Computational Alanine Scanning Research Articles

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.

Molecular insight into the affinity, specificity and cross-reactivity of systematic hepatocellular carcinoma RALT interaction profile with human receptor tyrosine kinases.

The ErbB family of receptor tyrosine kinases (RTKs) contains four members: EGFR, ErbB2, ErbB3 and ErbB4; they are involved in the tumorigenesis of diverse cancers and can be inhibited natively by receptor-associated late transducer (RALT), a negative feedback regulator of ErbB signaling in human hepatocytes and hepatocellular carcinoma. Although the biological effects of RALT on EGFR kinase have been widely documented previously, the binding behavior of RALT to other ErbB/RTK kinases still remains largely unexplored. Here, the intermolecular interactions of RALT ErbB-binding region (EBR) as well as its functional sections and peptide segments with ErbBs and other human RTKs were systematically investigated at molecular and structural levels, from which we were able to identify those potential kinase targets of RALT protein, and to profile the affinity, specificity and cross-reactivity of RALT EBR domain and its sub-regions against various RTKs. It is revealed that RALT can target all the four ErbB kinases with high affinity for EGFR/ErbB2/ErbB4 and moderate affinity for ErbB3, but generally exhibits modest affinity to other RTKs, albeit few kinases such as LTK, EPHB6, MET and MUSK were also top-ranked as the unexpected targets of RALT. Peptide segments covering the key binding regions of RALT EBR domain were identified with computational alanine scanning, which were then optimized to obtain a number of designed peptide mutants with improved selectivity between different top-ranked RTKs.

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.

Decoding molecular mechanism underlying binding of drugs to HIV-1 protease with molecular dynamics simulations and MM-GBSA calculations

ABSTRACT HIV-1 protease (PR) is thought to be efficient targets of anti-AIDS drug design. Molecular dynamics (MD) simulations and multiple post-processing analysis technologies were applied to decipher molecular mechanism underlying binding of three drugs Lopinavir (LPV), Nelfinavir (NFV) and Atazanavir (ATV) to the PR. Binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) suggest that compensation between binding enthalpy and entropy plays a vital role in binding of drugs to PR. Dynamics analyses show that binding of LPV, NFV and ATV highly affects structural flexibility, motion modes and dynamics behaviour of the PR, especially for two flaps. Computational alanine scanning and interaction network analysis verify that although three drugs have structural difference, they share similar binding modes to the PR and common interaction clusters with the PR. The current findings also confirm that residues located interaction clusters, such as Asp25/Asp25ʹ, Gly27/Gly27ʹ, Ala28/Ala28ʹ, Asp29, Ile47/Ile47ʹ, Gly49/Gly49ʹ, Ile50/Ile50ʹ, Val82/Val82ʹ and Ile84/Ile84, can be used as efficient targets of clinically available inhibitors towards the PR.

Identification of Potential Inhibitors against Epstein–Barr Virus Nuclear Antigen 1 (EBNA1): An Insight from Docking and Molecular Dynamic Simulations

Epstein-Barr virus (EBV), a known tumorigenic virus, is associated with various neuropathies, including multiple sclerosis (MS). However, there is no anti-EBV FDA-approved drug available in the market. Our study targeted EBV protein EBV nuclear antigen 1 (EBNA1), crucial in virus replication and expressed in all the stages of viral latencies. This dimeric protein binds to an 18 bp palindromic DNA sequence and initiates the process of viral replication. We chose phytochemicals and FDA-approved MS drugs based on literature survey followed by their evaluation efficacies as anti-EBNA1 molecules. Molecular docking revealed FDA drugs ozanimod, siponimod, teriflunomide, and phytochemicals; emodin; protoapigenone; and EGCG bound to EBNA1 with high affinities. ADMET and Lipinski's rule analysis of the phytochemicals predicted favorable druggability. We supported our assessments of pocket druggability with molecular dynamics simulations and binding affinity predictions by the molecular mechanics generalized Born surface area (MM/GBSA) method. Our results establish a stable binding for siponimod and ozanimod with EBNA1 mainly via van der Waals interactions. We identified hot spot residues like I481', K477', L582', and K586' in the binding of ligands. In particular, K477' at the amino terminal of EBNA1 is known to establish interaction with two bases at the major groove of the DNA. Siponimod bound to EBNA1 engaging K477', thus plausibly making it unavailable for DNA interaction. Computational alanine scanning further supported the significant roles of K477', I481', and K586' in the binding of ligands with EBNA1. Conclusively, the compounds showed promising results to be used against EBNA1.

