Exploring Scoring Function Space: Developing Computational Models for Drug Discovery


Cite item

Full Text

Abstract

Background:The idea of scoring function space established a systems-level approach to address the development of models to predict the affinity of drug molecules by those interested in drug discovery.

Objective:Our goal here is to review the concept of scoring function space and how to explore it to develop machine learning models to address protein-ligand binding affinity.

Methods:We searched the articles available in PubMed related to the scoring function space. We also utilized crystallographic structures found in the protein data bank (PDB) to represent the protein space.

Results:The application of systems-level approaches to address receptor-drug interactions allows us to have a holistic view of the process of drug discovery. The scoring function space adds flexibility to the process since it makes it possible to see drug discovery as a relationship involving mathematical spaces.

Conclusion:The application of the concept of scoring function space has provided us with an integrated view of drug discovery methods. This concept is useful during drug discovery, where we see the process as a computational search of the scoring function space to find an adequate model to predict receptor-drug binding affinity.

About the authors

Gabriela Bitencourt-Ferreira

, Pontifical Catholic University of Rio Grande do Sul - PUCRS,

Email: info@benthamscience.net

Marcos Villarreal

CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas,, Universidad Nacional de Córdoba, Ciudad Universitaria

Email: info@benthamscience.net

Rodrigo Quiroga

CONICET-Departamento de Matemática y Física, Instituto de Investigaciones en Fisicoquímica de Córdoba (INFIQC), Facultad de Ciencias Químicas,, Universidad Nacional de Córdoba, Ciudad Universitaria

