Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

Molecular Diversity - Tập 27 - Trang 1879-1903 - 2022
Abdul-Quddus Kehinde Oyedele1,2, Abdeen Tunde Ogunlana1, Ibrahim Damilare Boyenle1,3,4, Ayodeji Oluwadamilare Adeyemi5, Temionu Oluwakemi Rita5, Temitope Isaac Adelusi1, Misbaudeen Abdul-Hammed6, Oluwabamise Emmanuel Elegbeleye1, Tope Tunji Odunitan7
1Computational Biology/Drug Discovery Laboratory, Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
2Department of Chemistry, University of New Haven, West Haven, USA
3Department of Chemistry and Biochemsitry, University of Maryland, Maryland, USA
4College of Health Sciences, Crescent University, Abeokuta, Nigeria
5Department of Medical Laboratory Technology, Lagos State College of Health, Lagos, Nigeria
6Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria
7Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Nigeria

Tóm tắt

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the ‘best’ program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Tài liệu tham khảo

Singh J, Petter RC, Baillie TA, Whitty A (2011) The resurgence of covalent drugs. Nat Rev Drug Discov 10(4):307–317. https://doi.org/10.1038/nrd3410 Bauer RA (2015) Covalent inhibitors in drug discovery: from accidental discoveries to avoided liabilities and designed therapies. Drug Discov Today 20(9):1061–1073. https://doi.org/10.1016/j.drudis.2015.05.005 Mah R, Thomas JR, Shafer CM (2014) Drug discovery considerations in the development of covalent inhibitors. Bioorg Med Chem Lett 24(1):33–39. https://doi.org/10.1016/j.bmcl.2013.10.003 De Vita E (2021) 10 years into the resurgence of covalent drugs. Future Med Chem 13(2):193–210. https://doi.org/10.4155/fmc-2020-0236 Santos KLBD, Cruz JN, Silva LB, Ramos RS, Neto MFA, Lobato CC, Ota SSB, Leite FHA, Borges RS, Silva CHTPD, Campos JM, Santos CBR (2020) Identification of novel chemical entities for adenosine receptor type 2A using molecular modeling approaches. Molecules 25(5):1245. https://doi.org/10.3390/molecules25051245 Costa EB, Silva RC, Espejo-Román JM, Neto MFA, Cruz JN, Leite FHA, Silva CHTP, Pinheiro JC, Macêdo WJC, Santos CBR (2020) Chemometric methods in antimalarial drug design from 1,2,4,5-tetraoxanes analogues. SAR QSAR Environ Res 31(9):677–695. https://doi.org/10.1080/1062936X.2020.1803961 Neto RAM, Santos CBR, Henriques SVC, Machado LO, Cruz JN, da Silva CHTP, Federico LB, Oliveira EHC, de Souza MPC, da Silva PNB, Taft CA, Ferreira IM, Gomes MRF (2022) Novel chalcones derivatives with potential antineoplastic activity investigated by docking and molecular dynamics simulations. J Biomol Struct Dyn 40(5):2204–2216. https://doi.org/10.1080/07391102.2020.1839562 Adelusi TI, Oyedele AK, Boyenle ID, Ogunlana AT, Adeyemi RO, Ukachi CD, Idris MO, Olaoba OT, Adedotun IO, Kolawole OE, Xiaoxing Y, Abdul-Hammed M (2022) Molecular modeling in drug discovery. Informatics in Medicine Unlocked 24(29):100880. https://doi.org/10.1016/j.imu.2022.100880 Boyenle ID, Adelusi TI, Ogunlana AT, Oluwabusola RA, Ibrahim NO, Tolulope A, Okikiola OS, Adetunji BL, Abioye IO, Oyedele AQ (2022) Consensus scoring-based virtual screening and molecular dynamics simulation of some TNF-alpha inhibitors. Inform Med Unlocked 1(28):100833 Oyedele AK, Adelusi TI, Ogunlana AT, Adeyemi RO, Atanda OE, Babalola MO, Ashiru MA, Ayoola IJ, Boyenle ID (2022) Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction. J Mol Model 28(6):142. https://doi.org/10.1007/s00894-022-05131-w Oyedele AK, Adelusi TI, Ogunlana AT, Ayoola MA, Adeyemi RO, Babalola MO, Ayorinde JB, Isong JA, Ajasa TO, Boyenle ID (2022) Promising disruptors of p53-MDM2 dimerization from some medicinal plant phytochemicals: a molecular modeling study. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2022.2097313 Katritch V, Byrd CM, Tseitin V, Dai D, Raush E, Totrov M, Abagyan R, Jordan R, Hruby DE (2007) Discovery of small molecule inhibitors of ubiquitin-like poxvirus proteinase I7L using homology modeling and covalent docking approaches. J Comput Aided Mol Des 21(10–11):549–558. https://doi.org/10.1007/s10822-007-9138-7 Toledo Warshaviak D, Golan G, Borrelli KW, Zhu K, Kalid O (2014) Structure-based virtual screening approach for discovery of covalently bound ligands. J Chem Inf Model 54(7):194150. https://doi.org/10.1021/ci500175r Bianco G, Forli S, Goodsell DS, Olson AJ (2016) Covalent docking using autodock: two-point attractor and flexible side chain methods. Protein Sci 25(1):295–301. https://doi.org/10.1002/pro.2733 Ray S, Murkin AS (2019) New electrophiles and strategies for mechanism-based and targeted covalent inhibitor design. Biochemistry 58(52):5234–5244. https://doi.org/10.1021/acs.biochem.9b00293 Baillie TA (2016) Targeted covalent inhibitors for drug design. Angew Chem Int Ed Engl 55(43):13408–13421. https://doi.org/10.1002/anie.201601091 Vane JR, Botting RM (2003) The mechanism of action of aspirin. Thromb Res 110(5–6):255–258. https://doi.org/10.1016/s0049-3848(03)00379-7 Roth GJ, Stanford N, Majerus PW (1975) Acetylation of prostaglandin synthase by aspirin. Proc Natl Acad Sci USA 72(8):3073–3076. https://doi.org/10.1073/pnas.72.8.3073 Picot D, Loll PJ, Garavito RM (1994) The X-ray crystal structure of the membrane protein prostaglandin H2 synthase-1. Nature 367(6460):243–249. https://doi.org/10.1038/367243a0 Schrör K (1997) Aspirin and platelets: the antiplatelet action of aspirin and its role in thrombosis treatment and prophylaxis. Semin Thromb Hemost 23(4):349–356. https://doi.org/10.1055/s-2007-996108 Patrono C, Rocca B (2012) Aspirin and Other COX-1 inhibitors. Handb Exp Pharmacol 210:137–164. https://doi.org/10.1007/978-3-642-29423-5_6 Baillie TA (2015) The contributions of Sidney D. Nelson to drug metabolism research. Drug Metab Rev 47(1):4–11. https://doi.org/10.3109/03602532.2014.985790 Waxman DJ, Strominger JL (1980) Sequence of active site peptides from the penicillin-sensitive D-alanine carboxypeptidase of Bacillus subtilis. Mechanism of penicillin action and sequence homology to beta-lactamases. J Biol Chem 255(9):3964–3976 Liras P, Rodríguez-García A (2000) Clavulanic acid, a beta-lactamase inhibitor: biosynthesis and molecular genetics. Appl Microbiol Biotechnol 54(4):467–475. https://doi.org/10.1007/s002530000420 Noguchi JK, Gill MA (1988) Sulbactam: a beta-lactamase inhibitor. Clin Pharm 7(1):37–51 Schoonover LL, Occhipinti DJ, Rodvold KA, Danziger LH (1995) Piperacillin/tazobactam: a new beta-lactam/beta-lactamase inhibitor combination. Ann Pharmacother 29(5):501–514. https://doi.org/10.1177/106002809502900510 Potter WZ, Davis DC, Mitchell JR, Jollow DJ, Gillette JR, Brodie BB (1973) Acetaminophen-induced hepatic necrosis. 3. Cytochrome P-450-mediated covalent binding in vitro. J Pharmacol Exp Ther 187(1):203–210 Potter WZ, Thorgeirsson SS, Jollow DJ, Mitchell JR (1974) Acetaminophen-induced hepatic necrosis. V. Correlation of hepatic necrosis, covalent binding and glutathione depletion in hamsters. Pharmacology 12(3):129–143. https://doi.org/10.1159/000136531 Liang H, Liu H, Kuang Y, Chen L, Ye M, Lai L (2020) Discovery of targeted covalent natural products against PLK1 by herb-based screening. J Chem Inf Model 60(9):4350–4358. https://doi.org/10.1021/acs.jcim.0c00074 Gao M, Moumbock AFA, Qaseem A, Xu Q, Günther S (2022) CovPDB: a high-resolution coverage of the covalent protein-ligand interactome. Nucleic Acids Res 50(D1):D445–D450. https://doi.org/10.1093/nar/gkab868 Kathman SG, Statsyuk AV (2016) Covalent tethering of fragments for covalent probe discovery. MedChemComm 7(4):576–585. https://doi.org/10.1039/c5md00518c Ghosh AK, Samanta I, Mondal A, Liu WR (2019) Covalent inhibition in drug discovery. ChemMedChem 14(9):889–906. https://doi.org/10.1002/cmdc.201900107 Baillie TA (2021) Approaches to mitigate the risk of serious adverse