argue that current models are limited in reproducing measured data because molecular description of binding affinity must expand beyond the interfacial contribution and also incorporate effects stemming from conformational changes/dynamics and long-range interactions. Fortunately, explicit modeling of various kinetic schemes underlying biomolecular recognition and confined systems that reflect in vivo interactions are coming within reach. This quest will hopefully lead to an accurate biophysical interpretation of binding affinity that would allow unprecedented understanding of the molecular basis of life through unraveling the why's of interaction networks.

Molecular mechanisms of disease-causing missense mutations

Genetic variations resulting in a change of amino acid sequence can have a dramatic effect on stability, hydrogen bond network, conformational dynamics, activity and many other physiologically important properties of proteins. The substitutions of only one residue in a protein sequence, so-called missense mutations, can be related to many pathological conditions and may influence susceptibility to disease and drug treatment. The plausible effects of missense mutations range from affecting the macromolecular stability to perturbing macromolecular interactions and cellular localization. Here we review the individual cases and genome-wide studies that illustrate the association between missense mutations and diseases. In addition, we emphasize that the molecular mechanisms of effects of mutations should be revealed in order to understand the disease origin. Finally, we report the current state-of-the-art methodologies that predict the effects of mutations on protein stability, the hydrogen bond network, pH dependence, conformational dynamics and protein function.

Protonation and pK changes in protein-ligand binding

Formation of protein-ligand complexes causes various changes in both the receptor and the ligand. This review focuses on changes in pK and protonation states of ionizable groups that accompany protein-ligand binding. Physical origins of these effects are outlined, followed by a brief overview of the computational methods to predict them and the associated corrections to receptor-ligand binding affinities. Statistical prevalence, magnitude and spatial distribution of the pK and protonation state changes in protein-ligand binding are discussed in detail, based on both experimental and theoretical studies. While there is no doubt that these changes occur, they do not occur all the time; the estimated prevalence varies, both between individual complexes and by method. The changes occur not only in the immediate vicinity of the interface but also sometimes far away. When receptor-ligand binding is associated with protonation state change at particular pH, the binding becomes pH dependent: we review the interplay between sub-cellular characteristic pH and optimum pH of receptor-ligand binding. It is pointed out that there is a tendency for protonation state changes upon binding to be minimal at physiologically relevant pH for each complex (no net proton uptake/release), suggesting that native receptor-ligand interactions have evolved to reduce the energy cost associated with ionization changes. As a result, previously reported statistical prevalence of these changes - typically computed at the same pH for all complexes - may be higher than what may be expected at optimum pH specific to each complex. We also discuss whether proper account of protonation state changes appears to improve practical docking and scoring outcomes relevant to structure-based drug design. An overview of some of the existing challenges in the field is provided in conclusion.

Considering protonation as a posttranslational modification regulating protein structure and function

Posttranslational modification is an evolutionarily conserved mechanism for regulating protein activity, binding affinity, and stability. Compared with established posttranslational modifications such as phosphorylation or ubiquitination, posttranslational modification by protons within physiological pH ranges is a less recognized mechanism for regulating protein function. By changing the charge of amino acid side chains, posttranslational modification by protons can drive dynamic changes in protein conformation and function. Addition and removal of a proton is rapid and reversible and, in contrast to most other posttranslational modifications, does not require an enzyme. Signaling specificity is achieved by only a minority of sites in proteins titrating within the physiological pH range. Here, we examine the structural mechanisms and functional consequences of proton posttranslational modification of pH-sensing proteins regulating different cellular processes.

Prediction of optimal pH in hydrolytic reaction of beta-glucosidase

This is the continuation of our studies to use very basic information on enzyme to predict optimal reaction parameters in enzymatic reactions because the gap between available enzyme sequences and their available reaction parameters is widening. In this study, 23 features selected from 540 plus features of individual amino acid as well as a feature combined whole protein information were screened as independents in a 20-1 feedforward backpropagation neural network for predicting optimal pH in beta-glucosidase's hydrolytic reaction because this enzyme drew attention recently due to its role in biofuel industry. The results show that 11 features can be used as independents for the prediction, while the feature of amino acid distribution probability works better than the rest independents for the prediction. Our study paves a way to predict the optimal reaction parameters of enzymes based on the amino acid features of enzyme sequences.

Rapid calculation of protein pKa values using Rosetta

We developed a Rosetta-based Monte Carlo method to calculate the pK(a) values of protein residues that commonly exhibit variable protonation states (Asp, Glu, Lys, His, and Tyr). We tested the technique by calculating pK(a) values for 264 residues from 34 proteins. The standard Rosetta score function, which is independent of any environmental conditions, failed to capture pK(a) shifts. After incorporating a Coulomb electrostatic potential and optimizing the solvation reference energies for pK(a) calculations, we employed a method that allowed side-chain flexibility and achieved a root mean-square deviation (RMSD) of 0.83 from experimental values (0.68 after discounting 11 predictions with an error over 2 pH units). Additional degrees of side-chain conformational freedom for the proximal residues facilitated the capture of charge-charge interactions in a few cases, resulting in an overall RMSD of 0.85 pH units. The addition of backbone flexibility increased the overall RMSD to 0.93 pH units but improved relative pK(a) predictions for proximal catalytic residues. The method also captures large pK(a) shifts of lysine and some glutamate point mutations in staphylococcal nuclease. Thus, a simple and fast method based on the Rosetta score function and limited conformational sampling produces pK(a) values that will be useful when rapid estimation is essential, such as in docking, design, and folding.