Email: info@benthamscience.net

Nadezhda Biziukova

, Institute of Biomedical Chemistry

Email: info@benthamscience.net

Vladimir Poroikov

, Institute of Biomedical Chemistry

Email: info@benthamscience.net

Olga Tarasova

, Institute of Biomedical Chemistry

Author for correspondence.
Email: info@benthamscience.net

Walter de Azevedo Junior

, Pontifical Catholic University of Rio Grande do Sul - PUCRS,

Author for correspondence.
Email: info@benthamscience.net

References

  1. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Exploring the scoring function space. Methods Mol. Biol., 2019, 2053, 275-281. doi: 10.1007/978-1-4939-9752-7_17 PMID: 31452111
  2. Heck, G.S.; Pintro, V.O.; Pereira, R.R.; de Ávila, M.B.; Levin, N.M.B.; de Azevedo, W.F. Supervised machine learning methods applied to predict ligand- binding affinity. Curr. Med. Chem., 2017, 24(23), 2459-2470. PMID: 28641555
  3. Ross, G.A.; Morris, G.M.; Biggin, P.C. One size does not fit all: The limits of structure-based models in drug discovery. J. Chem. Theory Comput., 2013, 9(9), 4266-4274. doi: 10.1021/ct4004228 PMID: 24124403
  4. Aghamiri, S.S.; Amin, R.; Helikar, T. Recent applications of quantitative systems pharmacology and machine learning models across diseases. J. Pharmacokinet. Pharmacodyn., 2022, 49(1), 19-37. doi: 10.1007/s10928-021-09790-9 PMID: 34671863
  5. Abbasi, K.; Razzaghi, P.; Poso, A.; Ghanbari-Ara, S.; Masoudi-Nejad, A. Deep learning in drug target interaction prediction: Current and future perspectives. Curr. Med. Chem., 2021, 28(11), 2100-2113. doi: 10.2174/1875533XMTA5qNzU62 PMID: 32895036
  6. Gkeka, P.; Stoltz, G.; Barati Farimani, A.; Belkacemi, Z.; Ceriotti, M.; Chodera, J.D.; Dinner, A.R.; Ferguson, A.L.; Maillet, J.B.; Minoux, H.; Peter, C.; Pietrucci, F.; Silveira, A.; Tkatchenko, A.; Trstanova, Z.; Wiewiora, R.; Lelièvre, T. Machine learning force fields and coarse-grained variables in molecular dynamics: Application to materials and biological systems. J. Chem. Theory Comput., 2020, 16(8), 4757-4775. doi: 10.1021/acs.jctc.0c00355 PMID: 32559068
  7. Bitencourt-Ferreira, G.; Duarte da Silva, A.; Filgueira de Azevedo, W., J.r. Application of machine learning techniques to predict binding affinity for drug targets: A study of cyclin-dependent kinase 2. Curr. Med. Chem., 2021, 28(2), 253-265. doi: 10.2174/1875533XMTAy4MDQm4 PMID: 31729287
  8. Xie, L.; Draizen, E.J.; Bourne, P.E. Harnessing big data for systems pharmacology. Annu. Rev. Pharmacol. Toxicol., 2017, 57(1), 245-262. doi: 10.1146/annurev-pharmtox-010716-104659 PMID: 27814027
  9. Kandoi, G.; Acencio, M.L.; Lemke, N. Prediction of druggable proteins using machine learning and systems biology: A mini-review. Front. Physiol., 2015, 6, 366. doi: 10.3389/fphys.2015.00366 PMID: 26696900
  10. Abedi, M.; Marateb, H.R.; Mohebian, M.R.; Aghaee-Bakhtiari, S.H.; Nassiri, S.M.; Gheisari, Y. Systems biology and machine learning approaches identify drug targets in diabetic nephropathy. Sci. Rep., 2021, 11(1), 23452. doi: 10.1038/s41598-021-02282-3 PMID: 34873190
  11. Huang, Y.W.; Hsu, Y.C.; Chuang, Y.H.; Chen, Y.T.; Lin, X.Y.; Fan, Y.W.; Pathak, N.; Yang, J.M. Discovery of moiety preference by Shapley value in protein kinase family using random forest models. BMC Bioinformatics, 2022, 23(S4), 130. doi: 10.1186/s12859-022-04663-5 PMID: 35428180
