Exploiting the co-crystal ligands shape, features and structure-based approaches for identification of SARS-CoV-2 Mpro inhibitors

References

• Agbowuro, A. A., Huston, W. M., Gamble, A. B., & Tyndall, J. D. J. M. R. R. (2018). Proteases and protease inhibitors in infectious diseases. Medicinal Research Reviews, 38, 1295–1331.
   PubMed Web of Science ®Google Scholar
• Bhachoo, J., & Beuming, T. J. M. P.-P I. (2017). Investigating protein–peptide interactions using the Schrödinger computational suite. In Methods in molecular biology (pp. 235–54). Springer.
   Google Scholar
• Case, D. A., Aktulga, H. M., Belfon, K., Ben-Shalom, I., Brozell, S. R., Cerutti, D., Cheatham, T., Cruzeiro, V. W. D., Darden, T., & Duke, R. E. (2021). Amber 2021. Reference manual. San Francisco: University of California.
   Google Scholar
• Case, D. A., Cheatham, III, T. E., Darden, T., Gohlke, H., Luo, R., Merz, Jr, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005). The Amber biomolecular simulation programs. Journal of Computational Chemistry, 26, 1668–1688.
   PubMed Web of Science ®Google Scholar
• Case, D. A., Darden, T., Cheatham, III, T. E., Simmerling, C., Wang, J., Duke, R. E., Luo, R., Merz, K. M., Pearlman, D. A., Crowley, M. J. U. O. C. (2006). . AMBER (pp. 9–45). San Francisco: University of California.
   Google Scholar
• Chen, Y., Liu, Q., & Guo, D. (2020). Emerging coronaviruses: genome structure, replication, and pathogenesis. Journal of Medical Virology, 92, 418–423.
   PubMed Web of Science ®Google Scholar
• Chohan, T. A., Qian, H.-Y., Pan, Y.-L., & Chen, J.-Z. (2016). Molecular simulation studies on the binding selectivity of 2-anilino-4-(thiazol-5-yl)-pyrimidines in complexes with CDK2 and CDK7. Molecular BioSystems, 12, 145–161.
   PubMedGoogle Scholar
• Dai, W., Zhang, B., Jiang, X.-M., Su, H., Li, J., Zhao, Y., Xie, X., Jin, Z., Peng, J., & Liu, F. (2020). Structure-based design of antiviral drug candidates targeting the SARS-CoV-2 main protease. Science, 368, 1331–1335.
   PubMed Web of Science ®Google Scholar
• Daina, A., Michielin, O., & Zoete, V. (2017). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7, 1–13.
   PubMed Web of Science ®Google Scholar
• Duan, Y., Wu, C., Chowdhury, S., Lee, M. C., Xiong, G., Zhang, W., Yang, R., Cieplak, P., Luo, R., Lee, T., Caldwell, J., Wang, J., & Kollman, P. (2003). A point‐charge force field for molecular mechanics simulations of proteins based on condensed‐phase quantum mechanical calculations. Journal of Computational Chemistry, 24, 1999–2012.
   PubMed Web of Science ®Google Scholar
• Elseginy, S. A. (2021). Virtual screening and structure-based 3D pharmacophore approach to identify small-molecule inhibitors of SARS-CoV-2 Mpro. Journal of Biomolecular Structure and Dynamics, 40, 1–17.
   Google Scholar
• Gahlawat, A., Kumar, N., Kumar, R., Sandhu, H., Singh, I. P., Singh, S., Sjöstedt, A., & Garg, P. (2020). Structure-based virtual screening to discover potential lead molecules for the SARS-CoV-2 main protease. Journal of Chemical Information and Modeling, 60, 5781–5793.
   PubMed Web of Science ®Google Scholar
• Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10, 449–461.
   PubMed Web of Science ®Google Scholar
• Grant, B. J., Skjærven, L., & Yao, X. Q. (2021). The Bio3D packages for structural bioinformatics. Protein Science, 30, 20–30.
   PubMed Web of Science ®Google Scholar
• Halgren, T. A., Murphy, R. B., Friesner, R. A., Beard, H. S., Frye, L. L., Pollard, W. T., & Banks, J. L. (2004). Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. Journal of Medicinal Chemistry, 47(7), 1750–1759.
   PubMed Web of Science ®Google Scholar
• Hawkins, P. C., Skillman, A. G., & Nicholls, A. J. (2007). Comparison of shape-matching and docking as virtual screening tools. Journal of Medicinal Chemistry, 50(1), 74–82.
   PubMed Web of Science ®Google Scholar
• Hawkins, P. C., Skillman, A. G., Warren, G. L., Ellingson, B. A., & Stahl, M. T. (2010). Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. Journal of Chemical Information and Modeling, 50, 572–584.
