Generic placeholder image

Mini-Reviews in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1389-5575
ISSN (Online): 1875-5607

Review Article

The Power of Molecular Dynamics Simulations and Their Applications to Discover Cysteine Protease Inhibitors

Author(s): Igor José dos Santos Nascimento*, Joilly Nilce Santana Gomes, Jéssika de Oliveira Viana, Yvnni Maria Sales de Medeiros e Silva, Euzébio Guimarães Barbosa and Ricardo Olimpio de Moura

Volume 24, Issue 11, 2024

Published on: 27 September, 2023

Page: [1125 - 1146] Pages: 22

DOI: 10.2174/1389557523666230901152257

Price: $65

Abstract

A large family of enzymes with the function of hydrolyzing peptide bonds, called peptidases or cysteine proteases (CPs), are divided into three categories according to the peptide chain involved. CPs catalyze the hydrolysis of amide, ester, thiol ester, and thioester peptide bonds. They can be divided into several groups, such as papain-like (CA), viral chymotrypsin-like CPs (CB), papainlike endopeptidases of RNA viruses (CC), legumain-type caspases (CD), and showing active residues of His, Glu/Asp, Gln, Cys (CE). The catalytic mechanism of CPs is the essential cysteine residue present in the active site. These mechanisms are often studied through computational methods that provide new information about the catalytic mechanism and identify inhibitors. The role of computational methods during drug design and development stages is increasing. Methods in Computer-Aided Drug Design (CADD) accelerate the discovery process, increase the chances of selecting more promising molecules for experimental studies, and can identify critical mechanisms involved in the pathophysiology and molecular pathways of action. Molecular dynamics (MD) simulations are essential in any drug discovery program due to their high capacity for simulating a physiological environment capable of unveiling significant inhibition mechanisms of new compounds against target proteins, especially CPs. Here, a brief approach will be shown on MD simulations and how the studies were applied to identify inhibitors or critical information against cysteine protease from several microorganisms, such as Trypanosoma cruzi (cruzain), Trypanosoma brucei (rhodesain), Plasmodium spp. (falcipain), and SARS-CoV-2 (Mpro). We hope the readers will gain new insights and use our study as a guide for potential compound identifications using MD simulations.

« Previous
Graphical Abstract

[1]
Otto, H.H.; Schirmeister, T. Cysteine proteases and their inhibitors. Chem. Rev., 1997, 97(1), 133-172.
[http://dx.doi.org/10.1021/cr950025u] [PMID: 11848867]
[2]
Grzonka, Z.; Kasprzykowski, F.; Wiczk, W. Cysteine Proteases.Industrial Enzymes; Springer Netherlands: Dordrecht, 2007, pp. 181-195.
[http://dx.doi.org/10.1007/1-4020-5377-0_11]
[3]
José dos Santos Nascimento, I.; Mendonça de Aquino, T.; Fernando da Silva Santos-Júnior, P.; Xavier de Araújo-Júnior, J.; Ferreira da Silva-Júnior, E. Molecular modeling applied to design of cysteine protease inhibitors – A powerful tool for the identification of hit compounds against neglected tropical diseases.In: Frontiers in Computational Chemistry; Bentham Science, 2020, p. 5.
[4]
dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva-Júnior, E.F. Drug repurposing: A strategy for discovering inhibitors against emerging viral infections. Curr. Med. Chem., 2021, 28(15), 2887-2942.
[http://dx.doi.org/10.2174/1875533XMTA5rMDYp5] [PMID: 32787752]
[5]
dos Santos Nascimento, I.J.; da Silva-Júnior, E.F.; de Aquino, T.M. Molecular modeling targeting transmembrane serine protease 2 (TMPRSS2) as an alternative drug target against coronaviruses. Curr. Drug Targets, 2022, 23(3), 240-259.
[http://dx.doi.org/10.2174/1389450122666210809090909] [PMID: 34370633]
[6]
dos Santos Nascimento, I.J.; da Silva Rodrigues, É.E.; da Silva, M.F.; de Araújo-Júnior, J.X.; de Moura, R.O. Advances in computational methods to discover new NS2B-NS3 inhibitors useful against dengue and Zika viruses. Curr. Top. Med. Chem., 2022, 22(29), 2435-2462.
[http://dx.doi.org/10.2174/1568026623666221122121330] [PMID: 36415099]
[7]
dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva-Júnior, E.F. Cruzain and rhodesain inhibitors: Last decade of advances in seeking for new compounds against american and african trypanosomiases. Curr. Top. Med. Chem., 2021, 21(21), 1871-1899.
[http://dx.doi.org/10.2174/18734294MTE10MTEoz] [PMID: 33797369]
[8]
Cianni, L.; Feldmann, C.W.; Gilberg, E.; Gütschow, M.; Juliano, L.; Leitão, A.; Bajorath, J.; Montanari, C.A. Can cysteine protease cross-class inhibitors achieve selectivity? J. Med. Chem., 2019, 62(23), 10497-10525.
[http://dx.doi.org/10.1021/acs.jmedchem.9b00683] [PMID: 31361135]
[9]
Verma, S.; Dixit, R.; Pandey, K.C. Cysteine proteases: Modes of activation and future prospects as pharmacological targets. Front. Pharmacol., 2016, 7, 107.
[http://dx.doi.org/10.3389/fphar.2016.00107] [PMID: 27199750]
[10]
Vicik, R.; Busemann, M.; Baumann, K.; Schirmeister, T. Inhibitors of cysteine proteases. Curr. Top. Med. Chem., 2006, 6(4), 331-353.
[http://dx.doi.org/10.2174/156802606776287081] [PMID: 16611146]
[11]
Rawat, A.; Roy, M.; Jyoti, A.; Kaushik, S.; Verma, K.; Srivastava, V.K. Cysteine proteases: Battling pathogenic parasitic protozoans with omnipresent enzymes. Microbiol. Res., 2021, 249, 126784.
[http://dx.doi.org/10.1016/j.micres.2021.126784] [PMID: 33989978]
[12]
Sajid, M.; McKerrow, J.H. Cysteine proteases of parasitic organisms. Mol. Biochem. Parasitol., 2002, 120(1), 1-21.
[http://dx.doi.org/10.1016/S0166-6851(01)00438-8] [PMID: 11849701]
[13]
Tušar, L.; Usenik, A.; Turk, B.; Turk, D. Mechanisms applied by protein inhibitors to inhibit cysteine proteases. Int. J. Mol. Sci., 2021, 22(3), 997.