  12. Goldman, S.; Das, R.; Yang, K.K.; Coley, C.W. Machine learning modeling of family wide enzyme-substrate specificity screens. PLOS Comput. Biol., 2022, 18(2), e1009853. doi: 10.1371/journal.pcbi.1009853 PMID: 35143485
  13. Bohacek, R.S.; McMartin, C.; Guida, W.C. The art and practice of structure-based drug design: A molecular modeling perspective. Med. Res. Rev., 1996, 16(1), 3-50. doi: 10.1002/(SICI)1098-1128(199601)16:13.0.CO;2-6 PMID: 8788213
  14. Dobson, C.M. Chemical space and biology. Nature, 2004, 432(7019), 824-828. doi: 10.1038/nature03192 PMID: 15602547
  15. Kirkpatrick, P.; Ellis, C. Chemical space. Nature, 2004, 432(7019), 823. doi: 10.1038/432823a
  16. Lipinski, C.; Hopkins, A. Navigating chemical space for biology and medicine. Nature, 2004, 432(7019), 855-861. doi: 10.1038/nature03193 PMID: 15602551
  17. Shoichet, B.K. Virtual screening of chemical libraries. Nature, 2004, 432(7019), 862-865. doi: 10.1038/nature03197 PMID: 15602552
  18. Stockwell, B.R. Exploring biology with small organic molecules. Nature, 2004, 432(7019), 846-854. doi: 10.1038/nature03196 PMID: 15602550
  19. Maynard Smith, J. Natural selection and the concept of a protein space. Nature, 1970, 225(5232), 563-564. doi: 10.1038/225563a0 PMID: 5411867
  20. Hou, J.; Jun, S.R.; Zhang, C.; Kim, S.H. Global mapping of the protein structure space and application in structure-based inference of protein function. Proc. Natl. Acad. Sci., 2005, 102(10), 3651-3656. doi: 10.1073/pnas.0409772102 PMID: 15705717
  21. Bepler, T.; Berger, B. Learning the protein language: Evolution, structure, and function. Cell Syst., 2021, 12(6), 654-669.e3. doi: 10.1016/j.cels.2021.05.017 PMID: 34139171
  22. Vila, J.A. About the protein space vastness. Protein J., 2020, 39(5), 472-475. doi: 10.1007/s10930-020-09939-4 PMID: 33130957
  23. Hecht, N.; Monteil, C.L.; Perrière, G.; Vishkautzan, M.; Gur, E. Exploring protein space: From hydrolase to ligase by substitution. Mol. Biol. Evol., 2021, 38(3), 761-776. doi: 10.1093/molbev/msaa215 PMID: 32870983
  24. Ogbunugafor, C.B. A Reflection on 50 Years of John Maynard Smith’s "Protein Space". Genetics, 2020, 214(4), 749-754. doi: 10.1534/genetics.119.302764 PMID: 32291354
  25. Ogbunugafor, C.B.; Hartl, D.L. A New Take on John Maynard Smith’s concept of protein space for understanding molecular evolution. PLOS Comput. Biol., 2016, 12(10), e1005046. doi: 10.1371/journal.pcbi.1005046 PMID: 27736867
  26. Gorse, A.D. Diversity in medicinal chemistry space. Curr. Top. Med. Chem., 2006, 6(1), 3-18. doi: 10.2174/156802606775193310 PMID: 16454754
  27. Langdon, S.R.; Brown, N.; Blagg, J. Scaffold diversity of exemplified medicinal chemistry space. J. Chem. Inf. Model., 2011, 51(9), 2174-2185. doi: 10.1021/ci2001428 PMID: 21877753
  28. Westerhoff, H.V.; Palsson, B.O. The evolution of molecular biology into systems biology. Nat. Biotechnol., 2004, 22(10), 1249-1252. doi: 10.1038/nbt1020 PMID: 15470464
  29. Limbu, S.; Dakshanamurthy, S. A new hybrid neural network deep learning method for protein–ligand binding affinity prediction and de novo drug design. Int. J. Mol. Sci., 2022, 23(22), 13912. doi: 10.3390/ijms232213912 PMID: 36430386
  30. Hahn, D.F.; Bayly, C.I.; Boby, M.L.; Bruce Macdonald, H.E.; Chodera, J.D.; Gapsys, V.; Mey, A.S.J.S.; Mobley, D.L.; Benito, L.P.; Schindler, C.E.M.; Tresadern, G.; Warren, G.L. Best practices for constructing, preparing, and evaluating protein-ligand binding affinity benchmarks. Living J. Comput. Mol. Sci., 2022, 4(1), 1497. doi: 10.33011/livecoms.4.1.1497 PMID: 36382113
