Investigation of structural analogs of hydroxychloroquine for SARS-CoV-2 main protease (Mpro): A computational drug discovery study

Structural and thermodynamic properties of conserved water molecules in Mpro native: A combined approach by MD simulation and Grid Inhomogeneous Solvation Theory

The new viral strains of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are continuously rising, becoming more virulent, and transmissible. Therefore, the development of new antiviral drugs is essential. Due to its significant role in the viral life cycle of SARS-CoV-2, the main protease (Mpro) enzyme is a leading target for antiviral drug design. The Mpro monomer consists of domain DI, DII, and DI-DII interface. Twenty-one conserved water molecules (W4-W24) are occupied at these domains according to multiple crystal structure analyses. The crystal and MD structures reveal the presence of eight conserved water sites in domain DI, DII and remaining in the DI-DII interface. Grid-based inhomogeneous fluid solvation theory (GIST) was employed on MD structures of Mpro native to predict structural and thermodynamic properties of each conserved water site for focusing to identify the specific conserved water molecules that can easily be displaced by proposed ligands. Finally, MD water W13 is emerged as a promising candidate for water mimic drug design due to its low mean interaction energy, loose binding character with the protein, and its involvement in a water-mediated H-bond with catalytic His41 via the interaction Thr25(OG)---W13---W---His41(NE2). In this context, water occupancy, relative interaction energy, entropy, and topologies of W13 are thermodynamically acceptable for the water displacement method. Therefore, the strategic use of W13's geometrical position in the DI domain may be implemented for drug discovery against COVID disease by designing new ligands with appropriately oriented chemical groups to mimic its structural, electronic, and thermodynamic properties.

Identification of novel peptide inhibitors of Plasmodium falciparum dihydrofolate reductase (PfDHFR): molecular docking and MD simulation studies

The presence of drug-resistant variants of Plasmodium parasites within the population has presented a substantial obstacle to the eradication of Malaria. As a result, numerous research groups have directed their efforts towards creating new medication candidates that specifically target parasites. In this study, our main objective was to identify tri-peptide inhibitors for Plasmodium falciparum Dihydrofolate Reductase (PfDHFR) with the aim of finding a new peptide that exhibits superior binding properties compared to the current inhibitor, WR99210. In order to achieve this objective, a virtual library consisting of 8000 tripeptides was generated and subjected to computational screening against wild-type PfDHFR. The purpose of this screening was to discover the most effective binders at the active site. The four most optimal tripeptides identified (Trp-Trp-Glu, Trp-Phe-Tyr, Phe-Trp-Trp, Tyr-Trp-Trp) exhibited significant non-covalent interactions inside the active site of PfDHFR and had binding energies ranging from -9.5 to -9.0 kcal/mol and WR99210 had a binding energy of -6.2 kcal/mol. A 250 ns Molecular Dynamics (MD) simulation was performed to investigate the kinetic and thermodynamic characteristics of the protein-ligand complexes. The Root Mean Square Deviation (RMSD) values for the optimal tripeptides fell within the allowed range, indicating the stability of the ligands inside the protein complex. The Ki value for the most effective tripeptide was 0.3482 µM, whereas WR99210 had a Ki value of 1.02 µM. This article presents the initial discovery of peptide inhibitors targeting PfDHFR. In this text, we provide a comprehensive explanation of the interactions that occur between peptides and the enzyme.Communicated by Ramaswamy H. Sarma.

Identification of potential therapeutic targets for COVID-19 through a structural-based similarity approach between SARS-CoV-2 and its human host proteins

Background: The COVID-19 pandemic caused by SARS-CoV-2 has led to millions of deaths worldwide, and vaccination efficacy has been decreasing with each lineage, necessitating the need for alternative antiviral therapies. Predicting host-virus protein-protein interactions (HV-PPIs) is essential for identifying potential host-targeting drug targets against SARS-CoV-2 infection. Objective: This study aims to identify therapeutic target proteins in humans that could act as virus-host-targeting drug targets against SARS-CoV-2 and study their interaction against antiviral inhibitors. Methods: A structure-based similarity approach was used to predict human proteins similar to SARS-CoV-2 ("hCoV-2"), followed by identifying PPIs between hCoV-2 and its target human proteins. Overlapping genes were identified between the protein-coding genes of the target and COVID-19-infected patient's mRNA expression data. Pathway and Gene Ontology (GO) term analyses, the construction of PPI networks, and the detection of hub gene modules were performed. Structure-based virtual screening with antiviral compounds was performed to identify potential hits against target gene-encoded protein. Results: This study predicted 19,051 unique target human proteins that interact with hCoV-2, and compared to the microarray dataset, 1,120 target and infected group differentially expressed genes (TIG-DEGs) were identified. The significant pathway and GO enrichment analyses revealed the involvement of these genes in several biological processes and molecular functions. PPI network analysis identified a significant hub gene with maximum neighboring partners. Virtual screening analysis identified three potential antiviral compounds against the target gene-encoded protein. Conclusion: This study provides potential targets for host-targeting drug development against SARS-CoV-2 infection, and further experimental validation of the target protein is required for pharmaceutical intervention.

Coevolution-based computational approach to detect resistance mechanism of epidermal growth factor receptor

Tyrosine kinase epidermal growth factor receptor (EGFR) correlates the neoplastic cell metastasis, angiogenesis, neoplastic incursion, and apoptosis. Due to the involvement of EGFR in these biological processes, it becomes a most potent target for treating non-small cell lung cancer (NSCLC). The tyrosine kinase inhibitors (TKI) have endorsed high efficacy and anticipation to patients but unfortunately, within a year of treatment, drug targets develop resistance due to mutations. The present study detected the compensatory mutations in EGFR to know the evolutionary mechanism of drug resistance. The results of this study demonstrate that compensatory mutations enlarge the drug-binding pocket which may lead to the altered orientation of the ligand (gefitinib and erlotinib) causing drug resistance. This indicates that coevolutionary forces play a significant role in fine-tuning the structure of EGFR protein against the drugs. The analysis provides insight into the evolution-induced structural aspects of drug resistance changes in EGFR which in turn be useful in designing drugs with better efficacy.

A proteomics-MM/PBSA dual approach for the analysis of SARS-CoV-2 main protease substrate peptide specificity

The main protease M^pro of SARS-CoV-2 is a well-studied major drug target. Additionally, it has been linked to this virus' pathogenicity, possibly through off-target effects. It is also an interesting diagnostic target. To obtain more data on possible substrates as well as to assess the enzyme's primary specificity a two-step approach was introduced. First, Terminal Amine Isobaric Labeling of Substrates (TAILS) was employed to identify novel M^pro cleavage sites in a mouse lung proteome library. In a second step, using a structural homology model, the MM/PBSA variant MM/GBSA (Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area) free binding energy calculations were carried out to determine relevant interacting amino acids. As a result, 58 unique cleavage sites were detected, including six that displayed glutamine at the P1 position. Furthermore, modeling results indicated that M^pro has a far higher potential promiscuity towards substrates than expected. The combination of proteomics and MM/PBSA modeling analysis can thus be useful for elucidating the specificity of M^pro, and thus open novel perspectives for the development of future peptidomimetic drugs against COVID-19, as well as diagnostic tools.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Molecular insights to the binding interactions of APNS containing HIV-protease inhibitors against SARS-CoV-2 Mpro: an in silico approach towards drug repurposing

SARS-CoV-2 M^pro is one of the most vital enzymes of the new coronavirus-2 (SARS-CoV-2) and is a crucial target for drug discovery. Unfortunately, there is not any potential drugs available to combat the action of SARS-CoV-2 M^pro. Based on the reports HIV-protease inhibitors can be applied against the SARS by targeting the SARS-CoV-1 M^pro, we have chosen few clinically trialed experimental and allophenylnorstatine (APNS) containing HIV-protease inhibitors (JE-2147, JE-533, KNI-227, KNI-272 & KNI-1931), to examine their binding affinities with SARS-CoV-2 M^pro and to assess their potential to check for a possible drug candidate against the protease. Here, we have chosen a methodology to understand the binding mechanism of these five inhibitors to SARS-CoV-2 M^pro by merging molecular docking, molecular dynamics (MD) simulation and MM-PBSA based free energy calculations. Our estimations disclose that JE-2147 is highly effective (ΔG_Bind = -28.31 kcal/mol) due to an increased favorable van der Waals (ΔE_vdw) interactions and decreased solvation (ΔG_solv) energies between the inhibitor and viral protease. JE-2147 shows a higher level of interactions as compared to JE-533 (-6.85 kcal/mol), KNI-227 (-18.36 kcal/mol), KNI-272 (-15.69 kcal/mol) and KNI-1931 (-21.59 kcal/mol) against SARS-CoV-2 M^pro. Binding contributions of important residues (His41, Met49, Cys145, His164, Met165, Glu166, Pro168, Gln189, etc.) from the active site or near the active site regions with ≥1.0 kcal/mol suggest a potent binding of the inhibitors. It is anticipated that the current study of binding interactions of these APNS containing inhibitors can pitch some valuable insights to design the significantly effective anti-SARS-CoV-2 M^pro drugs.Communicated by Ramaswamy H. Sarma.