Definition of the Binding Architecture to a Target Promoter of HP1043, the Essential Master Regulator of Helicobacter pylori.

HP1043 is an essential orphan response regulator of Helicobacter pylori orchestrating multiple crucial cellular processes. Classified as a member of the OmpR/PhoB family of two-component systems, HP1043 exhibits a highly degenerate receiver domain and evolved to function independently of phosphorylation. Here, we investigated the HP1043 binding mode to a target sequence in the hp1227 promoter (Php1227). Scanning mutagenesis of HP1043 DNA-binding domain and consensus sequence led to the identification of residues relevant for the interaction of the protein with a target DNA. These determinants were used as restraints to guide a data-driven protein-DNA docking. Results suggested that, differently from most other response regulators of the same family, HP1043 binds in a head-to-head conformation to the Php1227 target promoter. HP1043 interacts with DNA largely through charged residues and contacts with both major and minor grooves of the DNA are required for a stable binding. Computational alanine scanning on molecular dynamics trajectory was performed to corroborate our findings. Additionally, in vitro transcription assays confirmed that HP1043 positively stimulates the activity of RNA polymerase.

Residue Specific Binding Mechanisms of PD-1 to Its Monoclonal Antibodies by Computational Alanine Scanning

Residue Specific Binding Mechanisms of PD-1 to Its Monoclonal Antibodies by Computational Alanine Scanning

Anatomy of the Extracellular Clasps That Regulate Platelet αIIbβ3 and αvβ3 Function

The integrins αIIbβ3 and αvβ3 on circulating platelets are maintained in inactive states by an intra-molecular clasp involving the cytoplasmic, transmembrane, and extracellular stalk domains of their α and β subunits. Following platelet stimulation, αIIbβ3 and αvβ3 rapidly undergo global rearrangements in which their clasps are disrupted and their headpieces open to expose a ligand binding site. Protein-protein interfaces, such as those of the αIIbβ3 and αvβ3 clasps, are usually large complementary surfaces in which specific amino acids, termed energetic “hot spots”, contribute disproportionately to the binding free energy. Previously, we scanned the β3 EGF-3, EGF-4, and βTD domains computationally with alanine residues and identified a limited number of interface residues that stabilized the extracellular clasps of αIIbβ3 and αvβ3. Here, we have used computational alanine scanning to identify corresponding “hot spots” in the Calf-1 and Calf-2 domains of the αIIb and αv components of the αIIbβ3 and αvβ3 clasps. The identified alanine substitutions were predicted to destabilize the respective stalk heterodimers. We identified 10 αIIb substitutions and 11 αv substitutions that would destabilize the stalk interfaces by a change in binding free energy (ΔΔG) of >0.3 kcal/mol. To confirm that a ΔΔG as small as 0.3 kcal/mol is sufficient to destabilize the β3 integrin stalk interfaces, we introduced 8 of the 10 αIIb substitutions into full-length αIIb, co-expressed the mutated αIIb with wild type β3 in CHO cells, and measured spontaneous binding of Alexa Fluor 488-labeled fibrinogen to the cells by flow cytometry. αIIbR751Aβ3 did not express, implying that R751 is important for correct αIIb folding and/or αIIbβ3 assembly. Each of the other mutations expressed and caused constitutive αIIbβ3 activation. Importantly, αIIbβ3 containing alanine substitutions whose effect on ΔΔG was <0.3 kcal/mol was inactive. Nonetheless, while the alanine scanning algorithm correctly identified destabilizing alanine substitutions, it did not correctly rank their functional effects. Hot spots could occur because they bury specific side chains in the protein-protein interface. While we found a modest correlation between ΔΔG and the buried surface area of specific αIIb side chains (R2=0.7), there was no correlation with αv (R2=0.1), a result consistent with an analysis of a compiled database of 2325 similar alanine mutants (Bogan and Thorn, J Mol Biol 280:1-9,1998). This suggests that other mechanisms, such as inter-subunit hydrogen bonds, are involved. Previously, in studies of the growth hormone-growth hormone receptor complex, Clackson and Wells (Science 267:383-86,1995) found that functionally important residues in the interface of the complex were in direct contact. To determine if this is the case for αIIbβ3 and αvβ3, we mapped the mutation-sensitive positions onto the αIIbβ3 and αvβ3 crystal structures to localize the αIIb, αv, and β3 hot spots in models of the αIIbβ3 and αvβ3 stalks. The energetic hot spots we identified in αIIb and αv were located in a strip along one face of the Calf-1 and Calf-2 domains. Similarly, the β3 hot spots were located in a strip along on one face of the EGF-3, EGF-4, and βTD domains. However, the hot spots were not contiguous and there was only partial overlap of the β3 side chains that interacted with αIIb versus αv. On the other hand, in models of the assembled stalk heterodimers, there was remarkable complementarity of the computationally identified hot spots in the αIIb and αv stalks with those in β3, indicating that only a limited number of specific inter-subunit interactions actually make significant contributions to stalk assembly. These results confirm the utility of computational alanine scanning for identifying and localizing functionally important interaction sites in αIIbβ3 and αvβ3. They also demonstrate that although the same surface of β3 interacts with the αIIb and αv stalks, unique, as well as overlapping, side chain interactions are responsible for maintaining the inactive state of the respective integrins. They also suggest that stabilizing the extracellular stalk heterodimers may be a way to attenuate β3 integrin activation. DisclosuresNo relevant conflicts of interest to declare.