  31. Scott, O.B.; Gu, J.; Chan, A.W.E. Classification of protein-binding sites using a spherical convolutional neural network. J. Chem. Inf. Model., 2022, 62(22), 5383-5396. doi: 10.1021/acs.jcim.2c00832 PMID: 36341715
  32. Sauer, S.; Matter, H.; Hessler, G.; Grebner, C. Optimizing interactions to protein binding sites by integrating docking-scoring strategies into generative AI methods. Front Chem., 2022, 10, 1012507. doi: 10.3389/fchem.2022.1012507 PMID: 36339033
  33. Bieniek, M.K.; Cree, B.; Pirie, R.; Horton, J.T.; Tatum, N.J.; Cole, D.J. An open-source molecular builder and free energy preparation workflow. Commun. Chem., 2022, 5(1), 136. doi: 10.1038/s42004-022-00754-9 PMID: 36320862
  34. Mudedla, S.K.; Braka, A.; Wu, S. Quantum-based machine learning and AI models to generate force field parameters for drug-like small molecules. Front. Mol. Biosci., 2022, 9, 1002535. doi: 10.3389/fmolb.2022.1002535 PMID: 36304919
  35. Ballester, P.J.; Mitchell, J.B.O. A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics, 2010, 26(9), 1169-1175. doi: 10.1093/bioinformatics/btq112 PMID: 20236947
  36. Ballester, P.J.; Schreyer, A.; Blundell, T.L. Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? J. Chem. Inf. Model., 2014, 54(3), 944-955. doi: 10.1021/ci500091r PMID: 24528282
  37. Li, H.; Leung, K-S.; Wong, M-H.; Ballester, P.J. The impact of docking pose generation error on the prediction of binding affinity. In: Computational intelligence methods for bioinformatics and biostatistics, 11th International Meeting, CIBB, Cambridge, UK, June 26-28, 2014, Springer: Cambridge, UK2015, pp. 231-241. doi: 10.1007/978-3-319-24462-4_20
  38. Li, H.; Leung, K.S.; Ballester, P.J.; Wong, M.H. istar: A web platform for large-scale protein-ligand docking. PLoS One, 2014, 9(1), e85678. doi: 10.1371/journal.pone.0085678 PMID: 24475049
  39. Murugan, N.A.; Muvva, C.; Jeyarajpandian, C.; Jeyakanthan, J.; Subramanian, V. Performance of force-field- and machine learning-based scoring functions in ranking MAO-B protein–inhibitor complexes in relevance to developing Parkinson’s therapeutics. Int. J. Mol. Sci., 2020, 21(20), 7648. doi: 10.3390/ijms21207648 PMID: 33081086
  40. Mohanan, A.; Melge, A.R.; Mohan, C.G. Predicting the molecular mechanism of EGFR domain II dimer binding interface by machine learning to identify potent small molecule inhibitor for treatment of cancer. J. Pharm. Sci., 2021, 110(2), 727-737. doi: 10.1016/j.xphs.2020.10.015 PMID: 33058896
  41. Decherchi, S.; Cavalli, A. Thermodynamics and kinetics of drug-target binding by molecular simulation. Chem. Rev., 2020, 120(23), 12788-12833. doi: 10.1021/acs.chemrev.0c00534 PMID: 33006893
  42. Barra, C.; Ackaert, C.; Reynisson, B.; Schockaert, J.; Jessen, L.E.; Watson, M.; Jang, A.; Comtois-Marotte, S.; Goulet, J.P.; Pattijn, S.; Paramithiotis, E.; Nielsen, M. Immunopeptidomic data integration to artificial neural networks enhances protein-drug immunogenicity prediction. Front. Immunol., 2020, 11, 1304. doi: 10.3389/fimmu.2020.01304 PMID: 32655572
  43. Stepniewska-Dziubinska, M.M.; Zielenkiewicz, P.; Siedlecki, P. Improving detection of protein-ligand binding sites with 3D segmentation. Sci. Rep., 2020, 10(1), 5035. doi: 10.1038/s41598-020-61860-z PMID: 32193447
  44. D’Souza, S.; Prema, K.V.; Balaji, S. Machine learning models for drug–target interactions: Current knowledge and future directions. Drug Discov. Today, 2020, 25(4), 748-756. doi: 10.1016/j.drudis.2020.03.003 PMID: 32171918