reactions in covalent drug design. Expert Opin Drug Discov 16(3):275–287. https://doi.org/10.1080/17460441.2021.1832079 Cross DA, Ashton SE, Ghiorghiu S, Eberlein C, Nebhan CA, Spitzler PJ, Orme JP, Finlay MR, Ward RA, Mellor MJ, Hughes G, Rahi A, Jacobs VN, Red Brewer M, Ichihara E, Sun J, Jin H, Ballard P, Al-Kadhimi K, Rowlinson R, Klinowska T, Richmond GH, Cantarini M, Kim DW, Ranson MR, Pao W (2014) AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discov 4(9):1046–1061. https://doi.org/10.1158/2159-8290.CD-14-0337 Finlay MR, Anderton M, Ashton S, Ballard P, Bethel PA, Box MR, Bradbury RH, Brown SJ, Butterworth S, Campbell A, Chorley C, Colclough N, Cross DA, Currie GS, Grist M, Hassall L, Hill GB, James D, James M, Kemmitt P, Klinowska T, Lamont G, Lamont SG, Martin N, McFarland HL, Mellor MJ, Orme JP, Perkins D, Perkins P, Richmond G, Smith P, Ward RA, Waring MJ, Whittaker D, Wells S, Wrigley GL (2014) Discovery of a potent and selective EGFR inhibitor (AZD9291) of both sensitizing and T790M resistance mutations that spares the wild type form of the receptor. J Med Chem 57(20):8249–8267. https://doi.org/10.1021/jm500973a Lonsdale R, Burgess J, Colclough N, Davies NL, Lenz EM, Orton AL, Ward RA (2017) Expanding the armory: predicting and tuning covalent warhead reactivity. J Chem Inf Model 57(12):3124–3137. https://doi.org/10.1021/acs.jcim.7b00553 Martin JS, MacKenzie CJ, Fletcher D, Gilbert IH (2019) Characterising covalent warhead reactivity. Bioorg Med Chem 27(10):2066–2074. https://doi.org/10.1016/j.bmc.2019.04.002 Moura A, Savageau MA, Alves R (2013) Relative amino acid composition signatures of organisms and environments. PLoS ONE 8(10):e77319. https://doi.org/10.1371/journal.pone.0077319 Baker BR, Meyer RB Jr (1968) Irreversible enzyme inhibitors. CXIX. Active-site-directed irreversible inhibitors of dihydrofolic reductase with tissue specificity derived from 2,4,6-triaminopyrimidine with a terminal sulfonyl fluoride at the 5 position. J Med Chem 11(3):489–494. https://doi.org/10.1021/jm00309a018 Baker BR, Meyer RB Jr (1969) Irreversible enzyme inhibitors. CXLII. Further studies on active-site-directed irreversible inhibitors of dihydrofolic reductase derived from 5-(p-Aminophenoxypropyl)-2,4,6-triaminopyrimidine bearing a terminal sulfonyl fluoride. J Med Chem 12(1):104–107. https://doi.org/10.1021/jm00301a027 Gehringer M, Laufer SA (2019) Emerging and re-emerging warheads for targeted covalent inhibitors: applications in medicinal chemistry and chemical biology. J Med Chem 62(12):5673–5724. https://doi.org/10.1021/acs.jmedchem.8b01153 Flanagan ME, Abramite JA, Anderson DP, Aulabaugh A, Dahal UP, Gilbert AM, Li C, Montgomery J, Oppenheimer SR, Ryder T, Schuff BP, Uccello DP, Walker GS, Wu Y, Brown MF, Chen JM, Hayward MM, Noe MC, Obach RS, Philippe L, Shanmugasundaram V, Shapiro MJ, Starr J, Stroh J, Che Y (2014) Chemical and computational methods for the characterization of covalent reactive groups for the prospective design of irreversible inhibitors. J Med Chem 57(23):10072–10079. https://doi.org/10.1021/jm501412a Lebraud H, Coxon CR, Archard VS, Bawn CM, Carbain B, Matheson CJ, Turner DM, Cano C, Griffin RJ, Hardcastle IR, Baisch U, Harrington RW, Golding BT (2014) Model system for irreversible inhibition of Nek2: thiol addition to ethynylpurines and related substituted heterocycles. Org Biomol Chem 12(1):141–148. https://doi.org/10.1039/c3ob41806e Craven GB, Affron DP, Allen CE, Matthies S, Greener JG, Morgan RML, Tate EW, Armstrong A, Mann DJ (2018) High-throughput kinetic analysis for target-directed covalent ligand discovery. Angew Chem Int Ed Engl 57(19):5257–5261. https://doi.org/10.1002/anie.201711825 MacFaul PA, Morley AD, Crawford JJ (2009) A simple in vitro assay for assessing the reactivity of nitrile containing compounds. Bioorg Med Chem Lett 19(4):1136–1138. https://doi.org/10.1016/j.bmcl.2008.12.105 Bianco G, Goodsell DS, Forli S (2020) Selective and effective: current progress in computational structure-based drug discovery of targeted covalent inhibitors. Trends Pharmacol Sci 41(12):1038–1049. https://doi.org/10.1016/j.tips.2020.10.005 Cully M (2015) Rational drug design: Tuning kinase inhibitor residence time. Nat Rev Drug Discov 14(7):457. https://doi.org/10.1038/nrd4673 Bonatto V, Shamim A, Rocho FDR, Leitão A, Luque FJ, Lameira J, Montanari CA (2021) Predicting the relative binding affinity for reversible covalent inhibitors by free energy perturbation calculations. J Chem Inf Model 61(9):4733–4744. https://doi.org/10.1021/acs.jcim.1c00515 Fanfrlík J, Brahmkshatriya PS, Řezáč J, Jílková A, Horn M, Mareš M, Hobza P, Lepšík M (2013) Quantum mechanics-based scoring rationalizes the irreversible inactivation of parasitic Schistosoma mansoni cysteine peptidase by vinyl sulfone inhibitors. J Phys Chem B 117(48):14973–14982. https://doi.org/10.1021/jp409604n Schirmeister T, Kesselring J, Jung S, Schneider TH, Weickert A, Becker J, Lee W, Bamberger D, Wich PR, Distler U, Tenzer S, Johé P, Hellmich UA, Engels B (2016) Quantum chemical-based protocol for the rational design of covalent inhibitors. J Am Chem Soc 138(27):8332–8335. https://doi.org/10.1021/jacs.6b03052 Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, Repasky MP, Knoll EH, Shelley M, Perry JK, Shaw DE, Francis P, Shenkin PS (2004) Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem 47(7):1739–1749. https://doi.org/10.1021/jm0306430 Zhu K, Borrelli KW, Greenwood JR, Day T, Abel R, Farid RS, Harder E (2014) Docking covalent inhibitors: a parameter free approach to pose prediction and scoring. J Chem Inf Model 54(7):1932–1940. https://doi.org/10.1021/ci500118s Alamri MA, Tahir Ul Qamar M, Afzal O, Alabbas AB, Riadi Y, Alqahtani SM (2021) Discovery of anti-MERS-CoV small covalent inhibitors through pharmacophore modeling, covalent docking and molecular dynamics simulation. J Mol Liq 330:115699. https://doi.org/10.1016/j.molliq.2021.115699 Scarpino A, Bajusz D, Proj M, Gobec M, Sosič I, Gobec S, Ferenczy GG, Keserű GM (2019) Discovery of immunoproteasome inhibitors using large-scale covalent virtual screening. Molecules 24(14):2590. https://doi.org/10.3390/molecules24142590 Paul AS, Islam R, Parves MR, Mamun AA, Shahriar I, Hossain MI, Hossain MN, Ali MA, Halim MA (2022) Cysteine focused covalent inhibitors against the main protease of SARS-CoV-2. J Biomol Struct Dyn 40(4):1639–1658. https://doi.org/10.1080/07391102.2020.1831610 Chowdhury SR, Kennedy S, Zhu K et al (2019) Discovery of covalent enzyme inhibitors using virtual docking of covalent fragments. Bioorg Med Chem Lett 29(1):36–39. https://doi.org/10.1016/j.bmcl.2018.11.019 Al-Khafaji K, Al-Duhaidahawi D, Taskin TT (2021) Using integrated computational approaches to identify safe and rapid treatment for SARS-CoV-2. J Biomol Struct Dyn 39(9):3387–3395. https://doi.org/10.1080/07391102.2020.1764392 Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261(3):470–489. https://doi.org/10.1006/jmbi.1996.0477 Jones G, Willett P, Glen RC, Leach AR, Taylor R (1997) Development and validation of a genetic algorithm for flexible docking. J Mol Biol 267(3):727–748. https://doi.org/10.1006/jmbi.1996.0897 Jones G, Willett P, Glen RC (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J Mol Biol 245(1):43–53. https://doi.org/10.1016/S0022-2836(95)80037-9 Verdonk ML, Cole JC, Hartshorn MJ, Murray CW, Taylor RD (2003) Improved protein-ligand docking using GOLD. Proteins 52(4):609–623. https://doi.org/10.1002/prot.10465 Clark M, Cramer RD III, Van Opdenbosch N (1989) Validation of the general purpose tripos 5.2 force field. J Comput Chem 10:982–1012 Schröder J, Klinger A, Oellien F, Marhöfer RJ, Duszenko M, Selzer PM (2013) Docking-based virtual screening of covalently binding ligands: an orthogonal lead discovery approach. J Med Chem 56(4):1478–1490. https://doi.org/10.1021/jm3013932 Li A, Sun H, Du L, Wu X, Cao J, You Q, Li Y (2014) Discovery of novel covalent proteasome inhibitors through a combination of pharmacophore screening, covalent docking, and molecular dynamics simulations. J Mol Model 20(11):2515. https://doi.org/10.1007/s00894-014-2515-y Zhang S, Tan J, Lai Z et al (2014) Effective virtual screening strategy toward covalent ligands: identification of novel NEDD8-activating enzyme inhibitors. J Chem Inf Model 54(6):1785–1797. https://doi.org/10.1021/ci5002058 Sgrignani J, De Luca F, Torosyan H, Docquier JD, Duan D, Novati B, Prati F, Colombo G, Grazioso G (2016) Structure-based approach for identification of novel phenylboronic acids as serine-β-lactamase inhibitors. J Comput Aided Mol Des 30(10):851–861. https://doi.org/10.1007/s10822-016-9962-8 Molecular Operating Environment (MOE), 2013.08; Chemical Computing Group Inc., 1010 Sherbooke St. West, Suite #910, Montreal, QC, Canada, H3A 2R7, 2013 Scholz C, Knorr S, Hamacher K, Schmidt B (2015) DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment. J Chem Inf Model 55(2):398–406. https://doi.org/10.1021/ci500681r Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 51(10):2731–2745. https://doi.org/10.1021/ci200274q Bensinger D, Stubba D, Cremer A, Kohl V, Waßmer T, Stuckert J, Engemann V, Stegmaier K, Schmitz K, Schmidt B (2019) Virtual screening identifies irreversible FMS-like tyrosine kinase 3 inhibitors with activity toward resistance-conferring mutations. J Med Chem 62(5):2428–2446. https://doi.org/10.1021/acs.jmedchem.8b01714 Omar SI, Lepre MG, Morbiducci U, Deriu MA, Tuszynski JA (2018) Virtual screening using covalent docking to find activators for G245S mutant p53. PLoS ONE 13(9):e0200769. https://doi.org/10.1371/journal.pone.0200769 Abagyan RA, Totrov MM, Kuznetsov DA (1994) ICM: a new method for protein modeling and design: applications to docking and structure prediction from the distorted native conformation. J Comput Chem 15:488–506 Abagyan R, Totrov M (1994) Biased probability Monte Carlo conformational searches and electrostatic calculations for peptides and proteins. J Mol Biol 235(3):983–1002. https://doi.org/10.1006/jmbi.1994.1052 Labute P (2008) The generalized born/volume integral implicit solvent model: estimation of the free energy of hydration using London dispersion instead of atomic surface area. J Comput Chem 29(10):1693–1698 Fradera X, Kaur J, Mestres J (2004) Unsupervised guided docking of covalently bound ligands. J Comput Aided Mol Des 18(10):635–650. https://doi.org/10.1007/s10822-004-5291-4 Fradera X, Knegtel RM, Mestres J (2000) Similarity-driven flexible ligand docking. Proteins 40(4):623–636. https://doi.org/10.1002/1097-0134(20000901)40:4%3c623::aid-prot70%3e3.0.co;2-i Ewing TJA, Kuntz ID (1997) Critical evaluation of search algorithms used in automated molecular docking. Comput Appl Biosci 18:1175–1189 Mestres J, Rohrer DC, Maggiora GM (1997) MIMIC: a molecular-field matching program. Exploiting applicability of molecular similarity approaches. J Comput Chem 18:934–954. https://doi.org/10.1002/(SICI)1096-987X(199705)18:7<934::AIDJCC6>3.0.CO Ewing TJA, Makino S, Skillman AG, Kuntz ID (2001) DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J Comput Aided Mol Des 15(5):411–428. https://doi.org/10.1023/A:1011115820450 Ouyang X, Zhou S, Su CT, Ge Z, Li R, Kwoh CK (2013) CovalentDock: automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints. J Comput Chem 34(4):326–336. https://doi.org/10.1002/jcc.23136 Blake L, Soliman MES (2014) Identification of irreversible protein splicing inhibitors as potential anti-TB drugs: insight from hybrid noncovalent/ covalent docking virtual screening and molecular dynamics simulations. Med Chem Res 23:2312–2323. https://doi.org/10.1007/s00044-013-0822-y London N, Miller RM, Krishnan S, Uchida K, Irwin JJ, Eidam O, Gibold L, Cimermančič P, Bonnet R, Shoichet BK, Taunton J (2014) Covalent docking of large libraries for the discovery of chemical probes. Nat Chem Biol. 10(12):1066–1072. https://doi.org/10.1038/nchembio.1666. Erratum in: Nat Chem Biol. 2015; 11(3):235 Mysinger MM, Shoichet BK (2010) Rapid context-dependent ligand desolvation in molecular docking. J Chem Inf Model 50(9):1561–1573. https://doi.org/10.1021/ci100214a Li J, Zhu T, Hawkins GD et al (1999) Extension of the platform of applicability of the SM5.42R universal solvation model. Theor Chem Acc 103:9–63. https://doi.org/10.1007/s002140050513 Shelley JC, Cholleti A, Frye LL, Greenwood JR, Timlin MR, Uchimaya M (2007) Epik: a software program for pK(a) prediction and protonation state generation for drug-like molecules. J Comput Aided Mol Des 21(12):681–691. https://doi.org/10.1007/s10822-007-9133-z Greenwood JR, Calkins D, Sullivan AP, Shelley JC (2010) Towards the comprehensive, rapid, and accurate prediction of the favorable tautomeric states of drug-like molecules in aqueous solution. J Comput Aided Mol Des 24(6–7):591–604. https://doi.org/10.1007/s10822-010-9349-1 Gasteiger J, Rudolph C, Sadowski J (1990) Automatic generation of 3Datomic coordinates for organic molecules. Tetrahedron Comput Methodol 3:537–547. https://doi.org/10.1016/0898-5529(90)90156-3 Hawkins PC, Skillman AG, Warren GL, Ellingson BA, Stahl MT (2010) Conformer generation with OMEGA: algorithm and validation using high quality structures from the Protein Databank and Cambridge Structural Database. J Chem Inf Model 50(4):572–584. https://doi.org/10.1021/ci100031x Shraga A, Olshvang E, Davidzohn N, Khoshkenar P, Germain N, Shurrush K, Carvalho S, Avram L, Albeck S, Unger T, Lefker B, Subramanyam C, Hudkins RL, Mitchell A, Shulman Z, Kinoshita T, London N (2019) Covalent docking identifies a potent and selective MKK7 inhibitor. Cell Chem Biol 26(1):98-108.e5. https://doi.org/10.1016/j.chembiol.2018.10.011 Nnadi CI, Jenkins ML, Gentile DR, Bateman LA, Zaidman D, Balius TE, Nomura DK, Burke JE, Shokat KM, London N (2018) Novel K-Ras G12C switch-II covalent binders destabilize Ras and accelerate nucleotide exchange. J Chem Inf Model 58(2):464–471. https://doi.org/10.1021/acs.jcim.7b00399 Morris GM, Huey R, Lindstrom W et al (2009) AutoDock4 and Auto-DockTools4: automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791 Amendola G, Ettari R, Previti S, Di Chio C, Messere A, Di Maro S, Hammerschmidt SJ, Zimmer C, Zimmermann RA, Schirmeister T, Zappalà M, Cosconati S (2021) Lead discovery of SARS-CoV-2 main protease inhibitors through covalent docking-based virtual screening. J Chem Inf Model 61(4):2062–2073. https://doi.org/10.1021/acs.jcim.1c00184 Wen C, Yan X, Gu Q, Du J, Wu D, Lu Y, Zhou H, Xu J (2019) Systematic studies on the protocol and criteria for selecting a covalent docking tool. Molecules 24(11):2183. https://doi.org/10.3390/molecules24112183 Wei L, Chen Y, Liu J, Rao L, Ren Y, Xu X, Wan J (2022) Cov_DOX: a method for structure prediction of covalent protein-ligand bindings. J Med Chem 65(7):5528–5538. https://doi.org/10.1021/acs.jmedchem.1c02007 Scarpino A, Ferenczy GG, Keserű GM (2018) Comparative evaluation of covalent docking tools. J Chem Inf Model 58(7):1441–1458. https://doi.org/10.1021/acs.jcim.8b00228 Adeniyi AA, Soliman MES (2017) Implementing QM in docking calculations: is it a waste of computational time? Drug Discov Today 22(8):1216–1223. https://doi.org/10.1016/j.drudis.2017.06.012 Chaskar P, Zoete V, Röhrig UF (2017) On-the-fly QM/MM docking with attracting cavities. J Chem Inf Model 57(1):73–84. https://doi.org/10.1021/acs.jcim.6b00406 Sotriffer C (2018) Docking of covalent ligands: challenges and approaches. Mol Inform 37(9–10):e1800062. https://doi.org/10.1002/minf.201800062 Scarpino A, Petri L, Knez D, Imre T, Ábrányi-Balogh P, Ferenczy GG, Gobec S, Keserű GM (2021) WIDOCK: a reactive docking protocol for virtual screening of covalent inhibitors. J Comput Aided Mol Des 35(2):223–244. https://doi.org/10.1007/s10822-020-00371-5 Yosaatmadja Y, Silva S, Dickson JM, Patterson AV, Smaill JB, Flanagan JU, McKeage MJ, Squire CJ (2015) Binding mode of the breakthrough inhibitor AZD9291 to epidermal growth factor receptor revealed. J Struct Biol 192(3):539–544. https://doi.org/10.1016/j.jsb.2015.10.018 Zhang Y, Zhang D, Tian H, Jiao Y, Shi Z, Ran T, Liu H, Lu S, Xu A, Qiao X, Pan J, Yin L, Zhou W, Lu T, Chen Y (2016) Identification of covalent binding sites targeting cysteines based on computational approaches. Mol Pharm 13(9):3106–3118. https://doi.org/10.1021/acs.molpharmaceut.6b00302 Lin HL, Zhang H, Pratt-Hyatt MJ, Hollenberg PF (2011) Thr302 is the site for the covalent modification of human cytochrome P450 2B6 leading to mechanism-based inactivation by tert-butylphenylacetylene. Drug Metab Dispos 39(12):2431–2439. https://doi.org/10.1124/dmd.111.042176 Soulère L, Barbier T, Queneau Y (2021) Docking-based virtual screening studies aiming at the covalent inhibition of SARS-CoV-2 MPro by targeting the cysteine 145. Comput Biol Chem 92:107463. https://doi.org/10.1016/j.compbiolchem.2021.107463 Ai Y, Yu L, Tan X, Chai X, Liu S (2016) Discovery of covalent ligands via noncovalent docking by dissecting covalent docking based on a “steric-clashes alleviating receptor (SCAR)” strategy. J Chem Inf Model 56(8):1563–1575. https://doi.org/10.1021/acs.jcim.6b00334 Backus KM, Correia BE, Lum KM, Forli S, Horning BD, González-Páez GE, Chatterjee S, Lanning BR, Teijaro JR, Olson AJ, Wolan DW, Cravatt BF (2016) Proteome-wide covalent ligand discovery in native biological systems. Nature 534(7608):570–574. https://doi.org/10.1038/nature18002 Jin Z, Du X, Xu Y, Deng Y, Liu M, Zhao Y, Zhang B, Li X, Zhang L, Peng C, Duan Y, Yu J, Wang L, Yang K, Liu F, Jiang R, Yang X, You T, Liu X, Yang X, Bai F, Liu H, Liu X, Guddat LW, Xu W, Xiao G, Qin C, Shi Z, Jiang H, Rao Z, Yang H (2020) Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature 582(7811):289–293. https://doi.org/10.1038/s41586-020-2223-y Ghosh R, Chakraborty A, Biswas A, Chowdhuri S (2021) Evaluation of green tea polyphenols as novel corona virus (SARS CoV-2) main protease (Mpro) inhibitors—an in silico docking and molecular dynamics simulation study. J Biomol Struct Dyn 39(12):4362–4374. https://doi.org/10.1080/07391102.2020.1779818 Ghosh R, Chakraborty A, Biswas A, Chowdhuri S (2020) Depicting the inhibitory potential of polyphenols from Isatis indigotica root against the main protease of SARS CoV-2 using computational approaches. J Biomol Struct Dyn 40(9):4110–4121. https://doi.org/10.1080/07391102.2020.1858164 Bharadwaj S, Dubey A, Yadava U, Mishra SK, Kang SG, Dwivedi VD (2021) Exploration of natural compounds with anti-SARS-CoV-2 activity via inhibition of SARS-CoV-2 Mpro. Brief Bioinform 22(2):1361–1377. https://doi.org/10.1093/bib/bbaa382 Bharadwaj S, Lee KE, Dwivedi VD, Kang SG (2020) Computational insights into tetracyclines as inhibitors against SARS-CoV-2 Mpro via combinatorial molecular simulation calculations. Life Sci 257:118080. https://doi.org/10.1016/j.lfs.2020.118080 Gogoi B, Chowdhury P, Goswami N, Gogoi N, Naiya T, Chetia P, Mahanta S, Chetia D, Tanti B, Borah P, Handique PJ (2021) Identification of potential plant-based inhibitor against viral proteases of SARS-CoV-2 through molecular docking, MM-PBSA binding energy calculations and molecular dynamics simulation. Mol Divers 25(3):1963–1977. https://doi.org/10.1007/s11030-021-10211-9 Shode FO, Idowu ASK, Uhomoibhi OJ, Sabiu S (2021) Repurposing drugs and identification of inhibitors of integral proteins (spike protein and main protease) of SARS-CoV-2. J Biomol Struct Dyn. https://doi.org/10.1080/07391102.2021.1886993 Das P, Majumder R, Mandal M, Basak P (2021) In-silico approach for identification of effective and stable inhibitors for COVID-19 main protease (Mpro) from flavonoid based phytochemical constituents of Calendula officinalis. J Biomol Struct Dyn 39(16):6265–6280. https://doi.org/10.1080/07391102.2020.1796799 Majumder R, Mandal M (2022) Screening of plant-based natural compounds as a potential COVID-19 main protease inhibitor: an in silico docking and molecular dynamics simulation approach. J Biomol Struct Dyn 40(2):696–711. https://doi.org/10.1080/07391102.2020.1817787 Fadaka AO, Sibuyi NRS, Martin DR, Klein A, Madiehe A, Meyer M (2021) Development of effective therapeutic molecule from natural sources against coronavirus protease. Int J Mol Sci 22(17):9431. https://doi.org/10.3390/ijms22179431 Patnin S, Makarasen A, Vijitphan P, Baicharoen A, Chaivisuthangkura A, Kuno M, Techasakul S (2022) Computational screening of phenylamino-phenoxy-quinoline derivatives against the main protease of SARS-CoV-2 using molecular docking and the ONIOM method. Molecules 27(6):1793. https://doi.org/10.3390/molecules27061793 Chowdhury KH, Chowdhury MR, Mahmud S, Tareq AM, Hanif NB, Banu N, Reza ASMA, Emran TB, Simal-Gandara J (2020) Drug repurposing approach against novel coronavirus disease (COVID-19) through virtual screening targeting SARS-CoV-2 main protease. Biology (Basel) 10(1):2. https://doi.org/10.3390/biology10010002 Das S, Singh A, Samanta SK, Roy AS (2022) Naturally occurring anthraquinones as potential inhibitors of SARS-CoV-2 main protease: an integrated computational study. Biologia (Bratisl) 77(4):1121–1134. https://doi.org/10.1007/s11756-021-01004-4 Baby K, Maity S, Mehta CH, Suresh A, Nayak UY, Nayak Y (2021) Targeting SARS-CoV-2 main protease: a computational drug repurposing study. Arch Med Res 52(1):38–47. https://doi.org/10.1016/j.arcmed.2020.09.013 Fiorucci D, Milletti E, Orofino F, Brizzi A, Mugnaini C, Corelli F (2021) Computational drug repurposing for the identification of SARS-CoV-2 main protease inhibitors. J Biomol Struct Dyn 39(16):6242–6248. https://doi.org/10.1080/07391102.2020.1796805 Rai H, Barik A, Singh YP, Suresh A, Singh L, Singh G, Nayak UY, Dubey VK, Modi G (2021) Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19. Mol Divers 25(3):1905–1927. https://doi.org/10.1007/s11030-021-10188-5 Djokovic N, Ruzic D, Djikic T, Cvijic S, Ignjatovic J, Ibric S, Baralic K, Buha Djordjevic A, Curcic M, Djukic-Cosic D, Nikolic K (2021) An integrative in silico drug repurposing approach for identification of potential inhibitors of SARS-CoV-2 main protease. Mol Inform 40(5):e2000187. https://doi.org/10.1002/minf.202000187 Nath V, Rohini A, Kumar V (2021) Identification of Mpro inhibitors of SARS-CoV-2 using structure based computational drug repurposing. Biocatal Agric Biotechnol 37:102178. https://doi.org/10.1016/j.bcab.2021.102178 Ibrahim MAA, Abdelrahman AHM, Hegazy MF (2021) In-silico drug repurposing and molecular dynamics puzzled out potential SARS-CoV-2 main protease inhibitors. J Biomol Struct Dyn 39(15):5756–5767. https://doi.org/10.1080/07391102.2020.1791958 Lamb YN (2020) Remdesivir: first approval. Drugs 80(13):1355–1363. https://doi.org/10.1007/s40265-020-01378-w Yin W, Mao C, Luan X, Shen DD, Shen Q, Su H, Wang X, Zhou F, Zhao W, Gao M, Chang S, Xie YC, Tian G, Jiang HW, Tao SC, Shen J, Jiang Y, Jiang H, Xu Y, Zhang S, Zhang Y, Xu HE (2020) Structural basis for inhibition of the RNA-dependent RNA polymerase from SARS-CoV-2 by remdesivir. Science 368(6498):1499–1504. https://doi.org/10.1126/science.abc1560 Kaliamurthi S, Selvaraj G, Selvaraj C, Singh SK, Wei DQ, Peslherbe GH (2021) Structure-based virtual screening reveals ibrutinib and zanubrutinib as potential repurposed drugs against COVID-19. Int J Mol Sci 22(13):7071. https://doi.org/10.3390/ijms22137071 Ezat AA, Elfiky AA, Elshemey WM, Saleh NA (2019) Novel inhibitors against wild-type and mutated HCV NS3 serine protease: an in silico study. Virusdisease 30(2):207–213. https://doi.org/10.1007/s13337-019-00516-7