Binding mode characterization of 13b in the monomeric and dimeric states of SARS-CoV-2 main protease using molecular dynamics simulations

The main protease, Mpro/3CLpro, plays an essential role in processing polyproteins translated from viral RNA to produce functional viral proteins and therefore serve as an attractive target for discovering COVID-19 therapeutics. The availability of both monomer and dimer crystal bound with a common ligand, ‘13b’ (α-ketoamide inhibitor), opened up opportunities to understand the Mpro mechanism of action. A comparative analysis of both forms of Mpro was carried out to elucidate the binding site architectural differences in the presence and absence of ‘13b’. Molecular dynamics simulations suggest that the presence of ‘13b’ enhances the stability of Mpro than the unbound APO form. The N- and C- terminals of both the protomers stabilize each other, and making it's interface essential for the active form of Mpro. In comparison to monomer, the relatively high affinity of ‘13b’ is gained in dimer pocket due to the high stability of the pocket by the interaction of S1 residue of chain B with residues F140, E166 and H172 of chain A, which is absent in monomer. The comprehensive essential dynamics, protein structure network analysis and thermodynamic profiling highlight the hot-spots, pivotal in molecular recognition process at protein-ligand and protein-protein interaction levels, cross-validated through computational alanine scanning study. A comparative description of ‘13b’ binding mechanism in both forms illustrates valuable insights into the inhibition mechanism and the selection of critical residues suitable for the structure-based approaches for the identification of more potent Mpro inhibitors. Communicated by Ramaswamy H. Sarma

Investigating effects of bridging water on the binding of neuraminidase−ligands using computational alanine scanning combined with interaction entropy method

Bridging water can form hydrogen bonds with protein and ligand, thus it plays an important role in the binding of protein and ligand. However, the detailed binding mechanism between bridging water and protein−ligand complexes is still unclear. In this study, the effect of bridging water on the binding of neuraminidase (NA) and two ligands (G20 and G28) was investigated using molecular dynamic (MD) simulations and computational alanine scanning combined with the newly developed interaction entropy method. The calculated binding free energy was consistent with the experimental value. Moreover, the rank of calculated binding free energy was in excellent agreement with the experimental rank. Computation analysis showed that the addition of bridging water was beneficial to the binding of NA and ligand, and remarkably enhanced the binding free energy. This is because the existence of bridging water leads to the enhancement in the energy of some residues. Therefore, the number of hot-spot residues also increases after considering bridging water. Our study identified that Leu134, Asp151, Arg152, Trp178, Ile222, Arg224, Glu227, Glu276, Glu277, Arg292 and Tyr406 were the key residues in the neuraminidase−ligand complex. Besides, hydrogen bond analysis showed that bridging water could regulate the hydrogen bond network, and thus could increase the number of hydrogen bonds. It is helpful for enhancing the stability of the complex and is one of the reasons for promoting their binding. These results provide directions and ideas for the design of more effective drugs against neuraminidase in the future.

In Silico Identification of Multi-target Anti-SARS-CoV-2 Peptides from Quinoa Seed Proteins.