  45. Boyles, F.; Deane, C.M.; Morris, G.M. Learning from the ligand: using ligand-based features to improve binding affinity prediction. Bioinformatics, 2020, 36(3), 758-764. PMID: 31598630
  46. Aranha, M.P.; Spooner, C.; Demerdash, O.; Czejdo, B.; Smith, J.C.; Mitchell, J.C. Prediction of peptide binding to MHC using machine learning with sequence and structure-based feature sets. Biochim. Biophys. Acta, 2020, 1864(4), 129535. doi: 10.1016/j.bbagen.2020.129535 PMID: 31954798
  47. Zhao, L.; Wang, J.; Pang, L.; Liu, Y.; Zhang, J. GANsDTA: Predicting drug-target binding affinity using GANs. Front. Genet., 2020, 10, 1243. doi: 10.3389/fgene.2019.01243 PMID: 31993067
  48. Miyazaki, Y.; Ono, N.; Huang, M.; Altaf-Ul-Amin, M.; Kanaya, S. Comprehensive exploration of target-specific ligands using a graph convolution neural network. Mol. Inform., 2020, 39(1-2), 1900095. doi: 10.1002/minf.201900095 PMID: 31815371
  49. Zheng, L.; Fan, J.; Mu, Y. OnionNet: A multiple-layer intermolecular-contact-based convolutional neural network for protein–ligand binding affinity prediction. ACS Omega, 2019, 4(14), 15956-15965. doi: 10.1021/acsomega.9b01997 PMID: 31592466
  50. Smith, C.C.; Chai, S.; Washington, A.R.; Lee, S.J.; Landoni, E.; Field, K.; Garness, J.; Bixby, L.M.; Selitsky, S.R.; Parker, J.S.; Savoldo, B.; Serody, J.S.; Vincent, B.G. Machine-learning prediction of tumor antigen immunogenicity in the selection of therapeutic epitopes. Cancer Immunol. Res., 2019, 7(10), 1591-1604. doi: 10.1158/2326-6066.CIR-19-0155 PMID: 31515258
  51. Vincenzi, M.; Mercurio, F.A.; Leone, M. Protein interaction domains and post-translational modifications: Structural features and drug discovery applications. Curr. Med. Chem., 2020, 27(37), 6306-6355. doi: 10.2174/0929867326666190620101637 PMID: 31250750
  52. Vincenzi, M.; Mercurio, F.A.; Leone, M. Protein interaction domains: Structural features and drug discovery applications (Part 2). Curr. Med. Chem., 2021, 28(5), 854-892. doi: 10.2174/0929867327666200114114142 PMID: 31942846
  53. Guzenko, D.; Burley, S.K.; Duarte, J.M. Real time structural search of the protein data bank. PLOS Comput. Biol., 2020, 16(7), e1007970. doi: 10.1371/journal.pcbi.1007970 PMID: 32639954
  54. Bittrich, S.; Burley, S.K.; Rose, A.S. Real-time structural motif searching in proteins using an inverted index strategy. PLOS Comput. Biol., 2020, 16(12), e1008502. doi: 10.1371/journal.pcbi.1008502 PMID: 33284792
  55. Sehnal, D.; Bittrich, S.; Velankar, S.; Koča, J.; Svobodová, R.; Burley, S.K.; Rose, A.S. BinaryCIF and CIFTools—Lightweight, efficient and extensible macromolecular data management. PLOS Comput. Biol., 2020, 16(10), e1008247. doi: 10.1371/journal.pcbi.1008247 PMID: 33075050
  56. Burley, S.K.; Bhikadiya, C.; Bi, C.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; Di Costanzo, L.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ganesan, S.; Goodsell, D.S.; Ghosh, S.; Green, R.K.; Guranović, V.; Guzenko, D.; Hudson, B.P.; Lawson, C.L.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Persikova, I.; Randle, C.; Rose, A.; Rose, Y.; Sali, A.; Segura, J.; Sekharan, M.; Shao, C.; Tao, Y.P.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Zardecki, C.; Zhuravleva, M. RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res., 2021, 49(D1), D437-D451. doi: 10.1093/nar/gkaa1038 PMID: 33211854
  57. Berman, H.M.; Vallat, B.; Lawson, C.L. The data universe of structural biology. IUCrJ, 2020, 7(4), 630-638. doi: 10.1107/S205225252000562X PMID: 32695409
  58. Sehnal, D.; Svobodová, R.; Berka, K.; Rose, A.S.; Burley, S.K.; Velankar, S.; Koča, J. High-performance macromolecular data delivery and visualization for the web. Acta Crystallogr. D Struct. Biol., 2020, 76(12), 1167-1173. doi: 10.1107/S2059798320014515 PMID: 33263322
  59. 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. doi: 10.1016/j.jmb.2020.11.003 PMID: 33186584
  60. Canduri, F.; de Azevedo, W., J.r. Protein crystallography in drug discovery. Curr. Drug Targets, 2008, 9(12), 1048-1053. doi: 10.2174/138945008786949423 PMID: 19128214
  61. Coates, L.; Myles, D.A. Prospects for atomic resolution and neutron crystallography in drug design. Curr. Drug Targets, 2004, 5(2), 173-178. doi: 10.2174/1389450043490613 PMID: 15011950
  62. Van Drie, J.H.; Tong, L. Cryo-EM as a powerful tool for drug discovery. Bioorg. Med. Chem. Lett., 2020, 30(22), 127524. doi: 10.1016/j.bmcl.2020.127524 PMID: 32890683
  63. Shimada, I.; Ueda, T.; Kofuku, Y.; Eddy, M.T.; Wüthrich, K. GPCR drug discovery: Integrating solution NMR data with crystal and cryo-EM structures. Nat. Rev. Drug Discov., 2019, 18(1), 59-82. doi: 10.1038/nrd.2018.180 PMID: 30410121
  64. Fadel, V.; Bettendorff, P.; Herrmann, T.; de Azevedo, W.F., J.r. ; Oliveira, E.B.; Yamane, T.; Wüthrich, K. Automated NMR structure determination and disulfide bond identification of the myotoxin crotamine from Crotalus durissus terrificus. Toxicon, 2005, 46(7), 759-767. doi: 10.1016/j.toxicon.2005.07.018 PMID: 16185738
  65. Behzadi, P.; Gajdács, M. Worldwide Protein Data Bank (wwPDB): A virtual treasure for research in biotechnology. Eur. J. Microbiol. Immunol., 2022, 11(4), 77-86. doi: 10.1556/1886.2021.00020 PMID: 34908533
  66. Perez, M.A.S.; Cuendet, M.A.; Röhrig, U.F.; Michielin, O.; Zoete, V. Structural prediction of Peptide–MHC binding modes. Methods Mol. Biol., 2022, 2405, 245-282. doi: 10.1007/978-1-0716-1855-4_13 PMID: 35298818
  67. Dey, S.; Prilusky, J.; Levy, E.D. QSalignWeb: A server to predict and analyze protein quaternary structure. Front. Mol. Biosci., 2022, 8, 787510. doi: 10.3389/fmolb.2021.787510 PMID: 35071324
  68. Christoffer, C.; Bharadwaj, V.; Luu, R.; Kihara, D. LZerD protein-protein docking webserver enhanced with de novo structure prediction. Front. Mol. Biosci., 2021, 8, 724947. doi: 10.3389/fmolb.2021.724947 PMID: 34466411
  69. Westbrook, J.D.; Soskind, R.; Hudson, B.P.; Burley, S.K. Impact of the Protein Data Bank on antineoplastic approvals. Drug Discov. Today, 2020, 25(5), 837-850. doi: 10.1016/j.drudis.2020.02.002 PMID: 32068073
  70. Ionescu, M.I. An overview of the crystallized structures of the SARS-CoV-2. Protein J., 2020, 39(6), 600-618. doi: 10.1007/s10930-020-09933-w PMID: 33098476
  71. Goodsell, D.S.; Burley, S.K. RCSB Protein Data Bank tools for 3D structure-guided cancer research: Human papillomavirus (HPV) case study. Oncogene, 2020, 39(43), 6623-6632. doi: 10.1038/s41388-020-01461-2 PMID: 32939013
  72. Di Costanzo, L.; Geremia, S. Atomic details of carbon-based nanomolecules interacting with proteins. Molecules, 2020, 25(15), 3555. doi: 10.3390/molecules25153555 PMID: 32759758
  73. Wang, J.; Yazdani, S.; Han, A.; Schapira, M. Structure-based view of the druggable genome. Drug Discov. Today, 2020, 25(3), 561-567. doi: 10.1016/j.drudis.2020.02.006 PMID: 32084498
