Interaction Entropy for Computational Alanine Scanning

Identification of FTY720 and COH29 as novel topoisomerase I catalytic inhibitors by experimental and computational studies

The development of novel topoisomerase I (TOP1) inhibitors is crucial for overcoming the drawbacks and limitations of current TOP1 poisons. Here, we identified two potential TOP1 inhibitors, namely, FTY720 (a sphingosine 1-phosphate antagonist) and COH29 (a ribonucleotide reductase inhibitor), through experimental screening of known active compounds. Biological experiments verified that FTY720 and COH29 were nonintercalative TOP1 catalytic inhibitors that did not induce the formation of DNA-TOP1 covalent complexes. Molecular docking revealed that FTY720 and COH29 interacted favorably with TOP1. Molecular dynamics simulations revealed that FTY720 and COH29 could affect the catalytic domain of TOP1, thus resulting in altered DNA-binding cavity size. The alanine scanning and interaction entropy identified Arg536 as a hotspot residue. In addition, the bioinformatics analysis predicted that FTY720 and COH29 could be effective in treating malignant breast tumors. Biological experiments verified their antitumor activities using MCF-7 breast cancer cells. Their combinatory effects with TOP1 poisons were also investigated. Further, FTY720 and COH29 were found to cause less DNA damage compared with TOP1 poisons. The findings provide reliable lead compounds for the development of novel TOP1 catalytic inhibitors and offer new insights into the potential clinical applications of FTY720 and COH29 in targeting TOP1.

Evaluating mAbs binding abilities to Omicron subvariant RBDs: implications for selecting effective mAb therapies

The ongoing evolution of the Omicron lineage of SARS-CoV-2 has led to the emergence of subvariants that pose challenges to antibody neutralization. Understanding the binding dynamics between the receptor-binding domains (RBD) of these subvariants spike and monoclonal antibodies (mAbs) is pivotal for elucidating the mechanisms of immune escape and for advancing the development of therapeutic antibodies. This study focused on the RBD regions of Omicron subvariants BA.2, BA.5, BF.7, and XBB.1.5, employing molecular dynamics simulations to unravel their binding mechanisms with a panel of six mAbs, and subsequently analyzing the origins of immune escape from energetic and structural perspectives. Our results indicated that the antibody LY-COV1404 maintained binding affinities across all studied systems, suggesting the resilience of certain antibodies against variant-induced immune escape, as seen with the mAb 1D1-Fab. The newly identified mAb 002-S21F2 showed a similar efficacy profile to LY-COV1404, though with a slightly reduced binding to BF.7. In parallel, mAb REGN-10933 emerged as a potential therapeutic candidate against BF.7 and XBB.1.5, reflecting the importance of identifying variant-specific antibody interactions, akin to the binding optimization observed in BA.4/5 and XBB.1.5. And key residues that facilitate RBD-mAb binding were identified (T345, L441, K444, V445, and T500), alongside residues that hinder protein-protein interactions (D420, L455, K440, and S446). Particularly noteworthy was the inhibited binding of V445 and R509 with mAbs in the presence of mAb 002-S21F2, suggesting a mechanism for immune escape, especially through the reduction of V445 hydrophobicity. These findings enhance our comprehension of the binding interactions between mAbs and RBDs, contributing to the understanding of immune escape mechanisms. They also lay the groundwork for the design and optimization of antiviral drugs and have significant implications for the development of treatments against current and future coronaviruses.

Computational analysis of PD-L1 dimerization mechanism induced by small molecules and potential dynamical properties

The design of small molecule inhibitors that target the programmed death ligand-1 (PD-L1) is a forefront issue in immune checkpoint blocking therapy. Small-molecule inhibitors have been shown to exert therapeutic effects by inducing dimerization of the PD-L1 protein, however, the specific mechanisms underlying this dimerization process remain largely unexplored. Furthermore, there is a notable lack of comparative studies examining the binding modes of structurally diverse inhibitors. In view of the research gaps, this work employed molecular dynamics simulations to meticulously examine the interactions between two distinct types of inhibitors and PD-L1 in both monomeric and dimeric forms, and predicted the dimerization mechanism. The results revealed that inhibitors initially bind to a PD-L1 monomer, subsequently attracting another monomer to form a dimer. Notably, symmetric inhibitors observed superior binding efficiency compared to other inhibitors. Key residues, including Ile54, Tyr56, Met115 and Tyr123 played a leading role in binding. Structurally, symmetric inhibitors were capable of thoroughly engaging the binding pocket, promoting a more symmetrical formation of PD-L1 dimers. Furthermore, symmetric inhibitors formed more extensive hydrophobic interactions with protein residues. The insights garnered from this research are expected to significantly contribute to the rational design and optimization of small molecule inhibitors targeting PD-L1.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Advanced molecular mechanisms of modified DRV compounds in targeting HIV-1 protease mutations and interrupting monomer dimerization

HIV-1 protease (PR) plays a crucial role in the treatment of HIV as a key target. The global issue of emerging drug resistance is escalating, and PR mutations pose a substantial challenge to the effectiveness of inhibitors. HIV-1 PR is an ideal model for studying drug resistance to inhibitors. The inhibitor, darunavir (DRV), exhibits a high genetic barrier to viral resistance, but with mutations of residues in the PR, there is also some resistance to DRV. Inhibitors can impede PR in two ways: one involves binding to the active site of the dimerization protease, and the other involves binding to the PR monomer, thereby preventing dimerization. In this study, we aimed to investigate the inhibitory effect of DRV with a modified inhibitor on PR, comparing the differences between wild-type and mutated PR, using molecular dynamics simulations. The inhibitory effect of the inhibitors on PR monomers was subsequently investigated. And molecular mechanics Poisson-Boltzmann surface area evaluated the binding free energy. The energy contribution of individual residues in the complex was accurately calculated by the alanine scanning binding interaction entropy method. The results showed that these inhibitors had strong inhibitory effects against PR mutations, with GRL-142 exhibiting potent inhibition of both the PR monomer and dimer. Improved inhibitors could strengthen hydrogen bonds and interactions with PR, thereby boosting inhibition efficacy. The binding of the inhibitor and mutation of the PR affected the distance between D25 and I50, preventing their dimerization and the development of drug resistance. This study could accelerate research targeting HIV-1 PR inhibitors and help to further facilitate drug design targeting both mechanisms.

Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease

Papain-like protease (PLpro), a non-structural protein encoded by SARS-CoV-2, is an important therapeutic target. Regions 1 and 5 of an existing drug, GRL0617, can be optimized to produce cooperativity with PLpro binding, resulting in stronger binding affinity. This work investigated the origin of the cooperativity using molecular dynamics simulations combined with the interaction entropy (IE) method. The regions' improvement exhibits cooperativity by calculating the binding free energies between the complex of PLpro-inhibitor. The thermodynamic integration method further verified the cooperativity generated in the drug improvement. To further determine the specific source of cooperativity, enthalpy and entropy in the complexes were calculated using molecular mechanics/generalized Born surface area and IE. The results show that the entropic change is an important contributor to the cooperativity. Our study also identified residues P248, Q269, and T301 that play a significant role in cooperativity. The optimization of the inhibitor stabilizes these residues and minimizes the entropic loss, and the cooperativity observed in the binding free energy can be attributed to the change in the entropic contribution of these residues. Based on our research, the application of cooperativity can facilitate drug optimization, and provide theoretical ideas for drug development that leverage cooperativity by reducing the contribution of entropy through multi-locus binding.

The molecular mechanism of non-covalent inhibitor WU-04 targeting SARS-CoV-2 3CLpro and computational evaluation of its effectiveness against mainstream coronaviruses

There is an urgent need for highly effective therapeutic agents to interrupt the continued spread of SARS-CoV-2. As a pivotal protease in the replication process of coronaviruses, the 3CLpro protein is considered as a potential target of drug development to stop the spread and infection of the virus. In this work, molecular dynamics (MD) simulations were used to elucidate the molecular mechanism of a novel and highly effective non-covalent inhibitor, WU-04, targeting the SARS-CoV-2 3CLpro protein. The difference in dynamic behavior between the apo-3CLpro and the holo-3CLpro systems suggests that the presence of WU-04 inhibits the motion amplitude of the 3CLpro protein relative to the apo-3CLpro system, thus maintaining a stable conformational binding state. The energy calculations and interaction analysis show that the hot-spot residues Q189, M165, M49, E166, and H41 and the warm-spot residues H163 and C145 have a strong binding capacity to WU-04 by forming multiple hydrogen bonds and hydrophobic interactions, which stabilizes the binding of the inhibitor. After that, the resistance of WU-04 to the six SARS-CoV-2 variants (Alpha, Beta, Gamma, Delta, Lambda, and Omicron) and two other mainstream coronavirus (SARS-CoV and MERS-CoV) 3CLpro proteins was further investigated. Excitingly, the slight difference in energy values relative to the SARS-CoV-2 system indicates that WU-04 is still highly effective against the coronaviruses, which becomes crucial evidence that WU-04 is a pan-inhibitor of the 3CLpro protein in various SARS-CoV-2 variants and other mainstream coronaviruses. The study will hopefully provide theoretical insights for the future rational design and improvement of novel non-covalent inhibitors targeting the 3CLpro protein.

Comprehensive analysis unveils altered binding kinetics of 5-/6-methylCytosine/adenine modifications in R2R3-DNA system

Recent studies have shown that DNA methylation is an important epigenetic marker. Two prominent forms are methylation of the C5 position of cytosine and methylation of the C6 position of adenine. Given the vital significance of DNA methylation, investigating the mechanisms that influence protein binding remains a compelling pursuit. This study used molecular dynamics simulations to investigate the binding patterns of R2R3 protein and four differentially methylated DNAs. The alanine scanning combined with interaction entropy method was used to identify key residues that respond to different methylation patterns. The order of protein binding ability to DNA is as follows: unmethylated DNA > A11 methylation (5'-A6mAC-3') (6m2A system) > A10 methylation (5'-6mAAC-3') (6m1A system) > both A10 and A11 methylation (5'-6mA6mAC-3') (6mAA system) > C12 methylation (5'-AA5mC-3') (5mC system). All methylation systems lead to the sixth α helix (H6) (residues D105 to L116) moving away from the binding interface, and in the 5mC and 6m1A systems, the third α helix (H3) (residues G54 to L65) exhibits a similar trend. When the positively charged amino acids in H3 and H6 move away from the binding interface, their electrostatic and van der Waals interactions with the negatively charged DNA are weakened. Structural changes induced by methylation contributed to the destabilization of the hydrogen bond network near the original binding site, except for the 6m2A system. Moreover, there is a positive correlation between the number of methylated sites and the probability of distorting the DNA structure. Our study explores how different methylation patterns affect binding and structural adaptability, and have implications for drug discovery and understanding diseases related to abnormal methylation.

Quantitative investigation of the effects of DNA modifications and protein mutations on MeCP2-MBD-DNA interactions

DNA methylation as an important epigenetic marker, has gained attention for the significance of three oxidative modifications (hydroxymethyl-C (hmC), formyl-C (fC), and carboxyl-C (caC)). Mutations occurring in the methyl-CpG-binding domain (MBD) of MeCP2 result in Rett. However, uncertainties persist regarding DNA modification and MBD mutation-induced interaction changes. Here, molecular dynamics simulations were used to investigate the underlying mechanisms behind changes due to different modifications of DNA and MBD mutations. Alanine scanning combined with the interaction entropy method was employed to accurately evaluate the binding free energy. The results show that MBD has the strongest binding ability for mCDNA, followed by caC, hmC, and fCDNA, with the weakest binding ability observed for CDNA. Further analysis revealed that mC modification induces DNA bending, causing residues R91 and R162 closer to the DNA. This proximity enhances van der Waals and electrostatic interactions. Conversely, the caC/hmC and fC modifications lead to two loop regions (near K112 and K130) closer to DNA, respectively. Furthermore, DNA modifications promote the formation of stable hydrogen bond networks, however mutations in the MBD significantly reduce the binding free energy. This study provides detailed insight into the effects of DNA modifications and MBD mutations on binding ability. It emphasizes the necessity for research and development of targeted Rett compounds that induce conformational compatibility between MBD and DNA, enhancing the stability and strength of their interactions.

Combined Antibodies Evusheld against the SARS-CoV-2 Omicron Variants BA.1.1 and BA.5: Immune Escape Mechanism from Molecular Simulation

The Omicron lineage of SARS-CoV-2, which was first reported in November 2021, has spread globally and become dominant, splitting into several sublineages. Experiments have shown that Omicron lineage has escaped or reduced the activity of existing monoclonal antibodies, but the origin of escape mechanism caused by mutation is still unknown. This work uses molecular dynamics and umbrella sampling methods to reveal the escape mechanism of BA.1.1 to monoclonal antibody (mAb) Tixagevimab (AZD1061) and BA.5 to mAb Cilgavimab (AZD8895), both mAbs were combined to form antibody cocktail, Evusheld (AZD7442). The binding free energy of BA.1.1-AZD1061 and BA.5-AZD8895 has been severely reduced due to multiple-site mutated Omicron variants. Our results show that the two Omicron variants, which introduce a substantial number of positively charged residues, can weaken the electrostatic attraction between the receptor binding domain (RBD) and AZD7442, thus leading to a decrease in affinity. Additionally, using umbrella sampling along dissociation pathway, we found that the two Omicron variants severely impaired the interaction between the RBD of SARS-CoV-2's spike glycoprotein (S protein) and complementary determining regions (CDRs) of mAbs, especially in CDR3H. Although mAbs AZD8895 and AZD1061 are knocked out by BA.5 and BA.1.1, respectively, our results confirm that the antibody cocktail AZD7442 retains activity against BA.1.1 and BA.5 because another antibody is still on guard. The study provides theoretical insights for mAbs interacting with BA.1.1 and BA.5 from both energetic and dynamic perspectives, and we hope this will help in developing new monoclonals and combinations to protect those unable to mount adequate vaccine responses.

An Attempt of Seeking Favorable Binding Free Energy Prediction Schemes Considering the Entropic Effect on Fis-DNA Binding

Protein-DNA binding mechanisms in a complex manner are essential for understanding many biological processes. Over the past decades, numerous experiments and calculations have analyzed the specificity of protein-DNA binding. However, the accuracy of binding free energy prediction for multi-base DNA systems still needs to be improved. Fis is a DNA-binding protein that regulates various transcription and recombination reactions. In the present work, we tested several methods of predict binding free energy based on this system to find a favorable prediction scheme and explore the binding mechanism of Fis protein and DNA. Two solvent models (explicit and implicit solvent models) were chosen for the dynamics process, and the predicted binding free energy was more accurate under the explicit solvent model. When different Poisson-Boltzmann/Generalized Born (PB/GB) models were tested for DNA force fields (BSC1 and OL15), it was found that the binding free energy predicted by the selected OL15 force field performed better and the correlation between predicted and experimental values was improved with the increasing interior dielectric constant (Dk). Finally, using Dk = 8, the GB^OBC1 model combined with interaction entropy (IE), which was calculated for entropic contribution (GB^OBC1_IE_8), was screened out for the binding free energy prediction and analysis of the Fis-DNA system, and the validity of the method was further verified by testing the Cren7-DNA system. By performing conformational analysis of the minor groove, it was found that mutation of the DNA central sequence A/T to C/G and deletion of the guanine 2-amino group would change the minor groove width and thus affect the formation of the major groove, altering the interaction and atomic contact between the protein and the major groove, thus changing the binding affinity of Fis and DNA. Hopefully, the series of tests in this work can shed some light on the related studies of protein and DNA systems.

Predicting binding affinity changes from long-distance mutations using molecular dynamics simulations and Rosetta

Computationally modeling how mutations affect protein-protein binding not only helps uncover the biophysics of protein interfaces, but also enables the redesign and optimization of protein interactions. Traditional high-throughput methods for estimating binding free energy changes are currently limited to mutations directly at the interface due to difficulties in accurately modeling how long-distance mutations propagate their effects through the protein structure. However, the modeling and design of such mutations is of substantial interest as it allows for greater control and flexibility in protein design applications. We have developed a method that combines high-throughput Rosetta-based side-chain optimization with conformational sampling using classical molecular dynamics simulations, finding significant improvements in our ability to accurately predict long-distance mutational perturbations to protein binding. Our approach uses an analytical framework grounded in alchemical free energy calculations while enabling exploration of a vastly larger sequence space. When comparing to experimental data, we find that our method can predict internal long-distance mutational perturbations with a level of accuracy similar to that of traditional methods in predicting the effects of mutations at the protein-protein interface. This work represents a new and generalizable approach to optimize protein free energy landscapes for desired biological functions.

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.

Discovery of novel inhibitors of SARS-CoV-2 main protease

Corona Virus Disease 2019 (COVID-19), referred to as 'New Coronary Pneumonia', is a type of acute infectious disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. M^pro is one of the main targets for treating COVID-19. The current research on M^pro mainly focuses on the repurposing of old drugs, and there are only a few novel ligands that inhibit M^pro. In this research, we used computational free energy calculation to screen a compound library against M^pro, and discovered four novel compounds with the two best compounds (AG-690/13507628 and AG-690/13507724) having experimental measured IC₅₀ of just under 3 μM and low cell toxicity. Detailed decomposition of the interactions between the inhibitors and M^pro reveals key interacting residues and interactions that determine the activity. The results from this study should provide a basis for further development of anti-SARS-CoV-2 drugs.Communicated by Ramaswamy H. Sarma.

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 Mechanism of the Non-Covalent Orally Targeted SARS-CoV-2 Mpro Inhibitor S-217622 and Computational Assessment of Its Effectiveness against Mainstream Variants

Convenient and efficient therapeutic agents are urgently needed to block the continued spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here, the mechanism for the novel orally targeted SARS-CoV-2 main protease (M^pro) inhibitor S-217622 is revealed through a molecular dynamics simulation. The difference in the movement modes of the S-217622-M^pro complex and apo-M^pro suggested S-217622 could inhibit the motility intensity of M^pro, thus maintaining their stable binding. Subsequent energy calculations showed that the P2 pharmacophore possessed the highest energy contribution among the three pharmacophores of S-217622. Additionally, hot-spot residues H41, M165, C145, E166, and H163 have strong interactions with S-217622. To further investigate the resistance of S-217622 to six mainstream variants, the binding modes of S-217622 with these variants were elucidated. The subtle differences in energy compared to that of the wild type implied that the binding patterns of these systems were similar, and S-217622 still inhibited these variants. We hope this work will provide theoretical insights for optimizing novel targeted M^pro drugs.

Recognition between CD147 and cyclophilin A deciphered by accelerated molecular dynamics simulations

CD147 functions as the receptor of extracellular cyclophilin A (CypA) in various diseases, and CD147-CypA binding ulteriorly underlies the pathological process of various viral infections including HIV-1, SARS, and SARS-CoV-2. Although CyPA has been identified as a key intermediate pro-inflammatory factor, the mechanism by which CD147 cooperates with CypA in the development of the cytokine storm remains largely unknown, and the binding profile of CD147 with CypA remains to be elucidated as well. Here, we prepared three binding models of the CD147-CypA complex, including the active site of CypA severally binding to the groove bound by the Ig1 and Ig2 domains (model-0), P180-G181 (model-1), and P211 (model-2) of CD147, as well as introducing mutations P180A-G181A and P211A individually in each model. All systems were studied using accelerated molecular dynamics simulations and the molecular mechanics generalized Born surface area (MM/GBSA) method. For model-0, CypA bound to the ectodomain of CD147 with the highest binding affinity. Moreover, mutations P180A-G181A of CD147 in model-0 decreased the binding affinity and weakened the dynamic correlation between CD147 and CypA, which resulted in CypA shifting from the initial binding location. Other residue mutations of CD147 did not significantly affect the CD147-CypA binding, as reflected by the energy and structural analyses. Compared with surface plasmon resonance results and nuclear magnetic resonance shift signals, CypA should tend to reciprocally bind to the groove of CD147, and the binding process might be modulated by P180-G181 rather than P211. Besides, residue R201 of CD147 is critical for CD147-CypA binding and needs further experimental verification. These findings further our understanding of the recruitment between CD147 and CypA and its potential role in the development of inflammation and viral infection.

Immune Escape Mechanisms of SARS-CoV-2 Delta and Omicron Variants against Two Monoclonal Antibodies That Received Emergency Use Authorization

Multiple-site mutated SARS-CoV-2 Delta and Omicron variants may trigger immune escape against existing monoclonal antibodies. Here, molecular dynamics simulations combined with the interaction entropy method reveal the escape mechanism of Delta/Omicron variants to Bamlanivimab/Etesevimab. The result shows the significantly reduced binding affinity of the Omicron variant for both antibodies, due to the introduction of positively charged residues that greatly weaken their electrostatic interactions. Meanwhile, significant structural deflection induces fewer atomic contacts and an unstable binding mode. As for the Delta variant, the reduced binding affinity for Bamlanivimab is owing to the alienation of the receptor-binding domain to the main part of this antibody, and the binding mode of the Delta variant to Etesevimab is similar to that of the wild type, suggesting that Etesevimab could still be effective against the Delta variant. We hope this work will provide timely theoretical insights into developing antibodies to prevalent and possible future variants of SARS-CoV-2.

Molecular investigation of the dual inhibition mechanism for targeted P53 regulator MDM2/MDMX inhibitors

Inhibitors that competitively bind MDM2/MDMX can block the inhibition of P53 by MDM2/MDMX and restart its tumor-suppressive effect. Molecular studies targeting MDM2/MDMX inhibitors have always been a hot topic in anticancer drug design. Although numerous inhibitors have been designed previously against MDM2/MDMX, their dual inhibition efficacy has not been demonstrated, and few studies assessed the general causes affecting the dual inhibition of MDM2/MDMX by these inhibitors. Here, molecular dynamics simulations and alanine scanning combined with the interaction entropy method were employed to precisely investigate whether 16 inhibitors could dually inhibit MDM2/MDMX and the similarities and differences in the interaction modes. Thereby addressing the key residue sites affecting dual inhibition. Residues L54/M53, I61/60, M62/61, Y67/66, and V93/92 of MDM2/MDMX, which are in corresponding positions in both protein structures, provide significant conditions for these inhibitors to bind to MDM2/MDMX tightly. In addition, most of these inhibitors prefer to bind MDM2 than MDMX, and residues H96 and I99 in MDM2 are attractive targets for inhibitors, resulting in inhibitors binding to MDM2/MDMX with different affinity. These key residues should be considered in the development of dual inhibitors. For these 16 inhibitors, most have dual inhibitory potential for MDM2/MDMX based on the binding affinity of the complexes. Still, it is questionable whether they can exert excellent dual inhibition considering the assessment of the hot-spots. At least their binding affinity for MDMX is not superior to that for MDM2 due to the difference in energy of the van der Waals interactions at the key sites. Furthermore, based on the analysis of three representative inhibitors (TUZ/HRH and HRQ with different binding preferences for MDM2/MDMX), 3-chloropyridine in TUZ leads to the differential binding affinity between the inhibitor and MDM2/MDMX. It readily forms hydrophobic interactions with the surrounding residues H96 and I99. But this phenomenon does not occur in the TUZ-MDMX system, implying the critical role of residues H96/P95 and I99/L98. And the completely different binding mechanism of HRQ binding to MDM2/MDMX explains its inability to inhibit MDM2 well. Thus, we are cautious about its dual inhibitory ability. Besides, HRH is more prone to strong van der Waals interactions with MDM2 than MDMX whereas its 2-chlorofluorobenzene is detrimental to this. We hope that these findings will provide reliable molecular insights for the screening and optimization of targeting MDM2/MDMX dual inhibitors.

AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

The protein-ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein-ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, π-stacking, π-cation, and metal-ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein-ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Origin of the tight binding mode to ACE2 triggered by multi-point mutations in the omicron variant: a dynamic insight

The continuous spread of the newly emerged SARS-CoV-2 Omicron variant (B.1.1.529) has become an important reason for the surge in COVID-19 infections. Its numerous mutated residues containing key sites on the receptor-binding domain (RBD) undoubtedly pose new challenges for epidemic control. Although the preventive measures are becoming more sophisticated, the effects of mutations on the binding of the virus to the receptor protein remain to be elucidated. Here, we used molecular dynamics (MD) simulations to investigate the differences in the binding mode between the Omicron variant and the angiotensin-converting enzyme 2 (ACE2) compared to the wild-type strain (WT). Multi-point mutations in the Omicron variant RBD could cause the conformation shift in the large Loop (where T478K and E484A are located), which makes it easier to wrap the N-terminal helix of ACE2 and form tighter contacts. The stronger electrostatic interaction was the main reason for its enhanced binding affinity as compared to WT. This was due to the large number of positively charged patches (N440K, T478K, Q493R, Q498R, and Y505H) formed by the substitution of neutral amino acids at multiple sites. The appearance of these highly polar hydrophilic amino acids may cause local perturbations and affect the electrostatic complementarity of RBD with the ACE2, and further mediate conformational changes. Thus, a more extensive interaction network was found in the mutation system and the complex interaction cluster was formed near E37@ACE2, which was essential for the stable binding of the two. In addition, we speculated that these mutations may affect the electrostatic complementarity with the four potential antibodies to reduce the sensitivity of the virus to antibodies. This study reveals the key details of the Omicron variant binding to ACE2 and provides important theoretical views for the enhanced infectivity of this variant. We hope that these observations can provide timely molecular insights for responding to the Omicron variant pandemic.

Mutational Effect of Some Major COVID-19 Variants on Binding of the S Protein to ACE2

COVID-19 is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has many variants that accelerated the spread of the virus. In this study, we investigated the quantitative effect of some major mutants of the spike protein of SARS-CoV-2 binding to the human angiotensin-converting enzyme 2 (ACE2). These mutations are directly related to the Variant of Concern (VOC) including Alpha, Beta, Gamma, Delta and Omicron. Our calculations show that five major mutations (N501Y, E484K, L452R, T478K and K417N), first reported in Alpha, Beta, Gamma and Delta variants, all increase the binding of the S protein to ACE2 (except K417N), consistent with the experimental findings. We also studied an additional eight mutations of the Omicron variant that are located on the interface of the receptor binding domain (RDB) and have not been reported in other VOCs. Our study showed that most of these mutations (except Y505H and G446S) enhance the binding of the S protein to ACE2. The computational predictions helped explain why the Omicron variant quickly became dominant worldwide. Finally, comparison of several different computational methods for binding free energy calculation of these mutants was made. The alanine scanning method used in the current calculation helped to elucidate the residue-specific interactions responsible for the enhanced binding affinities of the mutants. The results show that the ASGB (alanine scanning with generalized Born) method is an efficient and reliable method for these binding free energy calculations due to mutations.

Discovery of Novel Inhibitors of CDK2 Using Docking and Physics-based Binding Free Energy Calculation

Cyclin-dependent kinase (CDK) is a serine/threonine protein kinase family that cooperates with cyclin and plays an important role in the regulation of cell cycle. Cyclin-dependent kinase 2 is an important member of the CDK family and holds great promise as an anti-cancer drug target. In this study, we used molecular docking and physics-based binding free energy calculation method AS-IE that explicitly calculated protein-ligand binding entropy to discover novel inhibitors of CDK2. A total of 17 inhibitors were discovered with the best IC₅₀ reaching ~2 μM. Decomposition of the binding free energy using AS-IE reveals key protein-ligand interactions that determines the activity. These results provided a good example of drug design using physics-based free energy calculation method such as AS-IE and the novel compounds offered a good start point for further development of CDK2 inhibitors.

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.

Insights from computational analysis: how does the SARS-CoV-2 Delta (B.1.617.2) variant hijack ACE2 more effectively?

The SARS-CoV-2 Delta (B.1.617.2) variant was identified in India in October 2020, and it has quickly become the mainstream strain with strong toxicity and spread, posing great challenges to epidemic...

gmx_MMPBSA: A New Tool to Perform End-State Free Energy Calculations with GROMACS

Molecular mechanics/Poisson-Boltzmann (Generalized-Born) surface area is one of the most popular methods to estimate binding free energies. This method has been proven to balance accuracy and computational efficiency, especially when dealing with large systems. As a result of its popularity, several programs have been developed for performing MM/PB(GB)SA calculations within the GROMACS community. These programs, however, present several limitations. Here we present gmx_MMPBSA, a new tool to perform end-state free energy calculations from GROMACS molecular dynamics trajectories. gmx_MMPBSA provides the user with several options, including binding free energy calculations with different solvation models (PB, GB, or 3D-RISM), stability calculations, computational alanine scanning, entropy corrections, and binding free energy decomposition. Noteworthy, several promising methodologies to calculate relative binding free energies such as alanine scanning with variable dielectric constant and interaction entropy have also been implemented in gmx_MMPBSA. Two additional tools-gmx_MMPBSA_test and gmx_MMPBSA_ana-have been integrated within gmx_MMPBSA to improve its usability. Multiple illustrating examples can be accessed through gmx_MMPBSA_test, while gmx_MMPBSA_ana provides fast, easy, and efficient access to different graphics plotted from gmx_MMPBSA output files. The latest version (v1.4.3, 26/05/2021) is available free of charge (documentation, test files, and tutorials included) at https://github.com/Valdes-Tresanco-MS/gmx_MMPBSA.

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.

Difference in the binding mechanisms of ABT-263/43b with Bcl-xL/Bcl-2: computational perspective on the accurate binding free energy analysis

B-cell lymphoma/leukemia gene-2(Bcl-2) protein family known for regulating cell cycle arrest and subsequent cell death is highly expressed in a variety of cancers. Among them, the Bcl-xL and Bcl-2 are two essential proteins in the Bcl-2 family. In the present work, the differences in binding modes as between the two proteins and two ligands ABT-263/43b were investigated and compared. And the computational alanine scanning combined with the recently developed interaction entropy (AS-IE) method was employed for predicting their binding free energies and finding those amino acids that were more critical during the binding process. The result showed that the binding free energy calculated by the AS-IE method was more in line with experimental values than the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method. Besides, no significant difference was found between Bcl-xL and ABT-263/43b in the binding free energy, which Bcl-xL showed slightly weaker binding free energy to 43b because of the fewer number of key residues with interactions. Nonetheless, compared with the Bcl-2 and 43b complex, the Bcl-2 and ABT-263 system had greater number of key residues interacting with ABT-263, in particular, contribute favorably, resulting in a stronger binding ability for the Bcl-2 and ABT-263 systems. The van der Waals and hydrogen bond contributions were significant in the four protein-ligand complexes. Overall, Tyr108 was found to be the common key residues in the Bcl-xL-ligand complex, while Tyr105, Glu100, and Glu143 were established as the common key residue in the Bcl-2-ligand systems. We hope that the predicted hot spot residues and their energy distributions can guide the design of peptide and small-molecule drugs targeting Bcl-xL and Bcl-2.

Residue-specific binding mechanisms of PD-L1 to its monoclonal antibodies by computational alanine scanning

Programmed cell death 1 receptor (PD-1) on the surface of T cells and its ligand 1 (PD-L1) are immune checkpoint proteins. Treating cancer patients with inhibitors blocking this checkpoint has significantly prolonged the survival rate of patients. In this study, we examined several monoclonal antibodies (mAbs) of PD-L1 and studied their detailed binding mechanism to PD-L1. An efficient computational alanine scanning method was used to perform quantitative analysis of hotspot residues that are important for PD-1/PD-L1 binding. A total of five PD-L1/mAb complexes were investigated and hotspots on both PD-L1 and mAbs were predicted. Our result shows that PD-L1M115 and PD-L1Y123 are two relatively important hotspots in all the five PD-L1/mAb binding complexes. It is also found that the important residues of mAbs binding to PD-L1M115 and PD-L1Y123 are similar to each other. The computational alanine scanning result is compared to the experimental measurements that are available for two of the mAbs (KN035 and atezolizumab). The calculated alanine scanning result is in good agreement with the experimental data with a correlation coefficient of 0.87 for PD-L1/KN035 and 0.6 for PD-L1/atezolizumab. Our computation found more hotspots on PD-L1 in the PD-L1/KN035 complex than those in the PD-L1/atezolizumab system, indicating stronger binding affinity in the former than the latter, which is in good agreement with the experimental finding. The present work provides important insights for the design of new mAbs targeting PD-L1.

On the Use of Interaction Entropy and Related Methods to Estimate Binding Entropies

Molecular mechanics combined with Poisson-Boltzmann or generalized Born and solvent-accessible area solvation energies (MM/PBSA and MM/GBSA) are popular methods to estimate the free energy for the binding of small molecules to biomacromolecules. However, the estimation of the entropy has been problematic and time-consuming. Traditionally, normal-mode analysis has been used to estimate the entropy, but more recently, alternative approaches have been suggested. In particular, it has been suggested that exponential averaging of the electrostatic and Lennard-Jones interaction energies may provide much faster and more accurate entropies, the interaction entropy (IE) approach. In this study, we show that this exponential averaging is extremely poorly conditioned. Using stochastic simulations, assuming that the interaction energies follow a Gaussian distribution, we show that if the standard deviation of the interaction energies (σIE) is larger than 15 kJ/mol, it becomes practically impossible to converge the interaction entropies (more than 10 million energies are needed, and the number increases exponentially). A cumulant approximation to the second order of the exponential average shows a better convergence, but for σIE > 25 kJ/mol, it gives entropies that are unrealistically large. Moreover, in practical applications, both methods show a steady increase in the entropy with the number of energies considered.

Anchor-Locker Binding Mechanism of the Coronavirus Spike Protein to Human ACE2: Insights from Computational Analysis

COVID-19 has emerged as the most serious international pandemic in early 2020 and the lack of comprehensive knowledge in the recognition and transmission mechanisms of this virus hinders the development of suitable therapeutic strategies. The specific recognition during the binding of the spike glycoprotein (S protein) of coronavirus to the angiotensin-converting enzyme 2 (ACE2) in the host cell is widely considered the first step of infection. However, detailed insights on the underlying mechanism of dynamic recognition and binding of these two proteins remain unknown. In this work, molecular dynamics simulation and binding free energy calculation were carried out to systematically compare and analyze the receptor-binding domain (RBD) of six coronavirus' S proteins. We found that affinity and stability of the RBD from SARS-CoV-2 under the binding state with ACE2 are stronger than those of other coronaviruses. The solvent-accessible surface area (SASA) and binding free energy of different RBD subunits indicate an "anchor-locker" recognition mechanism involved in the binding of the S protein to ACE2. Loop 2 (Y473-F490) acts as an anchor for ACE2 recognition, and Loop 3 (G496-V503) locks ACE2 at the other nonanchoring end. Then, the charged or long-chain residues in the β-sheet 1 (N450-F456) region reinforce this binding. The proposed binding mechanism was supported by umbrella sampling simulation of the dissociation process. The current computational study provides important theoretical insights for the development of new vaccines against SARS-CoV-2.

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.

Molecular insights into the binding variance of the SARS-CoV-2 spike with human, cat and dog ACE2 proteins

SARS-CoV-2 has recently caused an epidemic in humans and poses a huge threat to global public health. As a primary receptor of SARS-CoV-2, angiotensin-converting enzyme 2 (ACE2) exists in different hosts that are in close contact with humans, especially cats and dogs. However, the underlying mechanism of how the spike receptor binding domain (RBD) of SARS-CoV-2 cooperates with human ACE2 (hACE2), cat ACE2 (cACE2) and dog ACE2 (dACE2) and the variation in binding remains largely unsolved. Therefore, we explored the binding behavior of the spike RBD with cACE2, dACE2 and hACE2 via all-atom molecular dynamics simulations. In accordance with the binding free energies and residue interactions, the spike RBD has respective binding specificities with cACE2, dACE2 and hACE2, and the binding affinities decrease in the order of hACE2, cACE2, dACE2, mainly due to changes in the amino acids Q24L, H34Y, and M82T in cACE2 or dACE2. Furthermore, alanine scanning analysis results validated some key residues of the spike RBD interact with ACE2 and provided clues to the variation of amino acid that could influence the transmissibility or immune responses of SARS-CoV-2. Decreasing dynamic correlations strengths of ACE2 with the RBD were found in all hACE2-RBD, cACE2-RBD and dACE2-RBD systems. The ACE2 protein shows variable motion modes across the zinc metallopeptidase domain, which induces different interactions between ACE2 and the RBD. Our studies reveal that the motion pattern of the zinc metallopeptidase domain is critical to the binding behavior of RBD with ACE2. These findings could aid our understanding of selective recognition involving various ACE2 with the SARS-CoV-2 spike and shed further light on the binding mechanisms.

Analysis of the binding modes of the first- and second-generation antiandrogens with respect to F876L mutation

Androgen receptor (AR) is an important target for the treatment of prostate cancer, and mutations in the AR have an important impact on the resistance of existing drugs. In this work, we performed molecular dynamics simulations of the existing marketed antiandrogens flutamide, nilutamide, bicalutamide, enzalutamide, apalutamide, darolutamide, and its main metabolite ORM15341 in complex with the wild-type and F876L mutant AR. We calculated the residue-specific binding free energy contribution of the wild-type and mutant ARs with the AS-IE method and analyzed the hotspot residues and the binding free energy contributions of specific residues before and after the mutation. In addition, we analyzed the total binding obtained by adding residue binding energy contributions and compared the results with experimental values. The obtained residue-specific binding information should be very helpful in understanding the mechanism of drug resistance with respect to specific mutations and in the design of new generation drugs against possible new mutations.

Computational Analysis of Residue-Specific Binding Free Energies of Androgen Receptor to Ligands

Androgen receptor (AR) is an important therapeutic target for the treatment of diseases such as prostate cancer, hypogonadism, muscle wasting, etc. In this study, the complex structures of the AR ligand-binding domain (LBD) with fifteen ligands were analyzed by molecular dynamics simulations combined with the alanine-scanning-interaction-entropy method (ASIE). The quantitative free energy contributions of the pocket residues were obtained and hotspot residues are quantitatively identified. Our calculation shows that that these hotspot residues are predominantly hydrophobic and their interactions with binding ligands are mainly van der Waals interactions. The total binding free energies obtained by summing over binding contributions by individual residues are in good correlation with the experimental binding data. The current quantitative analysis of binding mechanism of AR to ligands provides important insight on the design of future inhibitors.

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.

An Approach to Computing Solvent Reorganization Energy

A method for accurate calculation of reorganization free energy of an explicit solvent solvating a solute molecule is presented. The method relies on the knowledge of solvation free energy known either from experimental measurement or from accurate calculation. An important part of this approach lies in the calculation of entropy in solute-solvent interaction free energy using the interaction entropy method, combined with the calculation of enthalpy of solvent reorganization based on a finite number of solvent molecules near the solute molecule. This interaction entropy-solvent reorganization or IESR method enables one to compute both enthalpy and entropy components of the solvent reorganization energy from MD simulation of the solvated system. The calculated results are determined by the molecular force field only without any empirical parameter. The current method is applied to computing the reorganization energies of water solvent solvating a wide range of solute molecules including both hydrophilic and hydrophobic ones. The accuracy of the approach is indirectly verified by the excellent agreement of individual enthalpies and entropies of the solvation energies between theory and experiment.

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.

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

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

An accurate free energy estimator: based on MM/PBSA combined with interaction entropy for protein-ligand binding affinity

The molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) method is constantly used to calculate the binding free energy of protein-ligand complexes, and has been shown to effectively balance computational cost against accuracy. The relative binding affinities obtained by the MM/PBSA approach are acceptable, while it usually overestimates the absolute binding free energy. This paper proposes four free energy estimators based on the MM/PBSA for enthalpy change combined with interaction entropy (IE) for entropy change using different weights for individual energy terms. The ΔG_{PBSA_IE} method is determined to be an optimal estimator based on its performance in terms of the correlation between experimental and theoretical values and error estimations. This approach is optimized using high-quality experimental values from a training set containing 84 protein-ligand systems, and the coefficients for the sum of electrostatic energy and polar solvation free energy, van der Waals (vdW) energy, non-polar solvation energy and entropy change are obtained by multivariate linear fitting to the corresponding experimental values. A comparison between the traditional MM/PBSA method and this method shows that the correlation coefficient is improved from 0.46 to 0.72 and the slope of the regression line increases from 0.10 to 1.00. More importantly, the mean absolute error (MAE) is significantly reduced from 22.52 to 1.59 kcal mol^-1. Furthermore, the numerical stability of this method is validated on a test set with a similar correlation coefficient, slope and MAE to those of the training set. Based on the above advantages, the ΔG_{PBSA_IE} method can be a powerful tool for a reliable and accurate estimation of binding free energy and plays a significant role in a detailed energetic investigation of protein-ligand interaction.

Molecular Mechanism of Selective Binding of NMS-P118 to PARP-1 and PARP-2: A Computational Perspective

The nuclear protein poly (ADP-ribose) polymerase-1 (PARP-1) inhibitors have been proven effective to potentiate both chemotherapeutic agents and radiotherapy. However, a major problem of most current PARP inhibitors is their lack of selectivity for PARP-1 and its closest isoform PARP-2. NMS-P118 is a highly selective PARP inhibitor that binds PARP-1 stronger than PARP-2 and has many advantages such as excellent pharmacokinetic profiles. In this study, molecular dynamics (MD) simulations of NMS-P118 in complex with PARP-1 and PARP-2 were performed to understand the molecular mechanism of its selectivity. Alanine scanning together with free energy calculation using MM/GBSA and interaction entropy reveal key residues that are responsible for the selectivity. Although the conformation of the binding pockets and NMS-P118 are very similar in PARP-1 and PARP-2, most of the hot-spot residues in PARP-1 have stronger binding free energy than the corresponding residues in PARP-2. Detailed analysis of the binding energy shows that the 4′4-difluorocyclohexyl ring on NMS-P118 form favorable hydrophobic interaction with Y889 in PARP-1. In addition, the H862 residue in PARP-1 has stronger binding free energy than H428 in PARP-2, which is due to shorter distance and stronger hydrogen bonds. Moreover, the negatively charged E763 residue in PARP-1 forms stronger electrostatic interaction energy with the positively charged NMS-P118 than the Q332 residue in PARP-2. These results rationalize the selectivity of NMS-P118 and may be useful for designing novel selective PARP inhibitors.

Entropic effect and residue specific entropic contribution to the cooperativity in streptavidin-biotin binding

Molecular dynamics (MD) simulations were performed employing the polarized protein-specific charge (PPC) to explore the origin of the cooperativity in streptavidin-biotin systems (wild type, two single mutations and one double-mutation). The results of the experiment found that the existence of cooperativity is mainly the result of the entropic effect. In this study, the entropic contribution to the binding free energy was calculated using the recently developed interaction entropy (IE) method, and computational results are in excellent agreement with the experimental observations and are further verified by the calculation of the thermodynamic integration. Comparison of different force fields in terms of predicted binding strength ordering, cooperativity of energy and the stability of hydrogen bonding suggests that the PPC force field combined IE method is a suitable choice. In addition, the IE method enables us to obtain the residue-specific entropic contributions to the streptavidin-biotin binding affinity and identify ten hot-spot residues providing the dominant contribution to the cooperative binding. Importantly, the overall cooperativity obtained from the ten residues also comes mainly from the entropic effect in our study. The calculation of the potential of mean force shows that the unbinding of streptavidin-biotin is a multi-step process, and each step corresponds to the formation and rupture of the hydrogen bond network. And S45A mutation may increase the rigidity of the linker region, making the flap region relatively difficult to open. The present study provides significant molecular insight into the binding cooperativity of the streptavidin-biotin complex.

Computational approaches to studying methylated H4K20 recognition by DNA repair factor 53BP1

Histone lysine methylation regulates the recruitment of mammalian DNA repair factor 53BP1 to the histone H4 lysine 20 (H4K20), through specific recognition of the tandem Tudor domain of 53BP1. The di- and mono-methylated H4K20 bind to 53BP1 with high affinity, but the non- and tri-methylated H4K20 do not. Here, we develop a new approach to carry out computational study to unravel the binding mechanism of methylated H4K20 by 53BP1 and to compute relative binding affinities of different methylations of H4K20 by 53BP1. First, hot spots in 53BP1 were predicted by computational alanine scanning and aromatic cages formed by W1495, Y1500, Y1502, and Y1523 are found to provide the dominant binding to di- and mono-methylated H4K20 in addition to D1521. Secondly, a de-methylation method is proposed to predict relative binding free energies between 53BP1 and different methylated states of H4K20. Finally, the tri-methylated and non-methylated H4K20/53BP1 complexes are found to be dynamically unstable, explaining the experimental finding that neither can bind to 53BP1. The present work provides an important theoretical basis for our understanding of histone methylations of H4K20 and their recognition mechanism by DNA repair factor 53BP1.

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.