In silico investigation of pH-dependence of prolactin and human growth hormone binding to human prolactin receptor

Experimental data shows that the binding of human prolactin (hPRL) to human prolactin receptor (hPRLr-ECD) is strongly pH-dependent, while the binding of the same receptor to human growth hormone (hGH) is pH-independent. Here we carry in silico analysis of the molecular effects causing such a difference and reveal the role of individual amino acids. It is shown that the computational modeling correctly predicts experimentally determined pKa's of histidine residues in an unbound state in the majority of the cases and the pH-dependence of the binding free energy. Structural analysis carried in conjunction with calculated pH-dependence of the binding revealed that the main reason for pH-dependence of the binding of hPRL-hPRLr-ECD is a number of salt- bridges across the interface of the complex, while no salt-bridges are formed in the hGH-hPRlr-ECD. Specifically, most of the salt-bridges involve histidine residues and this is the reason for the pH-dependence across a physiological range of pH. The analysis not only revealed the molecular mechanism of the pH-dependence of the hPRL-hPRLr-ECD, but also provided critical insight into the underlying physic-chemical mechanism.

DelPhi Web Server: A comprehensive online suite for electrostatic calculations of biological macromolecules and their complexes

Here we report a web server, the DelPhi web server, which utilizes DelPhi program to calculate electrostatic energies and the corresponding electrostatic potential and ionic distributions, and dielectric map. The server provides extra services to fix structural defects, as missing atoms in the structural file and allows for generation of missing hydrogen atoms. The hydrogen placement and the corresponding DelPhi calculations can be done with user selected force field parameters being either Charmm22, Amber98 or OPLS. Upon completion of the calculations, the user is given option to download fixed and protonated structural file, together with the parameter and Delphi output files for further analysis. Utilizing Jmol viewer, the user can see the corresponding structural file, to manipulate it and to change the presentation. In addition, if the potential map is requested to be calculated, the potential can be mapped onto the molecule surface. The DelPhi web server is available from http://compbio.clemson.edu/delphi_webserver.

Biomolecular electrostatics and solvation: a computational perspective

An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

DelPhi: a comprehensive suite for DelPhi software and associated resources

Background

Accurate modeling of electrostatic potential and corresponding energies becomes increasingly important for understanding properties of biological macromolecules and their complexes. However, this is not an easy task due to the irregular shape of biological entities and the presence of water and mobile ions.

Results

Here we report a comprehensive suite for the well-known Poisson-Boltzmann solver, DelPhi, enriched with additional features to facilitate DelPhi usage. The suite allows for easy download of both DelPhi executable files and source code along with a makefile for local installations. The users can obtain the DelPhi manual and parameter files required for the corresponding investigation. Non-experienced researchers can download examples containing all necessary data to carry out DelPhi runs on a set of selected examples illustrating various DelPhi features and demonstrating DelPhi’s accuracy against analytical solutions.

Conclusions

DelPhi suite offers not only the DelPhi executable and sources files, examples and parameter files, but also provides links to third party developed resources either utilizing DelPhi or providing plugins for DelPhi. In addition, the users and developers are offered a forum to share ideas, resolve issues, report bugs and seek help with respect to the DelPhi package. The resource is available free of charge for academic users from URL: http://compbio.clemson.edu/DelPhi.php.

Allosteric function and dysfunction of the prion protein

Transmissible spongiform encephalopathies (TSEs) are neurodegenerative diseases associated with progressive oligo- and multimerization of the prion protein (PrP(C)), its conformational conversion, aggregation and precipitation. We recently proposed that PrP(C) serves as a cell surface scaffold protein for a variety of signaling modules, the effects of which translate into wide-range functional consequences. Here we review evidence for allosteric functions of PrP(C), which constitute a common property of scaffold proteins. The available data suggest that allosteric effects among PrP(C) and its partners are involved in the assembly of multi-component signaling modules at the cell surface, impose upon both physiological and pathological conformational responses of PrP(C), and that allosteric dysfunction of PrP(C) has the potential to entail progressive signal corruption. These properties may be germane both to physiological roles of PrP(C), as well as to the pathogenesis of the TSEs and other degenerative/non-communicable diseases.

On the role of electrostatics in protein-protein interactions

The role of electrostatics in protein-protein interactions and binding is reviewed in this paper. A brief outline of the computational modeling, in the framework of continuum electrostatics, is presented and the basic electrostatic effects occurring upon the formation of the complex are discussed. The effect of the salt concentration and pH of the water phase on protein-protein binding free energy is demonstrated which indicates that the increase of the salt concentration tends to weaken the binding, an observation that is attributed to the optimization of the charge-charge interactions across the interface. It is pointed out that the pH-optimum (pH of optimal binding affinity) varies among the protein-protein complexes, and perhaps is a result of their adaptation to particular subcellular compartments. The similarities and differences between hetero- and homo-complexes are outlined and discussed with respect to the binding mode and charge complementarity.