  74. Copoiu, L.; Malhotra, S. The current structural glycome landscape and emerging technologies. Curr. Opin. Struct. Biol., 2020, 62, 132-139. doi: 10.1016/j.sbi.2019.12.020 PMID: 32006784
  75. Haas, D.J. The early history of cryo-cooling for macromolecular crystallography. IUCrJ, 2020, 7(2), 148-157. doi: 10.1107/S2052252519016993 PMID: 32148843
  76. Bascos, N.A.D.; Landry, S.J. A history of molecular chaperone structures in the Protein Data Bank. Int. J. Mol. Sci., 2019, 20(24), 6195. doi: 10.3390/ijms20246195 PMID: 31817979
  77. Weber, P.; Pissis, C.; Navaza, R.; Mechaly, A.E.; Saul, F.; Alzari, P.M.; Haouz, A. High-throughput crystallization pipeline at the crystallography core facility of the institut pasteur. Molecules, 2019, 24(24), 4451. doi: 10.3390/molecules24244451 PMID: 31817305
  78. Thomsen, R.; Christensen, M.H. MolDock: A new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321. doi: 10.1021/jm051197e PMID: 16722650
  79. Heberlé, G.; de Azevedo, W.F., J.r. Bio-inspired algorithms applied to molecular docking simulations. Curr. Med. Chem., 2011, 18(9), 1339-1352. doi: 10.2174/092986711795029573 PMID: 21366530
  80. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Molegro virtual docker for docking. Methods Mol. Biol., 2019, 2053, 149-167. doi: 10.1007/978-1-4939-9752-7_10 PMID: 31452104
  81. Sterling, T.; Irwin, J.J. ZINC 15 – ligand discovery for everyone. J. Chem. Inf. Model., 2015, 55(11), 2324-2337. doi: 10.1021/acs.jcim.5b00559 PMID: 26479676
  82. Irwin, J.J.; Sterling, T.; Mysinger, M.M.; Bolstad, E.S.; Coleman, R.G. ZINC: A free tool to discover chemistry for biology. J. Chem. Inf. Model., 2012, 52(7), 1757-1768. doi: 10.1021/ci3001277 PMID: 22587354
  83. Irwin, J.J.; Shoichet, B.K. ZINC - a free database of commercially available compounds for virtual screening. J. Chem. Inf. Model., 2005, 45(1), 177-182. doi: 10.1021/ci049714+ PMID: 15667143
  84. Anwar, F.; Naqvi, S.; Al-Abbasi, F.A.; Neelofar, N.; Kumar, V.; Sahoo, A.; Kamal, M.A. Targeting COVID-19 in Parkinson’s patients: Drugs repurposed. Curr. Med. Chem., 2021, 28(12), 2392-2408. PMID: 32881656
  85. Wang, N.; Qiu, P.; Cui, W.; Yan, X.; Zhang, B.; He, S. Recent advances in multi-target anti-alzheimer disease compounds (2013 Up to the Present). Curr. Med. Chem., 2019, 26(30), 5684-5710. doi: 10.2174/0929867326666181203124102 PMID: 30501591
  86. Konreddy, A.K.; Rani, G.U.; Lee, K.; Choi, Y. Recent drug-repurposing-driven advances in the discovery of novel antibiotics. Curr. Med. Chem., 2019, 26(28), 5363-5388. doi: 10.2174/0929867325666180706101404 PMID: 29984648
  87. Mernea, M.; Martin, E.C.; Petrescu, A.J.; Avram, S. Deep learning in the quest for compound nomination for fighting COVID-19. Curr. Med. Chem., 2021, 28(28), 5699-5732. doi: 10.2174/0929867328666210113170222 PMID: 33441063
  88. Grassi, G.; Grassi, M. Drug repurposing in human cancers. Curr. Med. Chem., 2020, 27(42), 7213. doi: 10.2174/092986732742201105104417 PMID: 33342397
  89. Musella, S.; Verna, G.; Fasano, A.; Di Micco, S. New perspectives on machine learning in drug discovery. Curr. Med. Chem., 2021, 28(32), 6704-6728. doi: 10.2174/0929867327666201111144048 PMID: 33176630
  90. Schcolnik-Cabrera, A.; Juárez-López, D.; Duenas-Gonzalez, A. Perspectives on drug repurposing. Curr. Med. Chem., 2021, 28(11), 2085-2099. doi: 10.2174/0929867327666200831141337 PMID: 32867630