[http://dx.doi.org/10.3390/ijms22030997] [PMID: 33498210]
[14]
Santos Nascimento, I.J.; Silva-Júnior, E.F.; Aquino, T.M. Repurposing FDA-approved drugs targeting SARS-CoV2 3CL pro: A study by applying virtual screening, molecular dynamics, MM-PBSA calculations and covalent docking. Lett. Drug Des. Discov., 2022, 19(7), 637-653.
[http://dx.doi.org/10.2174/1570180819666220106110133]
[15]
Silva, L.R.; Guimarães, A.S.; do Nascimento, J.; do Santos Nascimento, I.J.; da Silva, E.B.; McKerrow, J.H.; Cardoso, S.H.; da Silva-Júnior, E.F. Computer-aided design of 1,4-naphthoquinone-based inhibitors targeting cruzain and rhodesain cysteine proteases. Bioorg. Med. Chem., 2021, 41, 116213.
[http://dx.doi.org/10.1016/j.bmc.2021.116213] [PMID: 33992862]
[16]
Nascimento, I.J.S.; de Aquino, T.M.; da Silva-Júnior, E.F. The new era of drug discovery: The power of computer-aided drug design (CADD). Lett. Drug Des. Discov., 2022, 19(11), 951-955.
[http://dx.doi.org/10.2174/1570180819666220405225817]
[17]
dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva-Júnior, E.F. Molecular docking and dynamics simulations studies of a dataset of NLRP3 inflammasome inhibitors; Recent Adv. Inflamm. Allergy Drug Discov, 2022.
[http://dx.doi.org/10.2174/2772270816666220126103909] [PMID: 35081900]
[18]
dos Santos Nascimento, I.J. Computer-aided drug design against emerging viruses: Part I. Curr. Top. Med. Chem., 2022, 22(29), 2395-2395.
[http://dx.doi.org/10.2174/156802662229221207124548] [PMID: 36650744]
[19]
Nascimento, I.J. dos S.; Mendonça de Aquino, T.; Ferreira da Silva-Júnior, E. Molecular dynamics applied to discover antiviral agents. In: Frontiers in Computational Chemistry; , 2022, pp. 62-131.
[http://dx.doi.org/10.2174/9789815036848122060005]
[20]
Salo-Ahen, O.M.H.; Alanko, I.; Bhadane, R.; Bonvin, A.M.J.J.; Honorato, R.V.; Hossain, S.; Juffer, A.H.; Kabedev, A.; Lahtela-Kakkonen, M.; Larsen, A.S.; Lescrinier, E.; Marimuthu, P.; Mirza, M.U.; Mustafa, G.; Nunes-Alves, A.; Pantsar, T.; Saadabadi, A.; Singaravelu, K.; Vanmeert, M. Molecular dynamics simulations in drug discovery and pharmaceutical development. Processes, 2020, 9(1), 71.
[http://dx.doi.org/10.3390/pr9010071]
[21]
dos Santos Nascimento, I.J.; da Silva Santos-Júnior, P.F.; de Araújo-Júnior, J.X.; da Silva-Júnior, E.F. Strategies in medicinal chemistry to discovery new hits compounds against ebola virus: Challenges and perspectives in drug discovery. Mini-Reviews. Med. Chem., 2022, 22(22), 2896-2924.
[22]
dos Santos Nascimento, I.J.; de Aquino, T.M.; da Silva Júnior, E.F. Computer-aided drug design of anti-inflammatory agents targeting microsomal prostaglandin E2 synthase-1 (mPGES-1). Curr. Med. Chem., 2022, 29(33), 5397-5419.
[http://dx.doi.org/10.2174/0929867329666220317122948] [PMID: 35301943]
[23]
Burley, S.K.; Berman, H.M.; Bhikadiya, C.; Bi, C.; Chen, L.; Di Costanzo, L.; Christie, C.; Dalenberg, K.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ghosh, S.; Goodsell, D.S.; Green, R.K. Guranović, V.; Guzenko, D.; Hudson, B.P.; Kalro, T.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Periskova, I.; Prlić, A.; Randle, C.; Rose, A.; Rose, P.; Sala, R.; Sekharan, M.; Shao, C.; Tan, L.; Tao, Y.P.; Valasatava, Y.; Voigt, M.; Westbrook, J.; Woo, J.; Yang, H.; Young, J.; Zhuravleva, M.; Zardecki, C. RCSB Protein Data Bank: Biological macromolecular structures enabling research and education in fundamental biology, biomedicine, biotechnology and energy. Nucleic Acids Res., 2019, 47(D1), D464-D474.
[http://dx.doi.org/10.1093/nar/gky1004] [PMID: 30357411]
[24]
Rose, Y.; Duarte, J.M.; Lowe, R.; Segura, J.; Bi, C.; Bhikadiya, C.; Chen, L.; Rose, A.S.; Bittrich, S.; Burley, S.K.; Westbrook, J.D. RCSB protein data bank: Architectural advances towards integrated searching and efficient access to macromolecular structure data from the PDB archive. J. Mol. Biol., 2021, 433(11), 166704.
[http://dx.doi.org/10.1016/j.jmb.2020.11.003] [PMID: 33186584]
[25]
Maia, E.H.B.; Assis, L.C.; de Oliveira, T.A.; da Silva, A.M.; Taranto, A.G. Structure-based virtual screening: From classical to artificial intelligence. Front Chem., 2020, 8, 343.
[http://dx.doi.org/10.3389/fchem.2020.00343] [PMID: 32411671]
[26]
Karplus, M.; Petsko, G.A. Molecular dynamics simulations in biology. Nature, 1990, 347(6294), 631-639.
[http://dx.doi.org/10.1038/347631a0] [PMID: 2215695]
[27]
Brodie, N.I.; Popov, K.I.; Petrotchenko, E.V.; Dokholyan, N.V.; Borchers, C.H. Conformational ensemble of native α-synuclein in solution as determined by short-distance crosslinking constraint guided discrete molecular dynamics simulations. PLOS Comput. Biol., 2019, 15(3), e1006859.
[http://dx.doi.org/10.1371/journal.pcbi.1006859] [PMID: 30917118]
[28]
Rehman, M.; AlAjmi, M.; Hussain, A.; Rather, G.; Khan, M. High throughput virtual screening, molecular dynamics simulation, and enzyme kinetics identified ZINC84525623 as a potential inhibitor of NDM-1. Int. J. Mol. Sci., 2019, 20(4), 819.
[http://dx.doi.org/10.3390/ijms20040819] [PMID: 30769822]
[29]
Harpole, T.J.; Delemotte, L. Conformational landscapes of membrane proteins delineated by enhanced sampling molecular dynamics simulations. Biochim. Biophys. Acta Biomembr., 2018, 1860(4), 909-926.
[http://dx.doi.org/10.1016/j.bbamem.2017.10.033] [PMID: 29113819]
[30]
Yoo, J.; Winogradoff, D.; Aksimentiev, A. Molecular dynamics simulations of DNA–DNA and DNA–protein interactions. Curr. Opin. Struct. Biol., 2020, 64, 88-96.
[http://dx.doi.org/10.1016/j.sbi.2020.06.007] [PMID: 32682257]
[31]
Hollingsworth, S.A.; Dror, R.O. Molecular dynamics simulation for all. Neuron, 2018, 99(6), 1129-1143.
[http://dx.doi.org/10.1016/j.neuron.2018.08.011] [PMID: 30236283]
[32]
MacKerell, A.D., Jr; Bashford, D.; Bellott, M.; Dunbrack, R.L., Jr; Evanseck, J.D.; Field, M.J.; Fischer, S.; Gao, J.; Guo, H.; Ha, S.; Joseph-McCarthy, D.; Kuchnir, L.; Kuczera, K.; Lau, F.T.K.; Mattos, C.; Michnick, S.; Ngo, T.; Nguyen, D.T.; Prodhom, B.; Reiher, W.E.; Roux, B.; Schlenkrich, M.; Smith, J.C.; Stote, R.; Straub, J.; Watanabe, M.; Wiórkiewicz-Kuczera, J.; Yin, D.; Karplus, M. All-atom empirical potential for molecular modeling and dynamics studies of proteins. J. Phys. Chem. B, 1998, 102(18), 3586-3616.
[http://dx.doi.org/10.1021/jp973084f] [PMID: 24889800]
[33]
González, M.A. Force fields and molecular dynamics simulations. Éc. Thémat. Soc. Fr. Neutron., 2011, 12, 169-200.
[34]
Chen, I.J.; Yin, D.; MacKerell, A.D. Jr Combinedab initio/empirical approach for optimization of Lennard-Jones parameters for polar-neutral compounds. J. Comput. Chem., 2002, 23(2), 199-213.
[http://dx.doi.org/10.1002/jcc.1166] [PMID: 11924734]
[35]
Maggs, A.C.; Rossetto, V. Local simulation algorithms for Coulomb interactions. Phys. Rev. Lett., 2002, 88(19), 196402.
[http://dx.doi.org/10.1103/PhysRevLett.88.196402] [PMID: 12005652]
[36]
He, X.; Man, V.H.; Yang, W.; Lee, T.S.; Wang, J. A fast and high quality charge model for the next generation general AMBER force field. J. Chem. Phys., 2020, 153(11), 114502.
[http://dx.doi.org/10.1063/5.0019056] [PMID: 32962378]
[37]
Huang, J.; Rauscher, S.; Nawrocki, G.; Ran, T.; Feig, M.; de Groot, B.L.; Grubmüller, H.; MacKerell, A.D., Jr CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nat. Methods, 2017, 14(1), 71-73.
[http://dx.doi.org/10.1038/nmeth.4067] [PMID: 27819658]
[38]
Lins, R.D.; Hünenberger, P.H. A new GROMOS force field for hexopyranose-based carbohydrates. J. Comput. Chem., 2005, 26(13), 1400-1412.
[http://dx.doi.org/10.1002/jcc.20275] [PMID: 16035088]
[39]
Silva, T.F.D.; Vila-Viçosa, D.; Reis, P.B.P.S.; Victor, B.L.; Diem, M.; Oostenbrink, C.; Machuqueiro, M. The impact of using single atomistic long-range cutoff schemes with the GROMOS 54A7 force field. J. Chem. Theory Comput., 2018, 14(11), 5823-5833.
[http://dx.doi.org/10.1021/acs.jctc.8b00758] [PMID: 30354115]
[40]
Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol., 2002, 9(9), 646-652.
[http://dx.doi.org/10.1038/nsb0902-646] [PMID: 12198485]
[41]
Sargsyan, K.; Grauffel, C.; Lim, C. How molecular size impacts RMSD applications in molecular dynamics simulations. J. Chem. Theory Comput., 2017, 13(4), 1518-1524.
[http://dx.doi.org/10.1021/acs.jctc.7b00028] [PMID: 28267328]
[42]
Bibi, S.; Khan, M.S.; El-Kafrawy, S.A.; Alandijany, T.A.; El-Daly, M.M.; Yousafi, Q.; Fatima, D.; Faizo, A.A.; Bajrai, L.H.; Azhar, E.I. Virtual screening and molecular dynamics simulation analysis of Forsythoside A as a plant-derived inhibitor of SARS-CoV-2 3CLpro. Saudi Pharm. J., 2022, 30(7), 979-1002.
[http://dx.doi.org/10.1016/j.jsps.2022.05.003] [PMID: 35637849]
[43]
Lobanov, M.Y.; Bogatyreva, N.S.; Galzitskaya, O.V. Radius of gyration as an indicator of protein structure compactness. Mol. Biol., 2008, 42(4), 623-628.
[http://dx.doi.org/10.1134/S0026893308040195] [PMID: 18856071]
[44]
Weiss, M.S.; Brandl, M.; Sühnel, J.; Pal, D.; Hilgenfeld, R. More hydrogen bonds for the (structural) biologist. Trends Biochem. Sci., 2001, 26(9), 521-523.
[http://dx.doi.org/10.1016/S0968-0004(01)01935-1] [PMID: 11551776]
[45]
Åqvist, J.; Medina, C.; Samuelsson, J.E. A new method for predicting binding affinity in computer-aided drug design. Protein Eng. Des. Sel., 1994, 7(3), 385-391.
[http://dx.doi.org/10.1093/protein/7.3.385] [PMID: 8177887]
[46]
Hou, T.; Wang, J.; Li, Y.; Wang, W. Assessing the performance of the molecular mechanics/Poisson Boltzmann surface area and molecular mechanics/generalized Born surface area methods. II. The accuracy of ranking poses generated from docking. J. Comput. Chem., 2011, 32(5), 866-877.
[http://dx.doi.org/10.1002/jcc.21666] [PMID: 20949517]
[47]
Kollman, P.A.; Massova, I.; Reyes, C.; Kuhn, B.; Huo, S.; Chong, L.; Lee, M.; Lee, T.; Duan, Y.; Wang, W.; Donini, O.; Cieplak, P.; Srinivasan, J.; Case, D.A.; Cheatham, T.E., III Calculating structures and free energies of complex molecules: Combining molecular mechanics and continuum models. Acc. Chem. Res., 2000, 33(12), 889-897.
[http://dx.doi.org/10.1021/ar000033j] [PMID: 11123888]
[48]
Kästner, J. Umbrella sampling. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2011, 1(6), 932-942.
[http://dx.doi.org/10.1002/wcms.66]
[49]
Ghosh, S.; Chetia, D.; Gogoi, N.; Rudrapal, M. Design, molecular docking, drug-likeness, and molecular dynamics studies of 1,2,4-trioxane derivatives as novel Plasmodium falciparum falcipain-2 (FP-2) inhibitors. BioTechnologia, 2021, 102(3), 257-275.
[http://dx.doi.org/10.5114/bta.2021.108722] [PMID: 36606151]
[50]
Chitranshi, N.; Kumar, A.; Sheriff, S.; Gupta, V.; Godinez, A.; Saks, D.; Sarkar, S.; Shen, T.; Mirzaei, M.; Basavarajappa, D.; Abyadeh, M.; Singh, S.K.; Dua, K.; Zhang, K.Y.J.; Graham, S.L.; Gupta, V. Identification of novel cathepsin B inhibitors with implications in Alzheimer’s disease: Computational refining and biochemical evaluation. Cells, 2021, 10(8), 1946.
[http://dx.doi.org/10.3390/cells10081946] [PMID: 34440715]
[51]
Berendsen, H.J.C.; van der Spoel, D.; van Drunen, R. GROMACS: A message-passing parallel molecular dynamics implementation. Comput. Phys. Commun., 1995, 91(1-3), 43-56.
[http://dx.doi.org/10.1016/0010-4655(95)00042-E]
[52]
Phillips, J.C.; Braun, R.; Wang, W.; Gumbart, J.; Tajkhorshid, E.; Villa, E.; Chipot, C.; Skeel, R.D.; Kalé, L.; Schulten, K. Scalable molecular dynamics with NAMD. J. Comput. Chem., 2005, 26(16), 1781-1802.
[http://dx.doi.org/10.1002/jcc.20289] [PMID: 16222654]
[53]
Brooks, B.R.; Brooks, C.L., III; Mackerell, A.D., Jr; Nilsson, L.; Petrella, R.J.; Roux, B.; Won, Y.; Archontis, G.; Bartels, C.; Boresch, S.; Caflisch, A.; Caves, L.; Cui, Q.; Dinner, A.R.; Feig, M.; Fischer, S.; Gao, J.; Hodoscek, M. Im, W.; Kuczera, K.; Lazaridis, T.; Ma, J.; Ovchinnikov, V.; Paci, E.; Pastor, R.W.; Post, C.B.; Pu, J.Z.; Schaefer, M.; Tidor, B.; Venable, R.M.; Woodcock, H.L.; Wu, X.; Yang, W.; York, D.M.; Karplus, M. CHARMM: The biomolecular simulation program. J. Comput. Chem., 2009, 30(10), 1545-1614.
[http://dx.doi.org/10.1002/jcc.21287] [PMID: 19444816]
[54]
Case, D.A.; Cheatham, T.E., III; Darden, T.; Gohlke, H.; Luo, R.; Merz, K.M., Jr; Onufriev, A.; Simmerling, C.; Wang, B.; Woods, R.J. The Amber biomolecular simulation programs. J. Comput. Chem., 2005, 26(16), 1668-1688.
[http://dx.doi.org/10.1002/jcc.20290] [PMID: 16200636]
[55]
Martínez, L.; Andrade, R.; Birgin, E.G.; Martínez, J.M. PACKMOL: A package for building initial configurations for molecular dynamics simulations. J. Comput. Chem., 2009, 30(13), 2157-2164.
[http://dx.doi.org/10.1002/jcc.21224] [PMID: 19229944]
[56]
Contreras-Riquelme, S.; Garate, J.A.; Perez-Acle, T.; Martin, A.J.M. RIP-MD: A tool to study residue interaction networks in protein molecular dynamics. PeerJ, 2018, 6, e5998.
[http://dx.doi.org/10.7717/peerj.5998] [PMID: 30568854]
[57]
Żaczek, S. MDMS: Software facilitating performing molecular dynamics simulations. J. Comput. Chem., 2020, 41(3), 266-271.
[http://dx.doi.org/10.1002/jcc.26090] [PMID: 31660624]
[58]
Gecht, M.; Siggel, M.; Linke, M.; Hummer, G.; Köfinger, J. MDBenchmark: A toolkit to optimize the performance of molecular dynamics simulations. J. Chem. Phys., 2020, 153(14), 144105.
[http://dx.doi.org/10.1063/5.0019045] [PMID: 33086826]
[59]
Bedart, C.; Renault, N.; Chavatte, P.; Porcherie, A.; Lachgar, A.; Capron, M.; Farce, A. SINAPs: A software tool for analysis and visualization of interaction networks of molecular dynamics simulations. J. Chem. Inf. Model., 2022, 62(6), 1425-1436.
[http://dx.doi.org/10.1021/acs.jcim.1c00854] [PMID: 35239339]
[60]
Frappier, V.; Chartier, M.; Najmanovich, R.J. ENCoM server: Exploring protein conformational space and the effect of mutations on protein function and stability. Nucleic Acids Res., 2015, 43(W1), W395-W400.
[http://dx.doi.org/10.1093/nar/gkv343] [PMID: 25883149]
[61]
Jo, S.; Kim, T.; Iyer, V.G. Im, W. CHARMM-GUI: A web-based graphical user interface for CHARMM. J. Comput. Chem., 2008, 29(11), 1859-1865.
[http://dx.doi.org/10.1002/jcc.20945] [PMID: 18351591]
[62]
Hospital, A.; Andrio, P.; Fenollosa, C.; Cicin-Sain, D.; Orozco, M.; Gelpí, J.L. MDWeb and MDMoby: An integrated web-based platform for molecular dynamics simulations. Bioinformatics, 2012, 28(9), 1278-1279.
[http://dx.doi.org/10.1093/bioinformatics/bts139] [PMID: 22437851]
[63]
Heo, L.; Feig, M. PREFMD: A web server for protein structure refinement via molecular dynamics simulations. Bioinformatics, 2018, 34(6), 1063-1065.
[http://dx.doi.org/10.1093/bioinformatics/btx726] [PMID: 29126101]
[64]
Yang, J.F.; Wang, F.; Chen, Y.Z.; Hao, G.F.; Yang, G.F. LARMD: Integration of bioinformatic resources to profile ligand-driven protein dynamics with a case on the activation of estrogen receptor. Brief. Bioinform., 2020, 21(6), 2206-2218.
[http://dx.doi.org/10.1093/bib/bbz141] [PMID: 31799600]
[65]
Chakrabarty, B.; Naganathan, V.; Garg, K.; Agarwal, Y.; Parekh, N. NAPS update: Network analysis of molecular dynamics data and protein–nucleic acid complexes. Nucleic Acids Res., 2019, 47(W1), W462-W470.
[http://dx.doi.org/10.1093/nar/gkz399] [PMID: 31106363]
[66]
Rodrigues, C.H.M.; Pires, D.E.V.; Ascher, D.B. DYNAMUT2: Assessing changes in stability and flexibility upon single and multiple point missense mutations. Protein Sci., 2021, 30(1), 60-69.
[http://dx.doi.org/10.1002/pro.3942] [PMID: 32881105]
[67]
Sheik Amamuddy, O.; Glenister, M.; Tshabalala, T.; Tastan Bishop, Ö. MDM-TASK-web: MD-TASK and MODE-TASK web server for analyzing protein dynamics. Comput. Struct. Biotechnol. J., 2021, 19, 5059-5071.
[http://dx.doi.org/10.1016/j.csbj.2021.08.043] [PMID: 34589183]
[68]
Hao, J.H.; Zheng, D.J.; Ye, Y.H.; Yu, J.T.; Li, X.Y.; Xiong, M.J.; Jiang, W.H.; He, K.P.; Li, P.Y.; Lv, Y.S.; Gu, W.M.; Lai, L.H.; Wu, Y.D.; Cao, S.L. Atomevo: A web server combining protein modelling, docking, molecular dynamic simulation and MMPBSA analysis of Candida antarctica lipase B (CalB) fusion protein. Bioresour. Bioprocess., 2022, 9(1), 53.
[http://dx.doi.org/10.1186/s40643-022-00546-y]
[69]
Stone, J.E.; Hallock, M.J.; Phillips, J.C.; Peterson, J.R.; Luthey-Schulten, Z.; Schulten, K. Evaluation of emerging energy-efficient heterogeneous computing platforms for biomolecular and cellular simulation workloads. Proceedings of the 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, IL, USA2016, pp. 89-100.
[http://dx.doi.org/10.1109/IPDPSW.2016.130]
[70]
Albaugh, A.; Boateng, H.A.; Bradshaw, R.T.; Demerdash, O.N.; Dziedzic, J.; Mao, Y.; Margul, D.T.; Swails, J.; Zeng, Q.; Case, D.A.; Eastman, P.; Wang, L.P.; Essex, J.W.; Head-Gordon, M.; Pande, V.S.; Ponder, J.W.; Shao, Y.; Skylaris, C.K.; Todorov, I.T.; Tuckerman, M.E.; Head-Gordon, T. Advanced potential energy surfaces for molecular simulation. J. Phys. Chem. B, 2016, 120(37), 9811-9832.
[http://dx.doi.org/10.1021/acs.jpcb.6b06414] [PMID: 27513316]
[71]
Lopes, P.E.M.; Huang, J.; Shim, J.; Luo, Y.; Li, H.; Roux, B.; MacKerell, A.D., Jr Polarizable force field for peptides and proteins based on the classical drude oscillator. J. Chem. Theory Comput., 2013, 9(12), 5430-5449.
[http://dx.doi.org/10.1021/ct400781b] [PMID: 24459460]
[72]
Best, R.B.; Zhu, X.; Shim, J.; Lopes, P.E.M.; Mittal, J.; Feig, M.; MacKerell, A.D., Jr Optimization of the additive CHARMM all atom protein force field targeting improved sampling of the backbone φ, ψ and side-chain χ(1) and χ(2) dihedral angles. J. Chem. Theory Comput., 2012, 8(9), 3257-3273.
[http://dx.doi.org/10.1021/ct300400x] [PMID: 23341755]
[73]
Wang, L.P.; Martinez, T.J.; Pande, V.S. Building force fields: An automatic, systematic, and reproducible approach. J. Phys. Chem. Lett., 2014, 5(11), 1885-1891.
[http://dx.doi.org/10.1021/jz500737m] [PMID: 26273869]
[74]
Leimkuhler, B.; Margul, D.T.; Tuckerman, M.E. Stochastic, resonance-free multiple time-step algorithm for molecular dynamics with very large time steps. Mol. Phys., 2013, 111(22-23), 3579-3594.
[http://dx.doi.org/10.1080/00268976.2013.844369]
[75]
Leimkuhler, B.; Matthews, C. Efficient molecular dynamics using geodesic integration and solvent–solute splitting. Proc.- Royal Soc., Math. Phys. Eng. Sci., 2016, 472(2189), 20160138.
[http://dx.doi.org/10.1098/rspa.2016.0138] [PMID: 27279779]
[76]
Sharma, V.; Wakode, S.; Kumar, H. Structure- and ligand-based drug design. In: Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences; Elsevier, 2021, pp. 27-53.
[http://dx.doi.org/10.1016/B978-0-12-821748-1.00004-X]
[77]
Liu, X.; Shi, D.; Zhou, S.; Liu, H.; Liu, H.; Yao, X. Molecular dynamics simulations and novel drug discovery. Expert Opin. Drug Discov., 2018, 13(1), 23-37.
[http://dx.doi.org/10.1080/17460441.2018.1403419] [PMID: 29139324]
[78]
Skariyachan, S.; Manjunath, M.; Bachappanavar, N. Screening of potential lead molecules against prioritised targets of multi-drug-resistant- Acinetobacter baumannii – insights from molecular docking, molecular dynamic simulations and in vitro assays. J. Biomol. Struct. Dyn., 2019, 37(5), 1146-1169.
[http://dx.doi.org/10.1080/07391102.2018.1451387] [PMID: 29529934]
[79]
Lourenço, E.M.G.; Fernandes, J.M.; Carvalho, V.F.; Grougnet, R.; Martins, M.A.; Jordão, A.K.; Zucolotto, S.M.; Barbosa, E.G. Identification of a selective PDE4B inhibitor from Bryophyllum pinnatum by target fishing study and In Vitro evaluation of quercetin 3-O-α-L-arabinopyranosyl-(1→2)-O-α-L-rhamnopyranoside. Front. Pharmacol., 2020, 10, 1582.
[http://dx.doi.org/10.3389/fphar.2019.01582] [PMID: 32038254]
[80]
Yan, W.; Lin, G.; Zhang, R.; Liang, Z.; Wu, W. Studies on the bioactivities and molecular mechanism of antioxidant peptides by 3D-QSAR, in vitro evaluation and molecular dynamic simulations. Food Funct., 2020, 11(4), 3043-3052.
[http://dx.doi.org/10.1039/C9FO03018B] [PMID: 32190865]
[81]
Jairajpuri, D.S.; Hussain, A.; Nasreen, K.; Mohammad, T.; Anjum, F.; Tabish Rehman, M.; Mustafa Hasan, G.; Alajmi, M.F.; Imtaiyaz Hassan, M. Identification of natural compounds as potent inhibitors of SARS-CoV-2 main protease using combined docking and molecular dynamics simulations. Saudi J. Biol. Sci., 2021, 28(4), 2423-2431.
[http://dx.doi.org/10.1016/j.sjbs.2021.01.040] [PMID: 33526965]
[82]
Othman, I.M.M.; Mahross, M.H.; Gad-Elkareem, M.A.M.; Rudrapal, M.; Gogoi, N.; Chetia, D.; Aouadi, K.; Snoussi, M.; Kadri, A. Toward a treatment of antibacterial and antifungal infections: Design, synthesis and in vitro activity of novel arylhydrazothiazolylsulfonamides analogues and their insight of DFT, docking and molecular dynamic simulations. J. Mol. Struct., 2021, 1243, 130862.
[http://dx.doi.org/10.1016/j.molstruc.2021.130862]
[83]
Maleki, M.F.; Nadri, H.; Kianfar, M.; Edraki, N.; Eisvand, F.; Ghodsi, R.; Mohajeri, S.A.; Hadizadeh, F. Design and synthesis of new carbamates as inhibitors for fatty acid amide hydrolase and cholinesterases: Molecular dynamic, in vitro and in vivo studies. Bioorg. Chem., 2021, 109, 104684.
[http://dx.doi.org/10.1016/j.bioorg.2021.104684] [PMID: 33607363]
[84]
Eldehna, W.M.; El Hassab, M.A.; Elsayed, Z.M.; Al-Warhi, T.; Elkady, H.; Abo-Ashour, M.F.; Abourehab, M.A.S.; Eissa, I.H.; Abdel-Aziz, H.A. Design, synthesis, in vitro biological assessment and molecular modeling insights for novel 3-(naphthalen-1-yl)-4,5-dihydropyrazoles as anticancer agents with potential EGFR inhibitory activity. Sci. Rep., 2022, 12(1), 12821.
[http://dx.doi.org/10.1038/s41598-022-15050-8] [PMID: 35896557]
[85]
Uddin, A.; Singh, V.; Irfan, I.; Mohammad, T.; Singh Hada, R.; Imtaiyaz Hassan, M.; Abid, M.; Singh, S. Identification and structure–activity relationship (SAR) studies of carvacrol derivatives as potential anti-malarial against Plasmodium falciparum falcipain-2 protease. Bioorg. Chem., 2020, 103, 104142.
[http://dx.doi.org/10.1016/j.bioorg.2020.104142] [PMID: 32763521]
[86]
Maia, M.; Andrade, R.; Sousa, J.; Sousa, N.; Rodrigues, G.; Menezes, R.; Silva, M.; Tavares, J.; Rodrigues, K.; Scotti, L.; Scotti, M. Virtual screening based on ligand and structure with in vitro assessment of neolignans against trypanosoma cruzi. J. Braz. Chem. Soc., 2023.
[http://dx.doi.org/10.21577/0103-5053.20220113]
[87]
Sartori, G.R.; Leitão, A.; Montanari, C.A.; Laughton, C.A. Ligand induced conformational selection predicts the selectivity of cysteine protease inhibitors. PLoS One, 2019, 14(12), e0222055.
[http://dx.doi.org/10.1371/journal.pone.0222055] [PMID: 31856175]
[88]
Luchi, A.M.; Villafañe, R.N.; Gómez Chávez, J.L.; Bogado, M.L.; Angelina, E.L.; Peruchena, N.M. Combining charge density analysis with machine learning tools to investigate the cruzain inhibition mechanism. ACS Omega, 2019, 4(22), 19582-19594.
[http://dx.doi.org/10.1021/acsomega.9b01934] [PMID: 31788588]
[89]
Silva, J.R.A.; Cianni, L.; Araujo, D.; Batista, P.H.J.; de Vita, D.; Rosini, F.; Leitão, A.; Lameira, J.; Montanari, C.A. Assessment of the cruzain cysteine protease reversible and irreversible covalent inhibition mechanism. J. Chem. Inf. Model., 2020, 60(3), 1666-1677.
[http://dx.doi.org/10.1021/acs.jcim.9b01138] [PMID: 32126170]
[90]
Dos Santos, A.M.; Cianni, L.; De Vita, D.; Rosini, F.; Leitão, A.; Laughton, C.A.; Lameira, J.; Montanari, C.A. Experimental study and computational modelling of cruzain cysteine protease inhibition by dipeptidyl nitriles. Phys. Chem. Chem. Phys., 2018, 20(37), 24317-24328.
[http://dx.doi.org/10.1039/C8CP03320J] [PMID: 30211406]
[91]
Cianni, L.; Sartori, G.; Rosini, F.; De Vita, D.; Pires, G.; Lopes, B.R.; Leitão, A.; Burtoloso, A.C.B.; Montanari, C.A. Leveraging the cruzain S3 subsite to increase affinity for reversible covalent inhibitors. Bioorg. Chem., 2018, 79, 285-292.
[http://dx.doi.org/10.1016/j.bioorg.2018.04.006] [PMID: 29783099]
[92]
Cianni, L.; Rocho, F.R.; Rosini, F.; Bonatto, V.; Ribeiro, J.F.R.; Lameira, J.; Leitão, A.; Shamim, A.; Montanari, C.A. Optimization strategy of single-digit nanomolar cross-class inhibitors of mammalian and protozoa cysteine proteases. Bioorg. Chem., 2020, 101, 104039.
[http://dx.doi.org/10.1016/j.bioorg.2020.104039] [PMID: 32629285]
[93]
Hoelz, L.V.B.; Leal, V.F.; Rodrigues, C.R.; Pascutti, P.G.; Albuquerque, M.G.; Muri, E.M.F.; Dias, L.R.S. Molecular dynamics simulations of the free and inhibitor-bound cruzain systems in aqueous solvent: Insights on the inhibition mechanism in acidic pH. J. Biomol. Struct. Dyn., 2016, 34(9), 1969-1978.
[http://dx.doi.org/10.1080/07391102.2015.1100139] [PMID: 26414241]
[94]
Saraiva, Á.P.B.; Miranda, R.M.; Valente, R.P.P.; Araújo, J.O.; Souza, R.N.B.; Costa, C.H.S.; Oliveira, A.R.S.; Almeida, M.O.; Figueiredo, A.F.; Ferreira, J.E.V.; Alves, C.N.; Honorio, K.M. Molecular description of α‐keto‐based inhibitors of cruzain with activity against Chagas disease combining 3D QSAR studies and molecular dynamics. Chem. Biol. Drug Des., 2018, 92(2), 1475-1487.
[http://dx.doi.org/10.1111/cbdd.13313] [PMID: 29682904]
[95]
da Costa, A.P.L.; Silva, J.R.A.; de Molfetta, F.A. Computational discovery of sulfonamide derivatives as potential inhibitors of the cruzain enzyme from T. cruzi by molecular docking, molecular dynamics and MM/GBSA approaches. Mol. Simul., 2022, 48(18), 1678-1687.
[http://dx.doi.org/10.1080/08927022.2022.2120625]
[96]
Souza, A.; Cardoso, F.; Martins, L.; Alves, C.; Silva, J.; Molfetta, F. Molecular modelling study of heteroarylamide/sulfonamide compounds with antitrypanosomal activity. J. Braz. Chem. Soc., 2021, 32(1), 83-97.
[http://dx.doi.org/10.21577/0103-5053.20200158]
[97]
Freitas, P.; Castilho, T.; de Almeida, L.; Maciel-Rezende, C.; Costa, L.; Viegas, Junior C.; Marques, M.; dos Santos, M.; da Silveira, N. An in silico study of benzophenone derivatives as potential non-competitive inhibitors of trypanosoma cruzi and leishmania amazonensis cysteine proteinases. J. Braz. Chem. Soc., 2017, 29(3), 1-13.
[http://dx.doi.org/10.21577/0103-5053.20170164]
[98]
Toman, N.P.; Kamenik, A.S.; Santos, L.H.; Hofer, F.; Liedl, K.R.; Ferreira, R.S. Profiling selectivity of chagasin mutants towards cysteine proteases cruzain or cathepsin L through molecular dynamics simulations. J. Biomol. Struct. Dyn., 2021, 39(16), 5940-5952.
[http://dx.doi.org/10.1080/07391102.2020.1796797] [PMID: 32715978]
[99]
Martins, L.C.; Torres, P.H.M.; de Oliveira, R.B.; Pascutti, P.G.; Cino, E.A.; Ferreira, R.S. Investigation of the binding mode of a novel cruzain inhibitor by docking, molecular dynamics, ab initio and MM/PBSA calculations. J. Comput. Aided Mol. Des., 2018, 32(5), 591-605.
[http://dx.doi.org/10.1007/s10822-018-0112-3] [PMID: 29564808]
[100]
Santos, L.H.; Waldner, B.J.; Fuchs, J.E.; Pereira, G.A.N.; Liedl, K.R.; Caffarena, E.R.; Ferreira, R.S. Understanding structure–activity relationships for trypanosomal cysteine protease inhibitors by simulations and free energy calculations. J. Chem. Inf. Model., 2019, 59(1), 137-148.
[http://dx.doi.org/10.1021/acs.jcim.8b00557] [PMID: 30532974]
[101]
Di Chio, C.; Previti, S.; Amendola, G.; Ravichandran, R.; Wagner, A.; Cosconati, S.; Hellmich, U.A.; Schirmeister, T.; Zappalà, M.; Ettari, R. Development of novel dipeptide nitriles as inhibitors of rhodesain of Trypanosoma brucei rhodesiense. Eur. J. Med. Chem., 2022, 236, 114328.
[http://dx.doi.org/10.1016/j.ejmech.2022.114328] [PMID: 35385806]
[102]
Previti, S.; Ettari, R.; Cosconati, S.; Amendola, G.; Chouchene, K.; Wagner, A.; Hellmich, U.A.; Ulrich, K.; Krauth-Siegel, R.L.; Wich, P.R.; Schmid, I.; Schirmeister, T.; Gut, J.; Rosenthal, P.J.; Grasso, S.; Zappalà, M. Development of novel peptide-based michael acceptors targeting rhodesain and falcipain-2 for the treatment of neglected tropical diseases (NTDs). J. Med. Chem., 2017, 60(16), 6911-6923.
[http://dx.doi.org/10.1021/acs.jmedchem.7b00405] [PMID: 28763614]
[103]
Previti, S.; Ettari, R.; Calcaterra, E.; Di Chio, C.; Ravichandran, R.; Zimmer, C.; Hammerschmidt, S.; Wagner, A.; Bogacz, M.; Cosconati, S.; Schirmeister, T.; Zappalà, M. Development of urea-bond-containing michael acceptors as antitrypanosomal agents targeting rhodesain. ACS Med. Chem. Lett., 2022, 13(7), 1083-1090.
[http://dx.doi.org/10.1021/acsmedchemlett.2c00084] [PMID: 35859868]
[104]
Klein, P.; Johe, P.; Wagner, A.; Jung, S.; Kühlborn, J.; Barthels, F.; Tenzer, S.; Distler, U.; Waigel, W.; Engels, B.; Hellmich, U.A.; Opatz, T.; Schirmeister, T. New cysteine protease inhibitors: Electrophilic (Het)arenes and unexpected prodrug identification for the trypanosoma protease rhodesain. Molecules, 2020, 25(6), 1451.
[http://dx.doi.org/10.3390/molecules25061451] [PMID: 32210166]
[105]
Alam, B.; Biswas, S. Inhibition of Plasmodium falciparum cysteine protease falcipain-2 by a human cross-class inhibitor serpinB3: A mechanistic insight. Biochim. Biophys. Acta. Proteins Proteomics, 2019, 1867(9), 854-865.
[http://dx.doi.org/10.1016/j.bbapap.2019.06.012] [PMID: 31247344]
[106]
Hernández-González, J.E.; Salas-Sarduy, E.; Hernández Ramírez, L.F.; Pascual, M.J.; Álvarez, D.E.; Pabón, A.; Leite, V.B.P.; Pascutti, P.G.; Valiente, P.A. Identification of (4-(9H-fluoren-9-yl) piperazin-1-yl) methanone derivatives as falcipain 2 inhibitors active against Plasmodium falciparum cultures. Biochim. Biophys. Acta, Gen. Subj., 2018, 1862(12), 2911-2923.
[http://dx.doi.org/10.1016/j.bbagen.2018.09.015] [PMID: 30253205]
[107]
Rajguru, T.; Bora, D.; Modi, M.K. Identification of promising inhibitors for Plasmodium haemoglobinase Falcipain-2, using virtual screening, molecular docking, and MD Simulation. J. Mol. Struct., 2022, 1248, 131427.
[http://dx.doi.org/10.1016/j.molstruc.2021.131427]
[108]
Salawu, E.O. In silico study reveals how E64 approaches, binds to, and inhibits falcipain-2 of Plasmodium falciparum that causes malaria in humans. Sci. Rep., 2018, 8(1), 16380.
[http://dx.doi.org/10.1038/s41598-018-34622-1] [PMID: 30401806]
[109]
Nkungli, N.K.; Fouegue, A.D.T.; Tasheh, S.N.; Bine, F.K.; Hassan, A.U.; Ghogomu, J.N. In silico investigation of falcipain-2 inhibition by hybrid benzimidazole-thiosemicarbazone antiplasmodial agents: A molecular docking, molecular dynamics simulation, and kinetics study. Mol. Divers., 2023, 1-22.
[http://dx.doi.org/10.1007/s11030-022-10594-3] [PMID: 36622482]
[110]
dos Santos Nascimento, I.J.; de Moura, R.O. Would the development of a multitarget inhibitor of 3CLpro and TMPRSS2 be promising in the fight against SARS-CoV-2? Med. Chem., 2022, 19(5), 405-412.
[111]
Structure-based drug discovery approaches applied to SARS-CoV- 2 (COVID-19).Nascimento, I.J. dos S.; de Aquino, T.M.; da Silva- Júnior, E.F., Eds.; Pharmaceuticals for Targeting Coronaviruses; Bentham Science Publishers, 2022, 1-61.
[http://dx.doi.org/10.2174/9789815051308122010003]
[112]
Nascimento, I.J. dos S.; Silva, L.R.; da Silva-Júnior, E.F. Challenges in designing antiviral agents. In: Viral Infections and Antiviral Therapies; Elsevier, 2023, pp. 169-209.
[http://dx.doi.org/10.1016/B978-0-323-91814-5.00017-9]
[113]
Alhadrami, H.A.; Burgio, G.; Thissera, B.; Orfali, R.; Jiffri, S.E.; Yaseen, M.; Sayed, A.M.; Rateb, M.E. Neoechinulin a as a promising SARS-CoV-2 Mpro inhibitor: In vitro and in silico study showing the ability of simulations in discerning active from inactive enzyme inhibitors. Mar. Drugs, 2022, 20(3), 163.
[http://dx.doi.org/10.3390/md20030163] [PMID: 35323462]
[114]
Gupta, A.; Sahu, N.; Singh, A.P.; Singh, V.K.; Singh, S.C.; Upadhye, V.J.; Mathew, A.T.; Kumar, R.; Sinha, R.P. Exploration of novel lichen compounds as inhibitors of SARS-CoV-2 Mpro: Ligand-based design, molecular dynamics, and ADMET analyses. Appl. Biochem. Biotechnol., 2022, 194(12), 6386-6406.
[http://dx.doi.org/10.1007/s12010-022-04103-3] [PMID: 35921031]
[115]
Mohan, A.; Rendine, N.; Mohammed, M.K.S.; Jeeva, A.; Ji, H.F.; Talluri, V.R. Structure-based virtual screening, in silico docking, ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro. Mol. Divers., 2022, 26(3), 1645-1661.
[http://dx.doi.org/10.1007/s11030-021-10298-0] [PMID: 34480682]
[116]
Shree, P.; Mishra, P.; Selvaraj, C.; Singh, S.K.; Chaube, R.; Garg, N.; Tripathi, Y.B. Targeting COVID-19 (SARS-CoV-2) main protease through active phytochemicals of ayurvedic medicinal plants – Withania somnifera (Ashwagandha), Tinospora cordifolia (Giloy) and Ocimum sanctum (Tulsi) – a molecular docking study. J. Biomol. Struct. Dyn., 2022, 40(1), 190-203.
[http://dx.doi.org/10.1080/07391102.2020.1810778] [PMID: 32851919]
[117]
Sacco, M.D.; Ma, C.; Lagarias, P.; Gao, A.; Townsend, J.A.; Meng, X.; Dube, P.; Zhang, X.; Hu, Y.; Kitamura, N.; Hurst, B.; Tarbet, B.; Marty, M.T.; Kolocouris, A.; Xiang, Y.; Chen, Y.; Wang, J. Structure and inhibition of the SARS-CoV-2 main protease reveal strategy for developing dual inhibitors against M pro and cathepsin L. Sci. Adv., 2020, 6(50), eabe0751.
[http://dx.doi.org/10.1126/sciadv.abe0751] [PMID: 33158912]
[118]
Wang, Y.; Lamim Ribeiro, J.M.; Tiwary, P. Machine learning approaches for analyzing and enhancing molecular dynamics simulations. Curr. Opin. Struct. Biol., 2020, 61, 139-145.
[http://dx.doi.org/10.1016/j.sbi.2019.12.016] [PMID: 31972477]
[119]
Sosso, G.C.; Chen, J.; Cox, S.J.; Fitzner, M.; Pedevilla, P.; Zen, A.; Michaelides, A. Crystal nucleation in liquids: Open questions and future challenges in molecular dynamics simulations. Chem. Rev., 2016, 116(12), 7078-7116.
[http://dx.doi.org/10.1021/acs.chemrev.5b00744] [PMID: 27228560]
[120]
Guterres, H.; Im, W. Improving protein-ligand docking results with high-throughput molecular dynamics simulations. J. Chem. Inf. Model., 2020, 60(4), 2189-2198.
[http://dx.doi.org/10.1021/acs.jcim.0c00057] [PMID: 32227880]
[121]
Lau, D.; Jian, W.; Yu, Z.; Hui, D. Nano-engineering of construction materials using molecular dynamics simulations: Prospects and challenges. Compos., Part B Eng., 2018, 143, 282-291.
[http://dx.doi.org/10.1016/j.compositesb.2018.01.014]
[122]
Kumari, I.; Sandhu, P.; Ahmed, M.; Akhter, Y. Molecular dynamics simulations, challenges and opportunities: A biologist’s prospective. Curr. Protein Pept. Sci., 2017, 18(11), 1163-1179.
[PMID: 28637405]
[123]
Shukla, R.; Tripathi, T. Dynamics simulation in drug discovery: Opportunities and challenges. Innovations and Implementations of Computer Aided Drug Discovery Strategies in Rational Drug Design; , 2021, pp. 295-316.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy