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

PypKa server: online pKa predictions and biomolecular structure preparation with precomputed data from PDB and AlphaFold DB

When preparing biomolecular structures for molecular dynamics simulations, pKa calculations are required to provide at least a representative protonation state at a given pH value. Neglecting this step and adopting the reference protonation states of the amino acid residues in water, often leads to wrong electrostatics and nonphysical simulations. Fortunately, several methods have been developed to prepare structures considering the protonation preference of residues in their specific environments (pKa values), and some are even available for online usage. In this work, we present the PypKa server, which allows users to run physics-based, as well as ML-accelerated methods suitable for larger systems, to obtain pKa values, isoelectric points, titration curves, and structures with representative pH-dependent protonation states compatible with commonly used force fields (AMBER, CHARMM, GROMOS). The user may upload a custom structure or submit an identifier code from PBD or UniProtKB. The results for over 200k structures taken from the Protein Data Bank and the AlphaFold DB have been precomputed, and their data can be retrieved without extra calculations. All this information can also be obtained from an application programming interface (API) facilitating its usage and integration into existing pipelines as well as other web services. The web server is available at pypka.org.

Interaction of major saffron constituent safranal with trypsin: An experimental and computational investigation

Trypsin is a serine protease, an important digestive enzyme that digests the proteins in the small intestine. In the present study, we have investigated the interaction of safranal, a major saffron metabolite, with trypsin using spectroscopic and molecular docking analyses. Fluorescence emission spectra of trypsin were largely affected by the inner filter effect from safranal; that's why these were corrected using the standard procedure. The corrected fluorescence spectra have shown that the safranal quenched the intrinsic fluorescence of trypsin with a blue shift in the wavelength of emission maximum, which revealed that the microenvironment of the fluorophore became more hydrophobic. There was approximately 1: 1 fair binding between them, which increased with a rise in temperature. The interaction was favored, principally, by hydrophobic forces, and there was an efficient energy transfer from the fluorophore to the safranal. Synchronous fluorescence spectra suggested that the tryptophan residues were the major ones taking part in the fluorescence quenching of trypsin. Safranal also influenced the secondary structure of trypsin and caused partial unfolding. Molecular Docking and the Molecular Dynamics simulation of the free and complexed trypsin was also carried out. Safranal formed a stable, non-covalent complex within the S2'-S5' subsite. Moreover, two nearby tyrosine residues (Tyr39 and Tyr151) stabilized safranal through π-π interactions. Additionally, the presence of safranal led to changes in the protein flexibility and compactness, which could indicate changes in the surrounding of tryptophan residues, impacting their fluorescence. Furthermore, a loss in compactness is in line with the partial unfolding observed experimentally. Thus, both experimental and computational studies were in good agreement with each other.

DeepKa Web Server: High-Throughput Protein pKa Prediction

DeepKa is a deep-learning-based protein pKa predictor proposed in our previous work. In this study, a web server was developed that enables online protein pKa prediction driven by DeepKa. The web server provides a user-friendly interface where a single step of entering a valid PDB code or uploading a PDB format file is required to submit a job. Two case studies have been attached in order to explain how pKa's calculated by the web server could be utilized by users. Finally, combining the web server with post processing as described in case studies, this work suggests a quick workflow of investigating the relationship between protein structure and function that are pH dependent. The web server of DeepKa is freely available at http://www.computbiophys.com/DeepKa/main.

Probing the role of the protonation state of a minor groove-linker histidine in Exd-Hox-DNA binding

DNA recognition and targeting by transcription factors (TFs) through specific binding are fundamental in biological processes. Furthermore, the histidine protonation state at the TF-DNA binding interface can significantly influence the binding mechanism of TF-DNA complexes. Nevertheless, the role of histidine in TF-DNA complexes remains underexplored. Here, we employed all-atom molecular dynamics simulations using AlphaFold2-modeled complexes based on previously solved co-crystal structures to probe the role of the His-12 residue in the Extradenticle (Exd)-Sex combs reduced (Scr)-DNA complex when binding to Scr and Ultrabithorax (Ubx) target sites. Our results demonstrate that the protonation state of histidine notably affected the DNA minor-groove width profile and binding free energy. Examining flanking sequences of various binding affinities derived from SELEX-seq experiments, we analyzed the relationship between binding affinity and specificity. We uncovered how histidine protonation leads to increased binding affinity but can lower specificity. Our findings provide new mechanistic insights into the role of histidine in modulating TF-DNA binding.

Adsorption of Diclofenac and PFBS on a Hair Keratin Dimer

Environmental pollution by man-made toxic and persistent organic compounds, found throughout the world in surface and groundwater, has various negative effects on aquatic life systems and even humans. Therefore, it is important to develop and improve water treatment technologies capable of removing such substances from wastewater and purifying drinking water. The two substances investigated are the widely used painkiller diclofenac and a member of the class of "forever chemicals", perfluorobutanesulfonate. Both are known to have serious negative effects on living organisms, especially under long-term exposure, and are detectable in human hair, suggesting adsorption to a part of the hair fiber complex. In this study, a human hair keratin dimer is investigated for its ability to absorb diclofenac and perfluorobutanesulfonate. Initial predictions for binding sites are obtained via molecular docking and subjected to molecular dynamics simulations for more than 1 μs. The binding affinities obtained by the linear interaction energy method are high enough to motivate further research on human hair keratins as a sustainable, low-cost, and easily allocatable filtration material.

Comparative Performance of High-Throughput Methods for Protein pKa Predictions

The medically relevant field of protein-based therapeutics has triggered a demand for protein engineering in different pH environments of biological relevance. In silico engineering workflows typically employ high-throughput screening campaigns that require evaluating large sets of protein residues and point mutations by fast yet accurate computational algorithms. While several high-throughput pKa prediction methods exist, their accuracies are unclear due to the lack of a current comprehensive benchmarking. Here, seven fast, efficient, and accessible approaches including PROPKA3, DeepKa, PKAI, PKAI+, DelPhiPKa, MCCE2, and H++ were systematically tested on a nonredundant subset of 408 measured protein residue pKa shifts from the pKa database (PKAD). While no method outperformed the null hypotheses with confidence, as illustrated by statistical bootstrapping, DeepKa, PKAI+, PROPKA3, and H++ had utility. More specifically, DeepKa consistently performed well in tests across multiple and individual amino acid residue types, as reflected by lower errors, higher correlations, and improved classifications. Arithmetic averaging of the best empirical predictors into simple consensuses improved overall transferability and accuracy up to a root-mean-square error of 0.76 pKa units and a correlation coefficient (R2) of 0.45 to experimental pKa shifts. This analysis should provide a basis for further methodological developments and guide future applications, which require embedding of computationally inexpensive pKa prediction methods, such as the optimization of antibodies for pH-dependent antigen binding.

Basis for Accurate Protein pKa Prediction with Machine Learning

pH regulates protein structures and the associated functions in many biological processes via protonation and deprotonation of ionizable side chains where the titration equilibria are determined by pK_a's. To accelerate pH-dependent molecular mechanism research in the life sciences or industrial protein and drug designs, fast and accurate pK_a prediction is crucial. Here we present a theoretical pK_a data set PHMD549, which was successfully applied to four distinct machine learning methods, including DeepKa, which was proposed in our previous work. To reach a valid comparison, EXP67S was selected as the test set. Encouragingly, DeepKa was improved significantly and outperforms other state-of-the-art methods, except for the constant-pH molecular dynamics, which was utilized to create PHMD549. More importantly, DeepKa reproduced experimental pK_a orders of acidic dyads in five enzyme catalytic sites. Apart from structural proteins, DeepKa was found applicable to intrinsically disordered peptides. Further, in combination with solvent exposures, it is revealed that DeepKa offers the most accurate prediction under the challenging circumstance that hydrogen bonding or salt bridge interaction is partly compensated by desolvation for a buried side chain. Finally, our benchmark data qualify PHMD549 and EXP67S as the basis for future developments of protein pK_a prediction tools driven by artificial intelligence. In addition, DeepKa built on PHMD549 has been proven an efficient protein pK_a predictor and thus can be applied immediately to, for example, pK_a database construction, protein design, drug discovery, and so on.

Benchmarking In Silico Tools for Cysteine pKa Prediction

Accurate estimation of the pK_a's of cysteine residues in proteins could inform targeted approaches in hit discovery. The pK_a of a targetable cysteine residue in a disease-related protein is an important physiochemical parameter in covalent drug discovery, as it influences the fraction of nucleophilic thiolate amenable to chemical protein modification. Traditional structure-based in silico tools are limited in their predictive accuracy of cysteine pK_a's relative to other titratable residues. Additionally, there are limited comprehensive benchmark assessments for cysteine pK_a predictive tools. This raises the need for extensive assessment and evaluation of methods for cysteine pK_a prediction. Here, we report the performance of several computational pK_a methods, including single-structure and ensemble-based approaches, on a diverse test set of experimental cysteine pK_a's retrieved from the PKAD database. The dataset consisted of 16 wildtype and 10 mutant proteins with experimentally measured cysteine pK_a values. Our results highlight that these methods are varied in their overall predictive accuracies. Among the test set of wildtype proteins evaluated, the best method (MOE) yielded a mean absolute error of 2.3 pK units, highlighting the need for improvement of existing pK_a methods for accurate cysteine pK_a estimation. Given the limited accuracy of these methods, further development is needed before these approaches can be routinely employed to drive design decisions in early drug discovery efforts.

Properties of a Single Amino Acid Residue in the Third Transmembrane Domain Determine the Kinetics of Ambient Light-Sensitive Channelrhodopsin

Channelrhodopsins have been utilized in gene therapy to restore vision in patients with retinitis pigmentosa and their channel kinetics are an important factor to consider in such applications. We investigated the channel kinetics of ComV1 variants with different amino acid residues at the 172nd position. Patch clamp methods were used to record the photocurrents induced by stimuli from diodes in HEK293 cells transfected with plasmid vectors. The channel kinetics (τon and τoff) were considerably altered by the replacement of the 172nd amino acid and was dependent on the amino acid characteristics. The size of amino acids at this position correlated with τon and decay, whereas the solubility correlated with τon and τoff. Molecular dynamic simulation indicated that the ion tunnel constructed by H172, E121, and R306 widened due to H172A variant, whereas the interaction between A172 and the surrounding amino acids weakened compared with H172. The bottleneck radius of the ion gate constructed with the 172nd amino acid affected the photocurrent and channel kinetics. The 172nd amino acid in ComV1 is a key residue for determining channel kinetics as its properties alter the radius of the ion gate. Our findings can be used to improve the channel kinetics of channelrhodopsins.

Functional evolution of SARS-CoV-2 spike protein: Maintaining wide host spectrum and enhancing infectivity via surface charge of spike protein

The SARS-CoV-2 virus, which causes the COVID-19, is rapidly accumulating mutations to adapt to the hosts. We collected SARS-CoV-2 sequence data from the end of 2019 to January 2023 to analyze for their evolutionary features during the pandemic. We found that most of the SARS-CoV-2 genes are undergoing negative purifying selection, while the spike protein gene (S-gene) is undergoing rapid positive selection. From the original strain to the alpha, delta and omicron variant types, the Ka/Ks of the S-gene increases, while the Ka/Ks within one variant type decreases over time. During the evolution, the codon usage did not evolve towards optimal translation and protein expression. In contrast, only S-gene mutations showed a remarkable trend on accumulating more positive charges. This facilitates the infection via binding human ACE2 for cell entry and binding furin for cleavage. Such a functional evolution emphasizes the survival strategy of SARS-CoV-2, and indicated new druggable target to contain the viral infection. The nearly fully positively-charged interaction surfaces indicated that the infectivity of SARS-CoV-2 virus may approach a limit.

Oxidation of active cysteines mediates protein aggregation of S10R, the cataract-associated mutant of mouse GammaB-crystallin

The Ser10 to Arg mutation in mouse γB-crystallin (MGB) has been associated with protein aggregation, dense nuclear opacity, and the degeneration of fiber cells in the lens core. Overexpression of the gap junction protein, connexin 46 (Cx46), was found to suppress the nuclear opacity and restore normal cell-cell contact. However, the molecular basis for the protein aggregation and related downstream effects were not evident from these studies. Here, we provide a comparison of the structures and solution properties of wild type MGB and the S10R mutant in vitro and show that, even though the mutation does not directly involve cysteine residues, some cysteines in the mutant protein are activated, leading to the enhanced formation of intermolecular disulfide-crosslinked protein aggregates relative to the wild-type. This occurs even as the protein structure is essentially unaltered. Thus, the primary event is enhanced protein aggregation due to the disulfide crosslinking of the mutant protein. We suggest that these aggregates eventually get deposited on fiber cell membranes. Since the gap junction protein, Cx46 is involved in the transport of reduced glutathione, we posit that these deposits interfere in Cx46-mediated glutathione transport and facilitate the oxidative stress-mediated downstream changes. Overexpression of Cx46 suppresses such oxidative aggregation. These studies provide a plausible explanation for the protein aggregation and other changes that accompany this mutation. If indeed cysteine oxidation is the primary event for protein aggregation also in vivo, then the S10R mutant mouse, which is currently available, could serve as a viable animal model for human age-onset cataract.

Extending the Stochastic Titration CpHMD to CHARMM36m

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

A Guide to the Continuous Constant pH Molecular Dynamics Methods in Amber and CHARMM [Article v1.0]

Like temperature and pressure, solution pH is an important environmental variable in biomolecular simulations. Virtually all proteins depend on pH to maintain their structure and function. In conventional molecular dynamics (MD) simulations of proteins, pH is implicitly accounted for by assigning and fixing protonation states of titratable sidechains. This is a significant limitation, as the assigned protonation states may be wrong and they may change during dynamics. In this tutorial, we guide the reader in learning and using the various continuous constant pH MD methods in Amber and CHARMM packages, which have been applied to predict pK a values and elucidate proton-coupled conformational dynamics of a variety of proteins including enzymes and membrane transporters.

Efficient proton shuttle makes SazCA an excellent CO2 hydration enzyme

The fastest member of the carbonic anhydrase family catalysing the reversible hydration of carbon dioxide to bicarbonate ions has been recently reported to be SazCA. While thermostable, this enzyme shows exceptional activity at 353 K for the reaction. This study explores the molecular basis for the exceptional activity of SazCA, in contrast to SspCA, probed using molecular dynamics simulations. Our simulations, carried out at different temperatures, indicate the presence of efficient proton shuttle between the active zinc centre and His64 residue in the two enzymes. The proton accepting His64 residue was identified to have in and out conformations with the in conformations being supportive to proton acceptance. Our simulations show a large population of in conformations in SazCA making the enzyme exhibit an exceptional activity. The RMSF and H-bonds analysis confirmed the role of His2 and His207 in supporting the attainment of in conformations in SazCA resulting in exceptional activity.Communicated by Ramaswamy H. Sarma.

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

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

Ab initio derivation of flavin hyperfine interactions for the protein magnetosensor cryptochrome

The radicals derived from flavin adenine dinucleotide (FAD) are a corner stone of recent hypotheses about magnetoreception, including the compass of migratory songbirds. These models attribute a magnetic sense to coherent spin dynamics in radical pairs within the flavo-protein cryptochrome. The primary determinant of sensitivity and directionality of this process are the hyperfine interactions of the involved radicals. Here, we present a comprehensive computational study of the hyperfine couplings in the protonated and unprotonated FAD radicals in cryptochrome 4 from C. livia. We combine long (800 ns) molecular dynamics trajectories to accurate quantum chemistry calculations. Hyperfine parameters are derived using auxiliary density functional theory applied to cluster and hybrid QM/MM (Quantum Mechanics/Molecular Mechanics) models comprising the FAD and its significant surrounding environment, as determined by a detailed sensitivity analysis. Thanks to this protocol we elucidate the sensitivity of the hyperfine interaction parameters to structural fluctuations and the polarisation effect of the protein environment. We find that the ensemble-averaged hyperfine interactions are predominantly governed by thermally induced geometric distortions of the flavin. We discuss our results in view of the expected performance of these radicals as part of a magnetoreceptor. Our data could be used to parametrize spin Hamiltonians including not only average values but also standard deviations.

Transmembrane Protease Serine 2 Proteolytic Cleavage of the SARS-CoV-2 Spike Protein: A Mechanistic Quantum Mechanics/Molecular Mechanics Study to Inspire the Design of New Drugs To Fight the COVID-19 Pandemic

Despite the development of vaccines against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, there is an urgent need for efficient drugs to treat infected patients. An attractive drug target is the human transmembrane protease serine 2 (TMPRSS2) because of its vital role in the viral infection mechanism of SARS-CoV-2 by activation of the virus spike protein (S protein). Having in mind that the information derived from quantum mechanics/molecular mechanics (QM/MM) studies could be an important tool in the design of transition-state (TS) analogue inhibitors, we resorted to adiabatic QM/MM calculations to determine the mechanism of the first step (acylation) of proteolytic cleavage of the S protein with atomistic details. Acylation occurred in two stages: (i) proton transfer from Ser441 to His296 concerted with the nucleophilic attack of Ser441 to the substrate's P1-Arg and (ii) proton transfer from His296 to the P1'-Ser residue concerted with the cleavage of the ArgP1-SerP1' peptide bond, with a Gibbs activation energy of 17.1 and 15.8 kcal mol^-1, relative to the reactant. An oxyanion hole composed of two hydrogen bonds stabilized the rate-limiting TS by 8 kcal mol^-1. An analysis of the TMPRSS2 interactions with the high-energy, short-lived tetrahedral intermediate highlighted the limitations of current clinical inhibitors and pointed out specific ways to develop higher-affinity TS analogue inhibitors. The results support the development of more efficient drugs against SARS-CoV-2 using a human target, free from resistance development.

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.

iCn3D: From Web-Based 3D Viewer to Structural Analysis Tool in Batch Mode

iCn3D was initially developed as a web-based 3D molecular viewer. It then evolved from visualization into a full-featured interactive structural analysis software. It became a collaborative research instrument through the sharing of permanent, shortened URLs that encapsulate not only annotated visual molecular scenes, but also all underlying data and analysis scripts in a FAIR manner. More recently, with the growth of structural databases, the need to analyze large structural datasets systematically led us to use Python scripts and convert the code to be used in Node. js scripts. We showed a few examples of Python scripts at https://github.com/ncbi/icn3d/tree/master/icn3dpython to export secondary structures or PNG images from iCn3D. Users just need to replace the URL in the Python scripts to export other annotations from iCn3D. Furthermore, any interactive iCn3D feature can be converted into a Node. js script to be run in batch mode, enabling an interactive analysis performed on one or a handful of protein complexes to be scaled up to analysis features of large ensembles of structures. Currently available Node. js analysis scripts examples are available at https://github.com/ncbi/icn3d/tree/master/icn3dnode. This development will enable ensemble analyses on growing structural databases such as AlphaFold or RoseTTAFold on one hand and Electron Microscopy on the other. In this paper, we also review new features such as DelPhi electrostatic potential, 3D view of mutations, alignment of multiple chains, assembly of multiple structures by realignment, dynamic symmetry calculation, 2D cartoons at different levels, interactive contact maps, and use of iCn3D in Jupyter Notebook as described at https://pypi.org/project/icn3dpy.

The pH Effects on SARS-CoV and SARS-CoV-2 Spike Proteins in the Process of Binding to hACE2

COVID-19 has been threatening human health since the late 2019, and has a significant impact on human health and economy. Understanding SARS-CoV-2 and other coronaviruses is important to develop effective treatments for COVID-19 and other coronavirus-caused diseases. In this work, we applied multi-scale computational approaches to study the electrostatic features of spike (S) proteins for SARS-CoV and SARS-CoV-2. From our results, we found that SARS-CoV and SARS-CoV-2 have similar charge distributions and electrostatic features when binding with the human angiotensin-converting enzyme 2 (hACE2). Energy pH-dependence calculations revealed that the complex structures of hACE2 and the S proteins of SARS-CoV/SARS-CoV-2 are stable at pH values ranging from 7.5 to 9. Three independent 100 ns molecular dynamics (MD) simulations were performed using NAMD to investigate the hydrogen bonds between S proteins RBD and hACE2 RBD. From MD simulations, we found that SARS-CoV-2 forms 19 pairs (average of three simulations) of hydrogen bonds with high occupancy (>50%) to hACE2, compared to 16 pairs between SARS-CoV and hACE2. Additionally, SARS-CoV viruses prefer sticking to the same hydrogen bond pairs, while SARS-CoV-2 tends to have a larger range of selections on hydrogen bonds acceptors. We also labelled key residues involved in forming the top five hydrogen bonds that were found in all three independent 100 ns simulations. This identification is important to potential drug designs for COVID-19 treatments. Our work will shed the light on current and future coronavirus-caused diseases.

Using a comprehensive approach to investigate the interaction between Kinesin-5/Eg5 and the microtubule

Kinesins are microtubule-based motor proteins that play important roles ranging from intracellular transport to cell division. Human Kinesin-5 (Eg5) is essential for mitotic spindle assembly during cell division. By combining molecular dynamics (MD) simulations with other multi-scale computational approaches, we systematically studied the interaction between Eg5 and the microtubule. We find the electrostatic feature on the motor domains of Eg5 provides attractive interactions to the microtubule. Additionally, the folding and binding energy analysis reveals that the Eg5 motor domain performs its functions best when in a weak acidic environment. Molecular dynamics analyses of hydrogen bonds and salt bridges demonstrate that, on the binding interfaces of Eg5 and the tubulin heterodimer, salt bridges play the most significant role in holding the complex. The salt bridge residues on the binding interface of Eg5 are mostly positive, while salt bridge residues on the binding interface of tubulin heterodimer are mostly negative. Such salt bridge residue distribution is consistent with electrostatic potential calculations. In contrast, the interface between α and β-tubulins is dominated by hydrogen bonds rather than salt bridges. Compared to the Eg5/α-tubulin interface, the Eg5/β-tubulin interface has a greater number of salt bridges and higher occupancy for salt bridges. This asymmetric salt bridge distribution may play a significant role in Eg5′s directionality. The residues involved in hydrogen bonds and salt bridges are identified in this work and may be helpful for anticancer drug design.

Protein pK a Prediction with Machine Learning

Protein pK _a prediction is essential for the investigation of the pH-associated relationship between protein structure and function. In this work, we introduce a deep learning-based protein pK _a predictor DeepKa, which is trained and validated with the pK _a values derived from continuous constant-pH molecular dynamics (CpHMD) simulations of 279 soluble proteins. Here, the CpHMD implemented in the Amber molecular dynamics package has been employed (Huang Y.J. Chem. Inf. Model.2018, 58, 1372-1383). Notably, to avoid discontinuities at the boundary, grid charges are proposed to represent protein electrostatics. We show that the prediction accuracy by DeepKa is close to that by CpHMD benchmarking simulations, validating DeepKa as an efficient protein pK _a predictor. In addition, the training and validation sets created in this study can be applied to the development of machine learning-based protein pK _a predictors in the future. Finally, the grid charge representation is general and applicable to other topics, such as the protein-ligand binding affinity prediction.

3D Interaction Homology: Computational Titration of Aspartic Acid, Glutamic Acid and Histidine Can Create pH-Tunable Hydropathic Environment Maps

Aspartic acid, glutamic acid and histidine are ionizable residues occupying various protein environments and perform many different functions in structures. Their roles are tied to their acid/base equilibria, solvent exposure, and backbone conformations. We propose that the number of unique environments for ASP, GLU and HIS is quite limited. We generated maps of these residue's environments using a hydropathic scoring function to record the type and magnitude of interactions for each residue in a 2703-protein structural dataset. These maps are backbone-dependent and suggest the existence of new structural motifs for each residue type. Additionally, we developed an algorithm for tuning these maps to any pH, a potentially useful element for protein design and structure building. Here, we elucidate the complex interplay between secondary structure, relative solvent accessibility, and residue ionization states: the degree of protonation for ionizable residues increases with solvent accessibility, which in turn is notably dependent on backbone structure.

Insights on peptide topology in the computational design of protein ligands: the example of lysozyme binding peptides

Herein, we compared the ability of linear and cyclic peptides generated in silico to target different protein sites: internal pockets and solvent-exposed sites. We selected human lysozyme (HuL) as a model target protein combined with the computational evolution of linear and cyclic peptides. The sequence evolution of these peptides was based on the PARCE algorithm. The generated peptides were screened based on their aqueous solubility and HuL binding affinity. The latter was evaluated by means of scoring functions and atomistic molecular dynamics (MD) trajectories in water, which allowed prediction of the structural features of the protein-peptide complexes. The computational results demonstrated that cyclic peptides constitute the optimal choice for solvent exposed sites, while both linear and cyclic peptides are capable of targeting the HuL pocket effectively. The most promising binders found in silico were investigated experimentally by surface plasmon resonance (SPR), nuclear magnetic resonance (NMR), and electrospray ionization mass spectrometry (ESI-MS) techniques. All tested peptides displayed dissociation constants in the micromolar range, as assessed by SPR; however, both NMR and ESI-MS suggested multiple binding modes, at least for the pocket binding peptides. A detailed NMR analysis confirmed that both linear and cyclic pocket peptides correctly target the binding site they were designed for.

The pH Effects on SARS-CoV and SARS-CoV-2 Spike Proteins in the Process of Binding to hACE2

COVID-19 has been threatening human health since the late 2019, which has significant impact on human health and economy. Understanding the SARS-CoV-2 and other coronaviruses is important to develop effective treatments for COVID-19 and other coronaviruses-caused diseases. In this work, we applied multi-scale computational approaches to study the electrostatic features of spike (S) proteins for SARS-CoV and SARS-CoV-2. From our results, we found thatSARS-CoV and SARS-CoV-2 have similar charge distributions and electrostatic features when binding with the human angiotensin-converting enzyme 2 (hACE2). The energy pH-dependence calculation srevealed that the complex structures of hACE2 and the S proteins of SARS-CoV/SARS-CoV-2 are stable at pH values ranging from 7.5 to 9. Molecular dynamics simulations were performed using NAMD to investigate the hydrogen bonds between S proteins and hACE2. From the MD simulations it was found that SARS-CoV-2 has four pairsof essential hydrogenbonds (high occupancy, >80%), while SARS-CoV has three pairs, which indicates the SARS-CoV-2 S protein has relatively more robust binding strategy than SARS-CoVS protein.Four key residues forming essential hydrogen bonds from SARS-CoV-2 are identified, which are potential drug targets for COVID-19 treatments. The findings in this study shed lights on the current and future treatments for COVID-19 and other coronaviruses-caused diseases.

Polyamine blockade and binding energetics in the MthK potassium channel

Polyamines such as spermidine and spermine are found in nearly all cells, at concentrations ranging up to 0.5 mM. These cations are endogenous regulators of cellular K⁺ efflux, binding tightly in the pores of inwardly rectifying K⁺ (K_ir) channels in a voltage-dependent manner. Although the voltage dependence of K_ir channel polyamine blockade is thought to arise at least partially from the energetically coupled movements of polyamine and K⁺ ions through the pore, the nature of physical interactions between these molecules is unclear. Here we analyze the polyamine-blocking mechanism in the model K⁺ channel MthK, using a combination of electrophysiology and computation. Spermidine (SPD³⁺) and spermine (SPM⁴⁺) each blocked current through MthK channels in a voltage-dependent manner, and blockade by these polyamines was described by a three-state kinetic scheme over a wide range of polyamine concentrations. In the context of the scheme, both SPD³⁺ and SPM⁴⁺ access a blocking site with similar effective gating valences (0.84 ± 0.03 e₀ for SPD³⁺ and 0.99 ± 0.04 e₀ for SPM⁴⁺), whereas SPM⁴⁺ binds in the blocked state with an ∼20-fold higher affinity than SPD³⁺ (K_d = 28.1 ± 3.1 µM for SPD³⁺ and 1.28 ± 0.20 µM for SPM⁴⁺), consistent with a free energy difference of 1.8 kcal/mol. Molecular simulations of the MthK pore in complex with either SPD³⁺ or SPM⁴⁺ are consistent with the leading amine interacting with the hydroxyl groups of T59, at the selectivity filter threshold, with access to this site governed by outward movement of K⁺ ions. These coupled movements can account for a large fraction of the voltage dependence of blockade. In contrast, differences in binding energetics between SPD³⁺ and SPM⁴⁺ may arise from distinct electrostatic interactions between the polyamines and carboxylate oxygens on the side chains of E92 and E96, located in the pore-lining helix.

High-resolution structure of the amino acid transporter AdiC reveals insights into the role of water molecules and networks in oligomerization and substrate binding

Background

The L-arginine/agmatine transporter AdiC is part of the arginine-dependent extreme acid resistance system of the bacterium Escherichia coli and its pathogenic varieties such as strain E. coli O157:H7. At the present time, there is a lack of knowledge concerning the role of water molecules and networks for the structure and function of AdiC, and solute transporters in general.

Results

The structure of the L-arginine/agmatine transporter AdiC was determined at 1.7 Å resolution by X-ray crystallography. This high resolution allowed for the identification of numerous water molecules buried in the structure. In combination with molecular dynamics (MD) simulations, we demonstrate that water molecules play an important role for stabilizing the protein and key residues, and act as placeholders for atoms of the AdiC substrates L-arginine and agmatine. MD simulations unveiled flexibility and restrained mobility of gating residues W202 and W293, respectively. Furthermore, a water-filled cavity was identified at the dimer interface of AdiC. The two monomers formed bridging interactions through water-mediated hydrogen bonds. The accessibility and presence of water molecules in this cavity was confirmed with MD simulations. Point mutations disrupting the interfacial water network validated the importance of water molecules for dimer stabilization.

Conclusions

This work gives new insights into the role and importance of water molecules in the L-arginine/agmatine transporter AdiC for protein stabilization and substrate-binding site shaping and as placeholders of substrate atoms. Furthermore, and based on the observed flexibility and restrained mobility of gating residues, a mechanistic role of the gate flexibility in the transport cycle was proposed. Finally, we identified a water-filled cavity at the dimeric interface that contributes to the stability of the amino acid transporter oligomer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-021-01102-4.

Electron Transfer Coupled to Conformational Dynamics in Cell Respiration

Cellular respiration is a fundamental process required for energy production in many organisms. The terminal electron transfer complex in mitochondrial and many bacterial respiratory chains is cytochrome c oxidase (CcO). This converts the energy released in the cytochrome c/oxygen redox reaction into a transmembrane proton electrochemical gradient that is used subsequently to power ATP synthesis. Despite detailed knowledge of electron and proton transfer paths, a central question remains as to whether the coupling between electron and proton transfer in mammalian mitochondrial forms of CcO is mechanistically equivalent to its bacterial counterparts. Here, we focus on the conserved span between H376 and G384 of transmembrane helix (TMH) X of subunit I. This conformationally-dynamic section has been suggested to link the redox activity with the putative H pathway of proton transfer in mammalian CcO. The two helix X mutants, Val380Met (V380M) and Gly384Asp (G384D), generated in the genetically-tractable yeast CcO, resulted in a respiratory-deficient phenotype caused by the inhibition of intra-protein electron transfer and CcO turnover. Molecular aspects of these variants were studied by long timescale atomistic molecular dynamics simulations performed on wild-type and mutant bovine and yeast CcOs. We identified redox- and mutation-state dependent conformational changes in this span of TMH X of bovine and yeast CcOs which strongly suggests that this dynamic module plays a key role in optimizing intra-protein electron transfers.

Computational Study on DNA Repair: The Roles of Electrostatic Interactions Between Uracil-DNA Glycosylase (UDG) and DNA

Uracil-DNA glycosylase (UDG) is one of the most important base excision repair (BER) enzymes involved in the repair of uracil-induced DNA lesion by removing uracil from the damaged DNA. Uracil in DNA may occur due to cytosine deamination or deoxy uridine monophosphate (dUMP) residue misincorporation during DNA synthesis. Medical evidences show that an abnormal expression of UDG is related to different types of cancer, including colorectal cancer, lung cancer, and liver cancer. Therefore, the research of UDG is crucial in cancer treatment and prevention as well as other clinical activities. Here we applied multiple computational methods to study UDG in several perspectives: Understanding the stability of the UDG enzyme in different pH conditions; studying the differences in charge distribution between the pocket side and non-pocket side of UDG; analyzing the field line distribution at the interfacial area between UDG and DNA; and performing electrostatic binding force analyses of the special region of UDG (pocket area) and the target DNA base (uracil) as well as investigating the charged residues on the UDG binding pocket and binding interface. Our results show that the whole UDG binding interface, and not the UDG binding pocket area alone, provides the binding attractive force to the damaged DNA at the uracil base.

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.

KEAP1 Cancer Mutants: A Large-Scale Molecular Dynamics Study of Protein Stability

We have performed 280 μs of unbiased molecular dynamics (MD) simulations to investigate the effects of 12 different cancer mutations on Kelch-like ECH-associated protein 1 (KEAP1) (G333C, G350S, G364C, G379D, R413L, R415G, A427V, G430C, R470C, R470H, R470S and G476R), one of the frequently mutated proteins in lung cancer. The aim was to provide structural insight into the effects of these mutants, including a new class of ANCHOR (additionally NRF2-complexed hypomorph) mutant variants. Our work provides additional insight into the structural dynamics of mutants that could not be analyzed experimentally, painting a more complete picture of their mutagenic effects. Notably, blade-wise analysis of the Kelch domain points to stability as a possible target of cancer in KEAP1. Interestingly, structural analysis of the R470C ANCHOR mutant, the most prevalent missense mutation in KEAP1, revealed no significant change in structural stability or NRF2 binding site dynamics, possibly indicating an covalent modification as this mutant's mode of action.

VEGF and VEGFR2 bind to similar pH-sensitive sites on fibronectin, exposed by heparin-mediated conformational changes

Physical interactions between vascular endothelial growth factor (VEGF), a central player in blood endothelial cell biology, and fibronectin, a major fibrillar protein of the extracellular matrix, are important determinants of angiogenic activity in health and disease. Conditions signaling the need for new blood vessel growth, such as hypoxia and low extracellular pH, increase VEGF–fibronectin interactions. These interactions can be further fine-tuned through changes in the availability of the VEGF-binding sites on fibronectin, regulated by conformational changes induced by heparin and heparan sulfate chains within the extracellular matrix. These interactions may alter VEGF bioavailability, generate gradients, or alter the way VEGF is recognized by and activates its cell-surface receptors. Here, using equilibrium and kinetic studies, we discovered that fibronectin can also interact with the extracellular domain of the VEGF receptor 2 (VEGFR2). The VEGFR2-binding sites on fibronectin show great similarity to the VEGF-binding sites, as they were also exposed upon heparin-induced conformational changes in fibronectin, and the interaction was enhanced at acidic pH. Kinetic parameters and affinities for VEGF and VEGFR2 binding to fibronectin were determined by surface plasmon resonance measurements, revealing two populations of fibronectin-binding sites for each molecule. Our data also suggest that a VEGF/VEGFR2/fibronectin triple complex may be formed by VEGF or VEGFR2 first binding to fibronectin and subsequently recruiting the third binding partner. The formation of such a complex may lead to the activation of distinct angiogenic signaling pathways, offering new possibilities for clinical applications that target angiogenesis.

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.

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.

Molecular mechanics and dynamic simulations of well-known Kabuki syndrome-associated KDM6A variants reveal putative mechanisms of dysfunction

BACKGROUND

Kabuki syndrome is a genetic disorder that affects several body systems and presents with variations in symptoms and severity. The syndrome is named for a common phenotype of faces resembling stage makeup used in a Japanese traditional theatrical art named kabuki. The most frequent cause of this syndrome is mutations in the H3K4 family of histone methyltransferases while a smaller percentage results from genetic alterations affecting the histone demethylase, KDM6A. Because of the rare presentation of the latter form of the disease, little is known about how missense changes in the KDM6A protein sequence impact protein function.

RESULTS

In this study, we use molecular mechanic and molecular dynamic simulations to enhance the annotation and mechanistic interpretation of the potential impact of eleven KDM6A missense variants found in Kabuki syndrome patients. These variants (N910S, D980V, S1025G, C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W, and R1351Q) are predicted to be pathogenic, likely pathogenic or of uncertain significance by sequence-based analysis. Here, we demonstrate, for the first time, that although Kabuki syndrome missense variants are found outside the functionally critical regions, they could affect overall function by significantly disrupting global and local conformation (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), chemical environment (C1153R, C1153Y, P1195L, L1200F, Q1212R, Q1248R, R1255W and R1351Q), and/or molecular dynamics of the catalytic domain (all variants). In addition, our approaches predict that many mutations, in particular C1153R, could allosterically disrupt the key enzymatic interactions of KDM6A.

CONCLUSIONS

Our study demonstrates that the KDM6A Kabuki syndrome variants may impair histone demethylase function through various mechanisms that include altered protein integrity, local environment, molecular interactions and protein dynamics. Molecular dynamics simulations of the wild type and the variants are critical to gain a better understanding of molecular dysfunction. This type of comprehensive structure- and MD-based analyses should help develop improved impact scoring systems to interpret the damaging effects of variants in this protein and other related proteins as well as provide detailed mechanistic insight that is not currently predictable from sequence alone.

Broad specificity of immune helminth scFv library to identify monoclonal antibodies targeting Strongyloides

Antibodies have different chemical properties capable of targeting a diverse nature of antigens. Traditionally, immune antibody libraries are perceived to be disease-specific with a skewed repertoire. The complexity during the generation of a combinatorial antibody library allows for a skewed but diverse repertoire to be generated. Strongyloides stercoralis is a parasite that causes strongyloidiasis, a potentially life-threatening disease with a complex diagnosis that impedes effective control and treatment of the disease. This study describes the isolation of monoclonal antibodies against S. stercoralis NIE recombinant protein using an immune antibody phage display library derived from lymphatic filaria-infected individuals. The isolated antibody clones showed both lambda and kappa light chains gene usage, with diverse amino acid distributions. Structural analysis showed that electropositivity and the interface area could determine the binding affinity of the clones with NIE. The successful identification of S. stercoralis antibodies from the filarial immune library highlights the breadth of antibody gene diversification in an immune antibody library that can be applied for closely related infections.

Protonation-Induced Dynamic Allostery in PDZ Domain: Evidence of Perturbation-Independent Universal Response Network

Dynamic allostery is a relatively new paradigm where certain external perturbations may lead to modulation of conformational dynamics at a distant part of a protein without significant changes in the overall structure. While most well-characterized examples of dynamic allostery involve binding with other entities like small molecules, peptides, or nucleic acids, in this work we demonstrate that chemical modifications like protonation may lead to significant dynamical allosteric response in a PDZ domain protein. Tuning the protonation states of two histidine residues (H317 and H372), we identify the allosteric pathways responsible for the dynamic response. Interestingly, the same set of residues that constitute the allosteric response network upon ligand binding seem to be responsible for protonation-induced dynamic allostery. Thus, we propose the existence of an inherent universal response network in signaling proteins, where the same set of residues can respond to varying types of external perturbations in terms of rearrangement of hydrogen-bonded network and redistribution of electrostatic interaction energies.

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.

Vulnerabilities of the SARS-CoV-2 Virus to Proteotoxicity-Opportunity for Repurposed Chemotherapy of COVID-19 Infection

The global pandemic of COVID-19 disease caused by infection with the SARS-CoV-2 coronavirus, has produced an urgent requirement and search for improved treatments while effective vaccines are developed. A strategy for improved drug therapy is to increase levels of endogenous reactive metabolites for selective toxicity to SARS-CoV-2 by preferential damage to the viral proteome. Key reactive metabolites producing major quantitative damage to the proteome in physiological systems are: reactive oxygen species (ROS) and the reactive glycating agent methylglyoxal (MG); cysteine residues and arginine residues are their most susceptible targets, respectively. From sequenced-based prediction of the SARS-CoV-2 proteome, we found 0.8-fold enrichment or depletion of cysteine residues in functional domains of the viral proteome; whereas there was a 4.6-fold enrichment of arginine residues, suggesting SARS-CoV-2 is resistant to oxidative agents and sensitive to MG. For arginine residues of the SARS-CoV-2 coronavirus predicted to be in functional domains, we examined which are activated toward modification by MG - residues with predicted or expected low pKa by neighboring group in interactions. We found 25 such arginine residues, including 2 in the spike protein and 10 in the nucleoprotein. These sites were partially conserved in related coronaviridae: SARS-CoV and MERS. Finally, we identified drugs which increase cellular MG concentration to virucidal levels: antitumor drugs with historical antiviral activity, doxorubicin and paclitaxel. Our findings provide evidence of potential vulnerability of SARS-CoV-2 to inactivation by MG and a scientific rationale for repurposing of doxorubicin and paclitaxel for treatment of COVID-19 disease, providing efficacy and adequate therapeutic index may be established.

Assessment of proton-coupled conformational dynamics of SARS and MERS coronavirus papain-like proteases: Implication for designing broad-spectrum antiviral inhibitors

Broad-spectrum antiviral drugs are urgently needed to stop the Coronavirus Disease 2019 pandemic and prevent future ones. The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is related to the SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), which have caused the previous outbreaks. The papain-like protease (PLpro) is an attractive drug target due to its essential roles in the viral life cycle. As a cysteine protease, PLpro is rich in cysteines and histidines, and their protonation/deprotonation modulates catalysis and conformational plasticity. Here, we report the pK_a calculations and assessment of the proton-coupled conformational dynamics of SARS-CoV-2 in comparison to SARS-CoV and MERS-CoV PLpros using the recently developed graphical processing unit (GPU)-accelerated implicit-solvent continuous constant pH molecular dynamics method with a new asynchronous replica-exchange scheme, which allows computation on a single GPU card. The calculated pK_a's support the catalytic roles of the Cys-His-Asp triad. We also found that several residues can switch protonation states at physiological pH among which is C270/271 located on the flexible blocking loop 2 (BL2) of SARS-CoV-2/CoV PLpro. Simulations revealed that the BL2 can open and close depending on the protonation state of C271/270, consistent with the most recent crystal structure evidence. Interestingly, despite the lack of an analogous cysteine, BL2 in MERS-CoV PLpro is also very flexible, challenging a current hypothesis. These findings are supported by the all-atom fixed-charge simulations and provide a starting point for more detailed studies to assist the structure-based design of broad-spectrum inhibitors against CoV PLpros.

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.

Visualizing group II intron dynamics between the first and second steps of splicing

Group II introns are ubiquitous self-splicing ribozymes and retrotransposable elements evolutionarily and chemically related to the eukaryotic spliceosome, with potential applications as gene-editing tools. Recent biochemical and structural data have captured the intron in multiple conformations at different stages of catalysis. Here, we employ enzymatic assays, X-ray crystallography, and molecular simulations to resolve the spatiotemporal location and function of conformational changes occurring between the first and the second step of splicing. We show that the first residue of the highly-conserved catalytic triad is protonated upon 5’-splice-site scission, promoting a reversible structural rearrangement of the active site (toggling). Protonation and active site dynamics induced by the first step of splicing facilitate the progression to the second step. Our insights into the mechanism of group II intron splicing parallels functional data on the spliceosome, thus reinforcing the notion that these evolutionarily-related molecular machines share the same enzymatic strategy.

Group II introns are self-splicing ribozymes. Here, the authors employ enzymatic assay, X-ray crystallography and molecular dynamics simulations to show that protonation of the group II intron catalytic triad plays an important role for the transition from the first to the second step of splicing.

Nα-Acetylation of the virulence factor EsxA is required for mycobacterial cytosolic translocation and virulence

The Mycobacterium tuberculosis virulence factor EsxA and its chaperone EsxB are secreted as a heterodimer (EsxA:B) and are crucial for mycobacterial escape from phagosomes and cytosolic translocation. Current findings support the idea that for EsxA to interact with host membranes, EsxA must dissociate from EsxB at low pH. However, the molecular mechanism by which the EsxA:B heterodimer separates is not clear. In the present study, using liposome-leakage and cytotoxicity assays, LC-MS/MS-based proteomics, and CCF-4 FRET analysis, we obtained evidence that the Nα-acetylation of the Thr-2 residue on EsxA, a post-translational modification that is present in mycobacteria but absent in Escherichia coli, is required for the EsxA:B separation. Substitutions at Thr-2 that precluded Nα-acetylation inhibited the heterodimer separation and hence prevented EsxA from interacting with the host membrane, resulting in attenuated mycobacterial cytosolic translocation and virulence. Molecular dynamics simulations revealed that at low pH, the Nα-acetylated Thr-2 makes direct and frequent "bind-and-release" contacts with EsxB, which generates a force that pulls EsxB away from EsxA. In summary, our findings provide evidence that the Nα-acetylation at Thr-2 of EsxA facilitates dissociation of the EsxA:B heterodimer required for EsxA membrane permeabilization and mycobacterial cytosolic translocation and virulence.