Peptides are promising antagonists against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). To expedite drug discovery, a computational approach is widely employed for the initial screening of anti-SARS-CoV-2 candidates. This study aimed to investigate the potential of peptides from quinoa seed proteins as multi-target antagonists against SARS-CoV-2 spike glycoprotein receptor-binding domain, main protease, and papain-like protease. Five quinoa proteins were hydrolyzed in silico by papain and subtilisin. Among the 1465 peptides generated, seven could interact stably with the key binding residues and catalytic residues of the viral targets, mainly via hydrogen bonds and hydrophobic interactions. The seven peptides were comparable or superior to previously reported anti-SARS-CoV-2 peptides based on docking scores. Key residues in the seven peptides contributing to binding to viral targets were determined by computational alanine scanning. The seven peptides were predicted in silico to be non-toxic and non-allergenic. The peptides ranged between 546.66 and 3974.87 g/mol in molecular mass, besides exhibiting basic and cationic properties (isoelectric points: 8.26–12.10; net charges: 0.1–4.0). Among the seven peptides, VEDKGMMHQQRMMEKAMNIPRMCGTMQRKCRMS was found to bind the largest number of key residues on the targets. In conclusion, seven putative non-toxic, non-allergenic, multi-target anti-SARS-CoV-2 peptides were identified from quinoa seed proteins. The in vitro and in vivo efficacies of the seven peptides against SARS-CoV-2 deserve attention in future bench-top testing.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10989-021-10214-y.

An in-silico approach to identify the potential hot spots in SARS-CoV-2 spike RBD to block the interaction with ACE2 receptor

A novel acute viral pneumonia induced by SARS-CoV-2 exploded at the end of 2019, causing a severe medical and economic crisis. For developing specific pharmacotherapy against SARS-CoV-2, an in silico virtual screening was developed for the available in-house molecules. The conserved domain analysis was performed to identify the highly conserved and exposed amino acid regions in the SARS-CoV-2-S RBD sites. The Protein-Protein interaction analyses demonstrated the higher affinity between the SARS-CoV-2-S and ACE2 due to varieties of significant interactions between them. The computational alanine scanning mutation study has recognized the highly stabilized amino acids in the SARS-CoV-2-S RBD/ACE2 complex. The cumulative sequence investigations have inferred that Lys417, Phe486, Asn487, Tyr489, and Gln493 are perhaps the iconic target amino acids to develop a drug molecule or vaccine against SARS-CoV-2 infection. Most of the selected compounds include luteolin, zhebeirine, 3-dehydroverticine, embelin, andrographolide, ophiopogonin D, crocin-1, sprengerinin A, B, C, peimine, etc. were exhibited distinguish drug actions through the strong hydrogen bonding with the hot spots of the RBD. Besides, the 100 ns molecular dynamics simulation and free energy binding analysis showed the significant efficacy of luteolin to inhibit the infection of SARS-CoV-2. Communicated by Ramaswamy H. Sarma

PIIMS Server: A Web Server for Mutation Hotspot Scanning at the Protein-Protein Interface.

Protein-protein interactions (PPIs) play vital roles in regulating biological processes, such as cellular and signaling pathways. Hotspots are certain residues located at protein-protein interfaces that contribute more in protein-protein binding than other residues. Research on the mutational effects of hotspots is important for understanding basic aspects of protein association. Hence, various computational tools have been developed to explore the impact of mutation hotspots, which will allow a better understanding of the forces that drive PPIs. However, tools that may provide comprehensive substitutions at hotspots are still rare. Hence, there is a strong need for a new free web server to explore mutational effects of hotspots. Herein we introduce a web server named PIIMS that integrates molecular dynamics simulation and one-step free energy perturbation. It contains two main computational functions: (1) computational alanine scanning analysis to identify hotspots and (2) full mutation scanning analysis to evaluate the effects of hotspot mutations. We rigidly validated its ability to predict binding free energy changes by using large and diverse datasets including 1,341 mutations from 50 PPIs with the correlation coefficient R = 0.75. The difference from the existing tools is that PIIMS can perform further evaluation of hotspot residues with regard to their different mutations. The PIIMS web server (accessible at http://chemyang.ccnu.edu.cn/ccb/server/PIIMS/index.php) is free and open to all users without login requirements.

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.

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

The anti-apoptotic proteins B-cell lymphoma-extra large (Bcl-xL) and B-cell lymphoma/leukemia-2 (Bcl-2) are members of the Bcl-2 protein family, and they play important roles in regulating apoptosis and cell cycle retardation. However, the binding mechanisms of Bcl-xL/Bcl-2 with their associated agonists, including Bcl-2-associated death promoter (Bad) and Bcl-2-associated X protein (Bax), are not well understood. In the present study, the recently developed interaction entropy approach was employed for the calculation of entropic contribution, and the computational alanine scanning method was used to identify the hot spot in the protein-protein interactions between Bcl-xL/Bcl-2 and Bad/Bax. The calculated binding free energies and their ranks for the four systems were in good agreement with the experimental results. Computational analysis shows that there are more hot-spot residues in the Bcl-xL/Bad complex than that in the Bcl-xL/Bax complex, leading to a stronger binding affinity in the former. It is interesting to find that the reason for the stronger binding affinity of Bcl-2 to Bad than to Bax is different for the Bcl-xL system. Although there are more hot-spot residues in the Bcl-2/Bax system than in the Bcl-2/Bad complex, there are also more negatively contributing residues in the Bcl-2/Bax. Our study identified Arg104, Tyr105, Leu116, and Leu134 to be the common key residues in the Bcl-xL complexes, and Arg107, Tyr108, Phe112, Gln118, Leu137, Arg146, and Tyr202 are common key residues in the Bcl-2 complexes. These results would provide valuable information for the design of potent inhibitors of Bcl-xL/Bcl-2.

Computational and Experimental Approaches to Decipher the Binding Mechanism of General Odorant-Binding Protein 2 from Athetis lepigone to Chlorpyrifos and Phoxim.

Insect resistance to insecticides is an increasingly serious problem, and the resistant mechanisms are complicated. The resistance research based on the chemosensory pathway is one of the hot problems at present, but the specific binding mechanism of chemosensory genes and insecticides remains elusive. The binding mechanism of AlepGOBP2 (belong to insect chemosensory gene) with two insecticides was investigated by computational and experimental approaches. Our calculation results indicated that four key residues (Phe12, Ile52, Ile94, and Phe118) could steadily interact with these two insecticides and be assigned as hotspot sites responsible for their binding affinities. The significant alkyl-π and hydrophobic interactions involved by these four hotspot residues were found to be the driving forces for their binding affinities, especially for two residues (Phe12 and Ile94) that significantly contribute to the binding of chlorpyrifos, which were also validated by our binding assay results. Furthermore, we also found that the AlepGOBP2-chlorpyrifos/phoxim complexes can be more efficiently converged in the residue-specific force field-(RSFF2C) and its higher accuracy and repeatability in protein dynamics simulation, per-residue free energy decomposition, and computational alanine scanning calculations have also been achieved in this paper. These findings provided useful insights for efficient and reliable calculation of the binding mechanism of relevant AlepGOBPs with other insecticides, facilitating to develop new and efficient insecticides targeting the key sites of AlepGOBP2.

Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor ACE2 and Antibodies CR3022/CB6* *Supported by the National Natural Science Foundation of China (Grant Nos. 11874045 and 11774147).

The spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a global health crisis. The binding affinity of SARS-CoV-2 (in particular the receptor binding domain, RBD) to its receptor angiotensin converting enzyme 2 (ACE2) and the antibodies is of great importance in understanding the infectivity of COVID-19 and evaluating the candidate therapeutic for COVID-19. We propose a new method based on molecular mechanics/Poisson–Boltzmann surface area (MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to ACE2 and antibodies. The calculated binding free energy of SARS-CoV-2 RBD to ACE2 is –13.3 kcal/mol, and that of SARS-CoV RBD to ACE2 is –11.4 kcal/mol, which agree well with the experimental results of –11.3 kcal/mol and –10.1 kcal/mol, respectively. Moreover, we take two recently reported antibodies as examples, and calculate the free energy of antibodies binding to SARS-CoV-2 RBD, which is also consistent with the experimental findings. Further, within the framework of the modified MM/PBSA, we determine the key residues and the main driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational alanine scanning method. The present study offers a computationally efficient and numerically reliable method to evaluate the free energy of SARS-CoV-2 binding to other proteins, which may stimulate the development of the therapeutics against the COVID-19 disease in real applications.

Computational Hot-Spot Analysis of the SARS-CoV-2 Receptor Binding Domain/ACE2 Complex*.

Infection and replication of SARS CoV-2 (the virus that causes COVID-19) requires entry to the interior of host cells. In humans, a protein-protein interaction (PPI) between the SARS CoV-2 receptor-binding domain (RBD) and the extracellular peptidase domain of ACE2 on the surface of cells in the lower respiratory tract is an initial step in the entry pathway. Inhibition of the SARS CoV-2 RBD/ACE2 PPI is currently being evaluated as a target for therapeutic and/or prophylactic intervention. However, relatively little is known about the molecular underpinnings of this complex. Employing multiple computational platforms, we predicted "hot-spot" residues in a positive-control PPI (PMI/MDM2) and the CoV-2 RBD/ACE2 complex. Computational alanine scanning mutagenesis was performed to predict changes in Gibbs' free energy that are associated with mutating residues at the positive control (PMI/MDM2) or SARS RBD/ACE2 binding interface to alanine. Additionally, we used the Adaptive Poisson-Boltzmann Solver to calculate macromolecular electrostatic surfaces at the interface of the positive-control PPI and SARS CoV-2/ACE2 PPI. Finally, a comparative analysis of hot-spot residues for SARS-CoV and SARS-CoV-2, in complex with ACE2, is provided. Collectively, this study illuminates predicted hot-spot residues, and clusters, at the SARS CoV-2 RBD/ACE2 binding interface, potentially guiding the development of reagents capable of disrupting this complex and halting COVID-19.

Computational Investigation of Structural Dynamics of SARS-CoV-2 Methyltransferase-Stimulatory Factor Heterodimer nsp16/nsp10 Bound to the Cofactor SAM.

Recently, a highly contagious novel coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, has emerged, posing a global threat to public health. Identifying a potential target and developing vaccines or antiviral drugs is an urgent demand in the absence of approved therapeutic agents. The 5′-capping mechanism of eukaryotic mRNA and some viruses such as coronaviruses (CoVs) are essential for maintaining the RNA stability and protein translation in the virus. SARS-CoV-2 encodes S-adenosyl-L-methionine (SAM) dependent methyltransferase (MTase) enzyme characterized by nsp16 (2′-O-MTase) for generating the capped structure. The present study highlights the binding mechanism of nsp16 and nsp10 to identify the role of nsp10 in MTase activity. Furthermore, we investigated the conformational dynamics and energetics behind the binding of SAM to nsp16 and nsp16/nsp10 heterodimer by employing molecular dynamics simulations in conjunction with the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) method. We observed from our simulations that the presence of nsp10 increases the favorable van der Waals and electrostatic interactions between SAM and nsp16. Thus, nsp10 acts as a stimulator for the strong binding of SAM to nsp16. The hydrophobic interactions were predominately identified for the nsp16-nsp10 interactions. Also, the stable hydrogen bonds between Ala83 (nsp16) and Tyr96 (nsp10), and between Gln87 (nsp16) and Leu45 (nsp10) play a vital role in the dimerization of nsp16 and nsp10. Besides, Computational Alanine Scanning (CAS) mutagenesis was performed, which revealed hotspot mutants, namely I40A, V104A, and R86A for the dimer association. Hence, the dimer interface of nsp16/nsp10 could also be a potential target in retarding the 2′-O-MTase activity in SARS-CoV-2. Overall, our study provides a comprehensive understanding of the dynamic and thermodynamic process of binding nsp16 and nsp10 that will contribute to the novel design of peptide inhibitors based on nsp16.

Structural models of human ACE2 variants with SARS-CoV-2 Spike protein for structure-based drug design

Emergence of coronaviruses poses a threat to global health and economy. The current outbreak of SARS-CoV-2 has infected more than 28,000,000 people and killed more than 915,000. To date, there is no treatment for coronavirus infections, making the development of therapies to prevent future epidemics of paramount importance. To this end, we collected information regarding naturally-occurring variants of the Angiotensin-converting enzyme 2 (ACE2), an epithelial receptor that both SARS-CoV and SARS-CoV-2 use to enter the host cells. We built 242 structural models of variants of human ACE2 bound to the receptor binding domain (RBD) of the SARS-CoV-2 surface spike glycoprotein (S protein) and refined their interfaces with HADDOCK. Our dataset includes 140 variants of human ACE2 representing missense mutations found in genome-wide studies, 39 mutants with reported effects on the recognition of the RBD, and 63 predictions after computational alanine scanning mutagenesis of ACE2-RBD interface residues. This dataset will help accelerate the design of therapeutics against SARS-CoV-2, as well as contribute to prevention of possible future coronaviruses outbreaks.