   PubMed Web of Science ®Google Scholar
• Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: visual molecular dynamics. Journal of Molecular Graphics, 14, 33–38.
   PubMed Web of Science ®Google Scholar
• Ibrahim, M. A., Abdelrahman, A. H., Hussien, T. A., Badr, E. A., Mohamed, T. A., EL-Seedi, H. R., Pare, P. W., Efferth, T., & Hegazy, M.-E. F. (2020). In silico drug discovery of major metabolites from spices as SARS-CoV-2 main protease inhibitors. Computers in Biology and Medicine, 126, 104046.
   PubMed Web of Science ®Google Scholar
• Imran, M., Waqar, S., Ogata, K., Ahmed, M., Noreen, Z., Javed, S., Bibi, N., Bokhari, H., Amjad, A., & Muddassar, M. J. R. A. (2020). Identification of novel bacterial urease inhibitors through molecular shape and structure based virtual screening approaches. RSC Advances, 10, 16061–16070.
   PubMed Web of Science ®Google Scholar
• Jiménez-Alberto, A., Ribas-Aparicio, R. M., Aparicio-Ozores, G., & Castelán-Vega, J. A. (2020). Virtual screening of approved drugs as potential SARS-CoV-2 main protease inhibitors. Computational Biology and Chemistry, 88, 107325.
   PubMed Web of Science ®Google Scholar
• Jin, Z., Du, X., Xu, Y., Deng, Y., Liu, M., Zhao, Y., & Zhang, B. J. N. (2020a). Structure of M. sup. pro from SARS-CoV-2 and discovery of its inhibitors. Nature, 582(7811), 289–294.
   Google Scholar
• Jin, Z., Zhao, Y., Sun, Y., Zhang, B., Wang, H., Wu, Y., Zhu, Y., Zhu, C., Hu, T., & Du, X. (2020b). Structural basis for the inhibition of COVID-19 virus main protease by carmofur, an antineoplastic drug. bioRxiv, 2020–04.
   Google Scholar
• Jorgensen, W., Chandrasekhar, J., Madura, J. D., Impey, R. W., & Klein, M. L. (1983). Comparison of simple potential functions for simulating liquid water. Journal of Chemical Physics, 79, 926.
   Web of Science ®Google Scholar
• Kapusta, K., Kar, S., Collins, J. T., Franklin, L. M., Kolodziejczyk, W., Leszczynski, J., & Hill, G. A. (2021). Protein reliability analysis and virtual screening of natural inhibitors for SARS-CoV-2 main protease (Mpro) through docking, molecular mechanic & dynamic, and ADMET profiling. Journal of Biomolecular Structure & Dynamics, 39(17), 6810–6827.
   PubMed Web of Science ®Google Scholar
• Khan, S. S., Shen, Y., Fatmi, M. Q., Campbell, R. E., & Bokhari, H. (2021). Design and prototyping of genetically encoded arsenic biosensors based on transcriptional regulator AfArsR. Biomolecules, 11, 1276.
   PubMed Web of Science ®Google Scholar
• Koes, D. R., & Camacho, C. (2014). Shape‐based virtual screening with volumetric aligned molecular shapes. Journal of Computational Chemistry, 35, 1824–1834.
   PubMed Web of Science ®Google Scholar
• Krishna, S., Kumar, S. B., Murthy, T. K., & Murahari, M. (2021). Structure-based design approach of potential BCL-2 inhibitors for cancer chemotherapy. Computers in Biology and Medicine, 134, 104455.
   Google Scholar
• Li, Q., Nie, J., Wu, J., Zhang, L., Ding, R., Wang, H., Zhang, Y., Li, T., Liu, S., & Zhang, M. (2021). SARS-CoV-2 501Y. V2 variants lack higher infectivity but do have immune escape. Cell, 184, 2362–2371.e9.
   Google Scholar
• Lobanov, M. Y., Bogatyreva, N., & Galzitskaya, O. (2008). Radius of gyration as an indicator of protein structure compactness. Molecular Biology, 42, 623–628.
   Google Scholar
• Lobo, J. M., Jiménez‐valverde, A., & Real, R. (2008). AUC: a misleading measure of the performance of predictive distribution models. Global ecology and Biogeography,17(2), 145–151.
   Google Scholar
• Madhavi Sastry, G., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: parameters, protocols and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27, 221–234.
   PubMed Web of Science ®Google Scholar
• Madhi, S. A., Baillie, V., Cutland, C. L., Voysey, M., Koen, A. L., Fairlie, L., Padayachee, S. D., Dheda, K., Barnabas, S. L., & Bhorat, Q. E. (2021). Efficacy of the ChAdOx1 nCoV-19 Covid-19 vaccine against the B. 1.351 variant. Lancet Infectious Disease, 384, 1885–1898.
   Google Scholar
• Mahase, E. (2021). Covid-19: Pfizer’s paxlovid is 89% effective in patients at risk of serious illness, company reports. British Medical Journal Publishing Group.
   Google Scholar
• Martinez, R., Blasina, A., Hallin, J. F., Hu, W., Rymer, I., Fan, J., Hoffman, R. L., Murphy, S., Marx, M., & Yanochko, G. (2015). Mitotic checkpoint kinase Mps1 has a role in normal physiology which impacts clinical utility. PLoS One, 10, e0138616.
   PubMed Web of Science ®Google Scholar
• Matsuoka, M., Kumar, A., Muddassar, M., Matsuyama, A., Yoshida, M., & Zhang, K. Y. (2017). Discovery of fungal denitrification inhibitors by targeting copper nitrite reductase from Fusarium oxysporum. Journal of Chemical Information and Modeling, 57, 203–213.
   PubMed Web of Science ®Google Scholar
• Mazzini, S., Musso, L., Dallavalle, S., & Artali, R. (2020). Putative SARS-CoV-2 Mpro inhibitors from an in-house library of natural and nature-inspired products: A virtual screening and molecular docking study. Molecules, 25(16), 3745.
   Google Scholar
• Mcgann, M. (2012). FRED and HYBRID docking performance on standardized datasets. Journal of computer-aided molecular design, 26(8), 897–906.
   Google Scholar
• Mengist, H. M., Dilnessa, T., & Jin, T. (2021). Structural basis of potential inhibitors targeting SARS-CoV-2 main protease. Frontiers in Chemistry, 9, 622898.
   Google Scholar
• Millán-Oñate, J., Rodriguez-Morales, A. J., Camacho-Moreno, G., Mendoza-Ramírez, H., Rodríguez-Sabogal, I. A., & Álvarez-Moreno, C. (2020). A new emerging zoonotic virus of concern: The 2019 novel Coronavirus (SARS CoV-2). Infectio, 24(3), 187–192.
   Google Scholar
• Muchmore, S. W., Souers, A. J., & Akritopoulou‐zanze, I. (2006). The use of three-dimensional shape and electrostatic similarity searching in the identification of a melanin-concentrating hormone receptor 1 antagonist. Chemical Biology & Drug Design, 67(2), 174–176.
   PubMed Web of Science ®Google Scholar
• Muramatsu, T., Takemoto, C., Kim, Y.-T., Wang, H., Nishii, W., Terada, T., Shirouzu, M., & Yokoyama, S. (2016). SARS-CoV 3CL protease cleaves its C-terminal autoprocessing site by novel subsite cooperativity. Proceedings of the National Academy of Sciences, 113(46), 12997–13002.
   Google Scholar
• Onufriev, A., Bashford, D., & Case, D. (2004). Exploring protein native states and large‐scale conformational changes with a modified generalized born model. Proteins: Structure, Function, and Bioinformatics, 55(2), 383–394.
   Google Scholar
• Paul, D., Basu, D., & Ghosh Dastidar, S. (2021). Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19. Journal of molecular modeling, 27, 1–16.
   Google Scholar
• Pechlaner, M., Dorta, A. P., Lin, Z., Rusu, V. H., & Van Gunsteren, W. F. (2021). A method to apply bond‐angle constraints in molecular dynamics simulations. Journal of Computational Chemistry, 42(6), 418–434.
   Google Scholar
• Phillips, J. C., Hardy, D. J., Maia, J. D., Stone, J. E., Ribeiro, J. V., Bernardi, R. C., Buch, R., Fiorin, G., Hénin, J., & Jiang, W. (2020). Scalable molecular dynamics on CPU and GPU architectures with NAMD. The Journal of chemical physics, 153(4), 044130.
   Google Scholar
• Poli, G., Seidel, T., & Langer, T. (2018). Conformational sampling of small molecules with iCon. Performance assessment in comparison with OMEGA. Frontiers in Chemistry, 6, 229.
   Google Scholar
• Rathnayake, A. D., Zheng, J., Kim, Y., Perera, K. D., Mackin, S., Meyerholz, D. K., Kashipathy, M. M., Battaile, K. P., Lovell, S., & Perlman, S. (2020). 3C-like protease inhibitors block coronavirus replication in vitro and improve survival in MERS-CoV–infected mice. Science translational medicine, 12(557), eabc5332.
   Google Scholar
• Release, S. (2017). 2017. 1: Canvas. New York, NY, USA: Schrödinger, LLC.
   Google Scholar
• Rocchia, W., Alexov, E., & Honig, B. (2001). Extending the applicability of the nonlinear Poisson − Boltzmann equation: multiple dielectric constants and multivalent ions. The Journal of Physical Chemistry B, 105(28), 6507–6514.
   Google Scholar
• Rossetti, G. G., Ossorio, M. A., Rempel, S., Kratzel, A., Dionellis, V. S., Barriot, S., Tropia, L., Gorgulla, C., Arthanari, H., & Thiel, V. (2022). Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits. Scientific reports, 12, 1–9.
   Google Scholar
• Ryckaert, J.-P., Ciccotti, G., & Berendsen, H. (1977). Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. Journal of computational physics, 23(3), 327–341.
   Google Scholar
• Sadeer, N. B., Llorent-Martínez, E. J., Bene, K., Mahomoodally, M. F., Mollica, A., Sinan, K. I., Stefanucci, A., Ruiz-Riaguas, A., Fernández-DE Córdova, M. L., & Zengin, G. (2019). Chemical profiling, antioxidant, enzyme inhibitory and molecular modelling studies on the leaves and stem bark extracts of three African medicinal plants. Journal of Pharmaceutical and Biomedical Analysis, 174, 19–33.
   Google Scholar
• Sargsyan, K., Grauffel, C., & Lim, C. (2017). How molecular size impacts RMSD applications in molecular dynamics simulations. Journal of chemical theory and computation, 13(4), 1518–1524.
   Google Scholar
• Seeliger, D., & DE Groot, B. (2010). Ligand docking and binding site analysis with PyMOL and Autodock/Vina. Journal of computer-aided molecular design, 24(5), 417–422.
   Google Scholar
• Shen, C., Wang, Z., Yao, X., Li, Y., Lei, T., Wang, E., Xu, L., Zhu, F., Li, D., & Hou, T. (2020). Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power. Briefings in bioinformatics, 21(1), 282–297.
   Google Scholar
• Shereen, M. A., Khan, S., Kazmi, A., Bashir, N., & Siddique, R. (2020). COVID-19 infection: Emergence, transmission, and characteristics of human coronaviruses. Journal of advanced research, 24, 91–98.
   Google Scholar
• Shivakumar, D., Harder, E., Damm, W., Friesner, R. A., & Sherman, W. (2012). Improving the prediction of absolute solvation free energies using the next generation OPLS force field. Journal of chemical theory and computation, 8(8), 2553–2558.
   Google Scholar
• Software, O. S. (2011). vROCS. Openeye Software Santa Fee.
   Google Scholar
• Temml, V., Voss, C. V., Dirsch, V. M., & Schuster, D. (2014). Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening. Journal of chemical information and modeling,54(2), 367–371.
   Google Scholar
• Trott, O., & Olson, A. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry, 31(2), 455–461.
   Google Scholar
• Ullrich, S., Nitsche, C. J. B., & Letters, M. C. (2020). The SARS-CoV-2 main protease as drug target. Bioorganic & medicinal chemistry letters, 30(17), 127377.
   Google Scholar
• Vuong, W., Khan, M. B., Fischer, C., Arutyunova, E., Lamer, T., Shields, J., Saffran, H. A., Mckay, R. T., VAN Belkum, M. J., & Joyce, M. A. (2020). Feline coronavirus drug inhibits the main protease of SARS-CoV-2 and blocks virus replication. Nature communications, 11(1), 1–8.
   Google Scholar
• Wang, Y., Anirudhan, V., Du, R., Cui, Q., & Rong, L. (2021). RNA‐dependent RNA polymerase of SARS‐CoV‐2 as a therapeutic target. Journal of medical virology, 93(1), 300–310.
   Google Scholar
• Weinzierl, R. O. J. (2021). Molecular dynamics simulations of human FOXO3 reveal intrinsically disordered regions spread spatially by intramolecular electrostatic repulsion. Biomolecules, 11, 856.
   PubMed Web of Science ®Google Scholar
• Yang, K. S., Ma, X. R., Ma, Y., Alugubelli, Y. R., Scott, D. A., Vatansever, E. C., Drelich, A. K., Sankaran, B., Geng, Z. Z., & Blankenship, L. R. (2021). A quick route to multiple highly potent SARS‐CoV‐2 main protease inhibitors. ChemMedChem, 16, 942–948.
   PubMed Web of Science ®Google Scholar
• Yoshino, R., Yasuo, N., & Sekijima, M. (2020). Identification of key interactions between SARS-CoV-2 main protease and inhibitor drug candidates. Scientific reports, 10(1), 1–8.
   Google Scholar
• Zhang, L., Lin, D., Sun, X., Curth, U., Drosten, C., Sauerhering, L., Becker, S., Rox, K., & Hilgenfeld, R. (2020). Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science, 368(6489), 409–412.
   Google Scholar