  91. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Molecular docking simulations with arguslab. Methods Mol. Biol., 2019, 2053, 203-220. doi: 10.1007/978-1-4939-9752-7_13 PMID: 31452107
  92. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Docking with SwissDock. Methods Mol. Biol., 2019, 2053, 189-202. doi: 10.1007/978-1-4939-9752-7_12
  93. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. How docking programs work. Methods Mol. Biol., 2019, 2053, 35-50. doi: 10.1007/978-1-4939-9752-7_3 PMID: 31452097
  94. Bitencourt-Ferreira, G.; Pintro, V.O.; de Azevedo, W.F., J.r. Docking with AutoDock4. Methods Mol. Biol., 2019, 2053, 125-148. doi: 10.1007/978-1-4939-9752-7_9 PMID: 31452103
  95. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Docking with GemDock. Methods Mol. Biol., 2019, 2053, 169-188. doi: 10.1007/978-1-4939-9752-7_11 PMID: 31452105
  96. Pintro, V.O.; de Azevedo, W.F., J.r. Optimized virtual screening workflow: Towards target-based polynomial scoring functions for HIV-1 protease. Comb. Chem. High Throughput Screen., 2018, 20(9), 820-827. doi: 10.2174/1386207320666171121110019 PMID: 29165067
  97. Santana Azevedo, L.; Pretto Moraes, F.; Morrone Xavier, M.; Ozorio Pantoja, E.; Villavicencio, B.; Aline Finck, J.; Menegaz Proenca, A.; Beiestorf Rocha, K.; Filgueira de Azevedo, W. Recent progress of molecular docking simulations applied to development of drugs. Curr. Bioinform., 2012, 7(4), 352-365. doi: 10.2174/157489312803901063
  98. De Azevedo, W.F., J.r. Structure-based virtual screening. Curr. Drug Targets, 2010, 11(3), 261-263. PMID: 20214598
  99. De Azevedo, W., J.r. MolDock applied to structure-based virtual screening. Curr. Drug Targets, 2010, 11(3), 327-334. doi: 10.2174/138945010790711941 PMID: 20210757
  100. Breda, A.; Basso, L.; Santos, D.; de Azevedo, W., J.r. Virtual screening of drugs: Score functions, docking, and drug design. Curr. Computeraided Drug Des., 2008, 4(4), 265-272. doi: 10.2174/157340908786786047
  101. Jimenez, M.; Campillo, N.E.; Canelles, M. COVID-19 vaccine race: Analysis of age-dependent immune responses against SARS-CoV-2 indicates that more than just one strategy may be needed. Curr. Med. Chem., 2021, 28(20), 3964-3979. doi: 10.2174/1875533XMTEwBOTYhx PMID: 33109026
  102. 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. doi: 10.2174/1875533XMTA5rMDYp5 PMID: 32787752
  103. Tarasova, O.; Ivanov, S.; Filimonov, D.A.; Poroikov, V. Data and text mining help identify key proteins involved in the molecular mechanisms shared by SARS-CoV-2 and HIV-1. Molecules, 2020, 25(12), 2944. doi: 10.3390/molecules25122944 PMID: 32604797
  104. Luan, B.; Huynh, T.; Cheng, X.; Lan, G.; Wang, H.R. Targeting proteases for treating COVID-19. J. Proteome Res., 2020, 19(11), 4316-4326. doi: 10.1021/acs.jproteome.0c00430 PMID: 33090793
  105. Li, J.; Zhou, X.; Zhang, Y.; Zhong, F.; Lin, C.; McCormick, P.J.; Jiang, F.; Luo, J.; Zhou, H.; Wang, Q.; Fu, Y.; Duan, J.; Zhang, J. Crystal structure of SARS-CoV-2 main protease in complex with the natural product inhibitor shikonin illuminates a unique binding mode. Sci. Bull., 2021, 66(7), 661-663. doi: 10.1016/j.scib.2020.10.018 PMID: 33163253
  106. Zhang, L.; Lin, D.; Sun, X.; Curth, U.; Drosten, C.; Sauerhering, L.; Becker, S.; Rox, K.; Hilgenfeld, R. Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science, 2020, 368(6489), 409-412. doi: 10.1126/science.abb3405 PMID: 32198291
  107. Mengist, H.M.; