New Parameters for Higher Accuracy in the Computation of Binding Free Energy Differences upon Alanine Scanning Mutagenesis on Protein–Protein Interfaces

Attenuation of Las/Rhl quorum sensing regulated virulence and biofilm formation in Pseudomonas aeruginosa PAO1 by Artocarpesin

The emergence of multidrug resistance and increased pathogenicity in microorganisms is conferred by the presence of highly synchronized cell density dependent signalling pathway known as quorum sensing (QS). The QS hierarchy is accountable for the secretion of virulence phenotypes, biofilm formation and drug resistance. Hence, targeting the QS phenomenon could be a promising strategy to counteract the bacterial virulence and drug resistance. In the present study, artocarpesin (ACN), a 6-prenylated flavone was investigated for its capability to quench the synthesis of QS regulated virulence factors. From the results, ACN showed significant inhibition of secreted virulence phenotypes such as pyocyanin (80%), rhamnolipid (79%), protease (69%), elastase (84%), alginate (88%) and biofilm formation (88%) in opportunistic pathogen, Pseudomonas aeruginosa PAO1. Further, microscopic observation of biofilm confirmed a significant reduction in biofilm matrix when P. aeruginosa PAO1 was supplemented with ACN at its sub-MIC concentration. Quantitative gene expression studies showed the promising aspects of ACN in down regulation of several QS regulatory genes associated with production of virulence phenotypes. Upon treatment with sub-MIC of ACN, the bacterial colonization in the gut of Caenorhabditis elegans was potentially reduced and the survival rate was greatly improved. The promising QS inhibition activities were further validated through in silico studies, which put an insight into the mechanism of QS inhibition. Thus, ACN could be considered as possible drug candidate targeting chronic microbial infections.

Decoding selectivity: computational insights into AKR1B1 and AKR1B10 inhibition

Understanding selectivity mechanisms of inhibitors towards highly homologous proteins is of paramount importance in the design of selective candidates. Human aldo-keto reductases (AKRs) pertain to a superfamily of monomeric oxidoreductases, which serve as NADPH-dependent cytosolic enzymes to catalyze the reduction of carbonyl groups to primary and secondary alcohols using electrons from NADPH. Among AKRs, AKR1B1 is emerging as a promising target for cancer treatment and diabetes, despite its high structural similarity with AKR1B10, which leads to severe adverse events. Therefore, it is crucial to understand the selectivity mechanisms of AKR1B1 and AKR1B10 to discover safe anticancer candidates with optimal therapeutic efficacy. In this study, multiple computational strategies, including sequence alignment, structural comparison, Protein Contacts Atlas analysis, molecular docking, molecular dynamics simulation, MM-GBSA calculation, alanine scanning mutagenesis and pharmacophore modeling analysis were employed to comprehensively understand the selectivity mechanisms of AKR1B1/10 inhibition based on selective inhibitor lidorestat and HAHE. This study would provide substantial evidence in the design of potent and highly selective AKR1B1/10 inhibitors in future.

Bioinformatics insights into CENP-T and CENP-W protein-protein interaction disruptive amino acid substitution in the CENP-T-W complex

Kinetochores are multi-protein assemblies present at the centromere of the human chromosome and play a crucial role in cellular mitosis. The CENP-T and CENP-W chains form a heterodimer, which is an integral part of the inner kinetochore, interacting with the linker DNA on one side and the outer kinetochore on the other. Additionally, the CENP-T-W dimer interacts with other regulatory proteins involved in forming inner kinetochores. The specific roles of different amino acids in the CENP-W at the protein-protein interaction (PPI) interface during the CENP-T-W dimer formation remain incompletely understood. Since cell division goes awry in diseases like cancer, this CENP-T-W partnership is a potential target for new drugs that could restore healthy cell division. We employed molecular docking, binding free energy calculations, and molecular dynamics (MD) simulations to investigate the disruptive effects of amino acids substitutions in the CENP-W chain on CENP-T-W dimer formation. By conducting a molecular docking study and analysing hydrogen bonding interactions, we identified key residues in CENP-W (ASN-46, ARG-53, LEU-83, SER-86, ARG-87, and GLY-88) for further investigation. Through site-directed mutagenesis and subsequent binding free energy calculations, we refined the selection of mutant. We chose four mutants (N46K, R53K, L83K, and R87E) of CENP-W to assess their comparative potential in forming CENP-T-W dimer. Our analysis from 250 ns long revealed that the substitution of LEU83 and ARG53 residues in CENP-W with the LYS significantly disrupts the formation of CENP-T-W dimer. In conclusion, LEU83 and ARG53 play a critical role in CENP-T and CENP-W dimerization which is ultimately required for cellular mitosis. Our findings not only deepen our understanding of cell division but also hint at exciting drug-target possibilities.

Developing similarity matrices for antibody-protein binding interactions

The inventions of AlphaFold and RoseTTAFold are revolutionizing computational protein science due to their abilities to reliably predict protein structures. Their unprecedented successes are due to the parallel consideration of several types of information, one of which is protein sequence similarity information. Sequence homology has been studied for many decades and depends on similarity matrices to define how similar or different protein sequences are to one another. A natural extension of predicting protein structures is predicting the interactions between proteins, but similarity matrices for protein-protein interactions do not exist. This study conducted a mutational analysis of 384 non-redundant antibody-protein antigen complexes to calculate antibody-protein interaction similarity matrices. Every important residue in each antibody and each antigen was mutated to each of the other 19 commonly occurring amino acids and the percentage changes in interaction energies were calculated using three force fields: CHARMM, Amber, and Rosetta. The data were used to construct six interaction similarity matrices, one for antibodies and another for antigens using each force field. The matrices exhibited both commonalities, such as mutations of aromatic and charged residues being the most detrimental, and differences, such as Rosetta predicting mutations of serines to be better tolerated than either Amber or CHARMM. A comparison to nine previously published similarity matrices for protein sequences revealed that the new interaction matrices are more similar to one another than they are to any of the previous matrices. The created similarity matrices can be used in force field specific applications to help guide decisions regarding mutations in protein-protein binding interfaces.

How helpful were molecular dynamics simulations in shaping our understanding of SARS-CoV-2 spike protein dynamics?

The SARS-CoV-2 spike protein (S) represents an important viral component that is required for successful viral infection in humans owing to its essential role in recognition of and entry to host cells. The spike is also an appealing target for drug designers who develop vaccines and antivirals. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. MD simulations found that the higher affinity of SARS-CoV-2-S to ACE2 is linked to its unique residues that add extra electrostatic and van der Waal interactions in comparison to the SARS-CoV S. This illustrates the spread potential of the pandemic SARS-CoV-2 relative to the epidemic SARS-CoV. Different mutations at the S-ACE2 interface, which is believed to increase the transmission of the new variants, affected the behavior and binding interactions in different simulations. The contributions of glycans to the opening of S were revealed via simulations. The immune evasion of S was linked to the spatial distribution of glycans. This help the virus to escape the immune system recognition. This article is important as it summarizes how molecular simulations successfully shaped our understanding of spike conformational behavior and its role in viral infection. This will pave the way to us preparing for the next pandemic as the computational tools are tailored to help fight new challenges.

Assessment of Different Parameters on the Accuracy of Computational Alanine Scanning of Protein-Protein Complexes with the Molecular Mechanics/Generalized Born Surface Area Method

Computational alanine scanning with the molecular mechanics generalized Born surface area (MM/GBSA) method constitutes a widely used approach for identifying critical residues at protein-protein interfaces. Despite its popularity, the MM/GBSA method still has certain drawbacks due to its dependence on many factors. Here, we performed a systematical study on the impact of four different parameters, namely, the internal dielectric constant, the generalized Born model, the entropic term, and the inclusion of structural waters on the accuracy of computational alanine scanning calculations with the MM/GBSA method. Our results show that the internal dielectric constant is the most critical parameter for getting accurate predictions. The introduction of entropy and interfacial water molecules decreased the quality of the predictions, while the generalized Born model had little to no effect. Considering the significance of the internal dielectric value, we proposed a methodology based on the energetic predominance of a particular set of amino acids at the protein-protein interface for selecting an appropriate value for this variable. We hope that these results serve as a guideline for future studies of protein-protein complexes using the MM/GBSA method.

Elucidating the molecular determinants in the process of gastrin C-terminal pentapeptide amide end activating cholecystokinin 2 receptor by Gaussian accelerated molecular dynamics simulations

Gastrin plays important role in stimulating the initiation and development of many gastrointestinal diseases through interacting with the cholecystokinin 2 receptor (CCK2R). The smallest bioactive unit of gastrin activating CCK2R is the C-terminal tetrapeptide capped with an indispensable amide end. Understanding the mechanism of this smallest bioactive unit interacting with CCK2R on a molecular basis could provide significant insights for designing CCK2R antagonists, which can be used to treat gastrin-related diseases. To this end, we performed extensive Gaussian accelerated molecular dynamics simulations to investigate the interaction between gastrin C-terminal pentapeptide capped with/without amide end and CCK2R. The amide cap influences the binding modes of the pentapeptide with CCK2R by weakening the electrostatic attractions between the C-terminus of the pentapeptide and basic residues near the extracellular domain in CCK2R. The C-terminus with the amide cap penetrates into the transmembrane domain of CCK2R while floating at the extracellular domain without the amide cap. Different binding modes induced different conformational dynamics of CCK2R. Residue pairs in CCK2R had stronger correlated motions when binding with the amidated pentapeptide. Key residues and interactions important for CCK2R binding with the amidated pentagastrin were also identified. Our results provide molecular insights into the determinants of the bioactive unit of gastrin activating CCK2R, which would be of great help for the design of CCK2R antagonists.

Atomic-level thermodynamics analysis of the binding free energy of SARS-CoV-2 neutralizing antibodies

Understanding how protein-protein binding affinity is determined from molecular interactions at the interface is essential in developing protein therapeutics such as antibodies, but this has not yet been fully achieved. Among the major difficulties are the facts that it is generally difficult to decompose thermodynamic quantities into contributions from individual molecular interactions and that the solvent effect-dehydration penalty-must also be taken into consideration for every contact formation at the binding interface. Here, we present an atomic-level thermodynamics analysis that overcomes these difficulties and illustrate its utility through application to SARS-CoV-2 neutralizing antibodies. Our analysis is based on the direct interaction energy computed from simulated antibody-protein complex structures and on the decomposition of solvation free energy change upon complex formation. We find that the formation of a single contact such as a hydrogen bond at the interface barely contributes to binding free energy due to the dehydration penalty. On the other hand, the simultaneous formation of multiple contacts between two interface residues favorably contributes to binding affinity. This is because the dehydration penalty is significantly alleviated: the total penalty for multiple contacts is smaller than a sum of what would be expected for individual dehydrations of those contacts. Our results thus provide a new perspective for designing protein therapeutics of improved binding affinity.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

Molecular dynamics study on the strengthening behavior of Delta and Omicron SARS-CoV-2 spike RBD improved receptor-binding affinity

The COVID-19 pandemic caused by a virus that can be transmitted from human to human via air droplets has changed the quality of life and economic systems all over the world. The viral DNA has mutated naturally over time leading to the diversity of coronavirus victims which has posed a serious threat to human security on a massive scale. The current variants have developed in a dominant way and are considered "Variants of Concern" by the World Health Organization (WHO). In this work, Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) variants were obtained to evaluate whether naturally occurring mutations have strengthened viral infectivity. We apply reliable in silico structural dynamics and energetic frameworks of the mutated S-RBD protein for ACE2-binding to analyze and compare the structural information related to the wild-type. In particular, the hotspot residues at Q493, Q498, and N501 on the S-RBD protein were determined as contributing factors to the employment stability of the relevant binding interface. The L452R mutation induces an increment of the hydrogen bonds formed by changing the Q493 environment for ACE2 binding. Moreover, the Q493K exchange in Omicron enables the formation of two additional salt bridges, leading to a strong binding affinity by increased electrostatic interaction energy. These results could be used in proposing concrete informative data for a structure-based design engaged in finding better therapeutics against novel variants.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

Integrated structure-based protein interface prediction

Background

Identifying protein interfaces can inform how proteins interact with their binding partners, uncover the regulatory mechanisms that control biological functions and guide the development of novel therapeutic agents. A variety of computational approaches have been developed for predicting a protein’s interfacial residues from its known sequence and structure. Methods using the known three-dimensional structures of proteins can be template-based or template-free. Template-based methods have limited success in predicting interfaces when homologues with known complex structures are not available to use as templates. The prediction performance of template-free methods that only rely only upon proteins’ intrinsic properties is limited by the amount of biologically relevant features that can be included in an interface prediction model.

Results

We describe the development of an integrated method for protein interface prediction (ISPIP) to explore the hypothesis that the efficacy of a computational prediction method of protein binding sites can be enhanced by using a combination of methods that rely on orthogonal structure-based properties of a query protein, combining and balancing both template-free and template-based features. ISPIP is a method that integrates these approaches through simple linear or logistic regression models and more complex decision tree models. On a diverse test set of 156 query proteins, ISPIP outperforms each of its individual classifiers in identifying protein binding interfaces.

Conclusions

The integrated method captures the best performance of individual classifiers and delivers an improved interface prediction. The method is robust and performs well even when one of the individual classifiers performs poorly on a particular query protein. This work demonstrates that integrating orthogonal methods that depend on different structural properties of proteins performs better at interface prediction than any individual classifier alone.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04852-2.

The chemistry of snake venom and its medicinal potential

The fascination and fear of snakes dates back to time immemorial, with the first scientific treatise on snakebite envenoming, the Brooklyn Medical Papyrus, dating from ancient Egypt. Owing to their lethality, snakes have often been associated with images of perfidy, treachery and death. However, snakes did not always have such negative connotations. The curative capacity of venom has been known since antiquity, also making the snake a symbol of pharmacy and medicine. Today, there is renewed interest in pursuing snake-venom-based therapies. This Review focuses on the chemistry of snake venom and the potential for venom to be exploited for medicinal purposes in the development of drugs. The mixture of toxins that constitute snake venom is examined, focusing on the molecular structure, chemical reactivity and target recognition of the most bioactive toxins, from which bioactive drugs might be developed. The design and working mechanisms of snake-venom-derived drugs are illustrated, and the strategies by which toxins are transformed into therapeutics are analysed. Finally, the challenges in realizing the immense curative potential of snake venom are discussed, and chemical strategies by which a plethora of new drugs could be derived from snake venom are proposed.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Inactive-to-Active Transition of Human Thymidine Kinase 1 Revealed by Molecular Dynamics Simulations

Despite its importance in the nucleoside (and nucleoside prodrug) metabolism, the structure of the active conformation of human thymidine kinase 1 (hTK1) remains elusive. We perform microsecond molecular dynamics simulations of the inactive enzyme form bound to a bisubstrate inhibitor that was shown experimentally to activate another TK1-like kinase, Thermotoga maritima TK (TmTK). Our results are in excellent agreement with the experimental findings for the TmTK closed-to-open state transition. We show that the inhibitor induces an increase of the enzyme radius of gyration due to the expansion on one of the dimer interfaces; the structural changes observed, including the active site pocket volume increase and the decrease in the monomer-monomer buried surface area and of the number of hydrogen bonds (as compared to the inactive enzyme control simulation), indicate that the catalytically competent (open) conformation of hTK1 can be assumed in the presence of an activating ligand.

Molecular rationale for SARS-CoV-2 spike circulating mutations able to escape bamlanivimab and etesevimab monoclonal antibodies

The purpose of this work is to provide an in silico molecular rationale of the role eventually played by currently circulating mutations in the receptor binding domain of the SARS-CoV-2 spike protein (S-RBDCoV‑2) in evading the immune surveillance effects elicited by the two Eli Lilly LY-CoV555/bamlanivimab and LY-CoV016/etesevimab monoclonal antibodies. The main findings from this study show that, compared to the wild-type SARS-CoV-2 spike protein, mutations E484A/G/K/Q/R/V, Q493K/L/R, S494A/P/R, L452R and F490S are predicted to be markedly resistant to neutralization by LY-CoV555, while mutations K417E/N/T, D420A/G/N, N460I/K/S/T, T415P, and Y489C/S are predicted to confer LY-CoV016 escaping advantage to the viral protein. A challenge of our global in silico results against relevant experimental data resulted in an overall 90% agreement. Thus, the results presented provide a molecular-based rationale for all relative experimental findings, constitute a fast and reliable tool for identifying and prioritizing all present and newly reported circulating spike SARS-CoV-2 variants with respect to antibody neutralization, and yield substantial structural information for the development of next-generation vaccines and monoclonal antibodies more resilient to viral evolution.

Probe into the Molecular Mechanism of Ibuprofen Interaction with Warfarin Bound to Human Serum Albumin in Comparison to Ascorbic and Salicylic Acids: Allosteric Inhibition of Anticoagulant Release

The release of anticoagulant drugs such as warfarin from human serum albumin (HSA) has been important not only mechanistically but also clinically for patients who take multiple drugs simultaneously. In this study, the role of some commonly used drugs, including s-ibuprofen, ascorbic acid, and salicylic acid, was investigated in the release of warfarin bound to HSA in silico. The effects of the aforementioned drugs on the HSA-warfarin complex were investigated with molecular dynamics (MD) simulations using two approaches; in the first perspective, molecular docking was used to model the interaction of each drug with the HSA-warfarin complex, and in the second approach, drugs were positioned randomly and distant from the binary complex (HSA-warfarin) in a physiologically relevant concentration. The results obtained from both approaches indicated that s-ibuprofen and ascorbic acid both displayed allosteric effects on the release of warfarin from HSA. Although ascorbic acid aided in warfarin release, leading to destabilization of HSA, ibuprofen demonstrated a stabilizing effect on releasing the anticoagulant drug through several noncovalent interactions, including hydrophobic, electrostatic, and hydrogen-bonding interactions with the protein. The calculated binding free energy and energy contribution of involved residues using the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA) method, along with root mean square deviation (RMSD) values, protein gyration, and free energy surface (FES) mapping of the protein, provided valuable details on the nature of the interactions of each drug on the release of warfarin from HSA. These results can provide important information on the mechanisms of anticoagulant release that has not been revealed in molecular details previously.

Quantitative analysis of ACE2 binding to coronavirus spike proteins: SARS-CoV-2 vs. SARS-CoV and RaTG13

The global outbreak of the COVID-19 pandemic is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Bat virus RaTG13 and SARS-CoV are also members of the coronavirus family and SARS-CoV caused a world-wide pandemic in 2003. SARS-CoV-2, SARS-CoV and RaTG13 bind to angiotensin-converting enzyme 2 (ACE2) through their receptor-binding domain (RBD) of the spike protein. SARS-CoV-2 binds ACE2 with a higher binding affinity than SARS-CoV and RaTG13. Here we performed molecular dynamics simulation of these binding complexes and calculated their binding free energies using a computational alanine scanning method. Our MD simulation and hotspot residue analysis showed that the lower binding affinity of SARS-CoV to ACE2 vs. SARS-CoV-2 to ACE2 can be explained by different hotspot interactions in these two systems. We also found that the lower binding affinity of RaTG13 to ACE2 is mainly due to a mutated residue (D501) which resulted in a less favorable complex formation for binding. We also calculated an important mutation of N501Y in SARS-CoV-2 using both alanine scanning calculation and a thermodynamic integration (TI) method. Both calculations confirmed a significant increase of the binding affinity of the N501Y mutant to ACE2 and explained its molecular mechanism. The present work provides an important theoretical basis for understanding the molecular mechanism in coronavirus spike protein binding to human ACE2.

Computational analysis of binding free energies, hotspots and the binding mechanism of Bcl-xL/Bcl-2 binding to Bad/Bax

Hierarchical clustering tree of residues providing contributions to system binding based on the binding free energy of specific residues for (A) Bcl-xL systems (B) Bcl-2 systems.

Computational Alanine Scanning and Structural Analysis of the SARS-CoV-2 Spike Protein/Angiotensin-Converting Enzyme 2 Complex

The recent emergence of the pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent for the coronavirus disease 2019 (COVID-19), is causing a global pandemic that poses enormous challenges to global public health and economies. SARS-CoV-2 host cell entry is mediated by the interaction of the viral transmembrane spike glycoprotein (S-protein) with the angiotensin-converting enzyme 2 gene (ACE2), an essential counter-regulatory carboxypeptidase of the renin-angiotensin hormone system that is a critical regulator of blood volume, systemic vascular resistance, and thus cardiovascular homeostasis. Accordingly, this work reports an atomistic-based, reliable in silico structural and energetic framework of the interactions between the receptor-binding domain of the SARS-CoV-2 S-protein and its host cellular receptor ACE2 that provides qualitative and quantitative insights into the main molecular determinants in virus/receptor recognition. In particular, residues D38, K31, E37, K353, and Y41 on ACE2 and Q498, T500, and R403 on the SARS-CoV-2 S-protein receptor-binding domain are determined as true hot spots, contributing to shaping and determining the stability of the relevant protein-protein interface. Overall, these results could be used to estimate the binding affinity of the viral protein to different allelic variants of ACE2 receptors discovered in COVID-19 patients and for the effective structure-based design and development of neutralizing antibodies, vaccines, and protein/protein inhibitors against this terrible new coronavirus.

Computational Investigation on the p53-MDM2 Interaction Using the Potential of Mean Force Study

Murine double minute 2 (MDM2) proteins are found to be overproduced by many human tumors in order to inhibit the functioning of p53 molecules, a tumor suppressor protein. Thus, reactivating p53 functioning in cancer cells by disrupting p53-MDM2 interactions may offer a significant approach in cancer treatment. However, the structural characterization of the p53-MDM2 complex at the atomistic level and the mechanism of binding/unbinding of the p53-MDM2 complex still remain unclear. Therefore, we demonstrate here the probable binding (unbinding) pathway of transactivation domain 1 of p53 during the formation (dissociation) of the p53-MDM2 complex in terms of free energy as a function of reaction coordinate from the potential of mean force (PMF) study using two different force fields: ff99SB and ff99SB-ILDN. From the PMF plot, we noticed the PMF to have a minimum value at a p53-MDM2 separation of 12 Å, with a dissociation energy of 30 kcal mol^-1. We also analyzed the conformational dynamics and stability of p53 as a function of its distance of separation from MDM2. The secondary structure content (helix and turns) in p53 was found to vary with its distance of separation from MDM2. The p53-MDM2 complex structure with lowest potential energy was isolated from the ensemble at the reaction coordinate corresponding to the minimum PMF value and subjected to molecular dynamics simulation to identify the interface surface area, interacting residues at the interface, and the stability of the complex. The simulation results highlight the importance of hydrogen bonds and the salt bridge between Lys94 of MDM2 and Glu17 of p53 in the stability of the p53-MDM2 complex. We also carried out the binding free energy calculations and the per residue energy decomposition analyses of the interface residues of the p53-MDM2 complex. We found that the binding affinity between MDM2 and p53 is indeed high [ΔG _bind = -7.29 kcal mol^-1 from molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) and ΔG _bind = -53.29 kcal mol^-1 from molecular mechanics/generalized borne surface area]. The total binding energy obtained using the MM/PBSA method was noticed to be closer to the experimental values (-6.4 to -9.0 kcal mol^-1). The p53-MDM2 complex binding profile was observed to follow the same trend even in the duplicate simulation run and also in the simulation carried out with different force fields. We found that Lys51, Leu54, Tyr100, and Tyr104 from MDM2 and the residues Phe19, Trp23, and Leu26 from p53 provide the highest energy contributions for the p53-MDM2 interaction. Our findings highlight the prominent structural and binding characteristics of the p53-MDM2 complex that may be useful in designing potential inhibitors to disrupt the p53-MDM2 interactions.

Molecular basis of SMAC-XIAP binding and the effect of electrostatic polarization

X-chromosome-linked inhibitor of apoptosis (XIAP) inhibits cell apoptosis. Overexpression of XIAP is widely found in human cancers. Second mitochondria-derived activator of caspase (SMAC) protein inhibits XIAP through binding with Baculovirus Inhibitor of apoptosis protein Repeat (BIR) 3 or BIR2 domain of XIAP. In this study, molecular dynamics (MD) simulations and the alanine scanning calculations by MM-GBSA_IE method were used to investigate the protein-peptide interaction between BIR3 and BIR2 domains of XIAP and SMAC peptide. Energetic contribution of each binding residue is calculated and hotspots on both XIAP and SMAC were identified using computational alanine scanning with interaction entropy method. We found that electrostatic polarization is important in stabilizing the protein-protein complex structure in MD simulation. By using polarized protein-specific charges, much better agreement with experimental result is obtained for calculated binding free energies compared to those using standard (nonpolarizable) AMBER force field. In particular, excellent correlation between calculated binding free energies in alanine scanning with mutational experimental data was obtained for BIR3/SMAC binding.Communicated by Ramaswamy H. Sarma.

SKEMPI 2.0: an updated benchmark of changes in protein-protein binding energy, kinetics and thermodynamics upon mutation

Motivation

Understanding the relationship between the sequence, structure, binding energy, binding kinetics and binding thermodynamics of protein–protein interactions is crucial to understanding cellular signaling, the assembly and regulation of molecular complexes, the mechanisms through which mutations lead to disease, and protein engineering.

Results

We present SKEMPI 2.0, a major update to our database of binding free energy changes upon mutation for structurally resolved protein–protein interactions. This version now contains manually curated binding data for 7085 mutations, an increase of 133%, including changes in kinetics for 1844 mutations, enthalpy and entropy changes for 443 mutations, and 440 mutations, which abolish detectable binding.

Availability and implementation

The database is available as supplementary data and at https://life.bsc.es/pid/skempi2/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Insight Into the Binding Mechanism of p53/pDIQ-MDMX/MDM2 With the Interaction Entropy Method

The study of the p53-MDMX/MDM2 binding sites is a research hotspot for tumor drug design. The inhibition of p53-targeted MDMX/MDM2 has become an effective approach in anti-tumor drug development. In this paper, a theoretically rigorous and computationally accurate method, namely, the interaction entropy (IE) method, combined with the polarized protein-specific charge (PPC) force field, is used to explore the difference in the binding mechanism between p53-MDMX and p53-MDM2. The interaction of a 12mer peptide inhibitor (pDIQ), which is similar to p53 in structure, with MDMX/MDM2 is also studied. The results demonstrate that p53/pDIQ with MDM2 generates a stronger interaction than with MDMX. Compared to p53, pDIQ has larger binding free energies with MDMX and MDM2. According to the calculated binding free energies, the differences in the binding free energy among the four complexes that are obtained from the combination of PPC and IE are more consistent with the experimental values than with the results from the combination of the non-polarizable AMBER force field and IE. In addition, according to the decomposition of the binding free energy, the van der Waals (vdW) interactions are the main driving force for the binding of the four complexes. They are also the main source of the weaker binding affinity of p53/pDIQ-MDMX relative to p53/pDIQ-MDM2. Compared with p53-MDMX/MDM2, according to the analysis of the residue decomposition, the predicated total residue contributions are higher in pDIQ-MDMX/MDM2 than in p53-MDMX/MDM2, which explains why pDIQ has higher binding affinity than p53 with MDMX/MDM2. The current study provides theoretical guidance for understanding the binding mechanisms and designing a potent dual inhibitor that is targeted to MDMX/MDM2.

Computational analysis of hot spots and binding mechanism in the PD-1/PD-L1 interaction

Programmed cell death protein-1 (PD-1) is an important immunological checkpoint and plays a vital role in maintaining the peripheral tolerance of the human body by interacting with its ligand PD-L1. The overexpression of PD-L1 in tumor cells induces local immune suppression and helps the tumor cells to evade the endogenous anti-tumor immunity. Developing monoclonal antibodies against the PD-1/PD-L1 protein–protein interaction to block the PD-1/PD-L1 signaling pathway has demonstrated superior anti-tumor efficacy in a variety of solid tumors and has made a profound impact on the field of cancer immunotherapy in recent years. Although the X-ray crystal structure of the PD-1/PD-L1 complex has been solved, the detailed binding mechanism of the PD-1/PD-L1 interaction is not fully understood from a theoretical point of view. In this study, we performed computational alanine scanning on the PD-1/PD-L1 complex to quantitatively identify the hot spots in the PD-1/PD-L1 interaction and characterize its binding mechanisms at the atomic level. To the best of our knowledge, this is the first time that theoretical calculations have been used to systematically and quantitatively predict the hot spots in the PD-1/PD-L1 interaction. We hope that the predicted hot spots and the energy profile of the PD-1/PD-L1 interaction presented in this work can provide guidance for the design of peptide and small molecule drugs targeting PD-1 or PD-L1.

The hot spots quantitatively predicted by the recently developed MM/GBSA/IE method reveal a hydrophobic core in the PD-1/PD-L1 interaction.

The impact of interior dielectric constant and entropic change on HIV-1 complex binding free energy prediction

At present, the calculated binding free energy obtained using the molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area (MM/PB(GB)SA) method is overestimated due to the lack of knowledge of suitable interior dielectric constants in the simulation on the interaction of Human Immunodeficiency Virus (HIV-1) protease systems with inhibitors. Therefore, the impact of different values of the interior dielectric constant and the entropic contribution when using the MM/PB(GB)SA method to calculate the binding free energy was systemically evaluated. Our results show that the use of higher interior dielectric constants (1.4-2.0) can clearly improve the predictive accuracy of the MM/PBSA and MM/GBSA methods, and computational errors are significantly reduced by including the effects of electronic polarization and using a new highly efficient interaction entropy (IE) method to calculate the entropic contribution. The suitable range for the interior dielectric constant is 1.4-1.6 for the MM/PBSA method; within this range, the correlation coefficient fluctuates around 0.84, and the mean absolute error fluctuates around 2 kcal/mol. Similarly, an interior dielectric constant of 1.8-2.0 produces a correlation coefficient of approximately 0.76 when using the MM/GBSA method. In addition, the entropic contribution of each individual residue was further calculated using the IE method to predict hot-spot residues, and the detailed binding mechanisms underlying the interactions of the HIV-1 protease, its inhibitors, and bridging water molecules were investigated. In this study, the use of a higher interior dielectric constant and the IE method can improve the calculation accuracy of the HIV-1 system.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction

Background

The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task.

Results

In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI).

Conclusions

The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2043-3) contains supplementary material, which is available to authorized users.

Recent Developments and Applications of the MMPBSA Method

The Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach has been widely applied as an efficient and reliable free energy simulation method to model molecular recognition, such as for protein-ligand binding interactions. In this review, we focus on recent developments and applications of the MMPBSA method. The methodology review covers solvation terms, the entropy term, extensions to membrane proteins and high-speed screening, and new automation toolkits. Recent applications in various important biomedical and chemical fields are also reviewed. We conclude with a few future directions aimed at making MMPBSA a more robust and efficient method.

Saturation Mutagenesis by Efficient Free-Energy Calculation

Single-point mutations in proteins can greatly influence protein stability, binding affinity, protein function or its expression per se. Here, we present accurate and efficient predictions of the free energy of mutation of amino acids. We divided the complete mutational free energy into an uncharging step, which we approximate by a third-power fitting (TPF) approach, and an annihilation step, which we approximate using the one-step perturbation (OSP) method. As a diverse set of test systems, we computed the solvation free energy of all amino acid side chain analogues and obtained an excellent agreement with thermodynamic integration (TI) data. Moreover, we calculated mutational free energies in model tripeptides and established an efficient protocol involving a single reference state. Again, the approximate methods agreed excellently with the TI references, with a root-mean-square error of only 3.6 kJ/mol over 17 mutations. Our combined TPF+OSP approach does show not only a very good agreement but also a 2-fold higher efficiency than full blown TI calculations.

Properties that rank protein:protein docking poses with high accuracy

The development of docking algorithms to predict near-native structures of protein:protein complexes from the structure of the isolated monomers is of paramount importance for molecular biology and drug discovery.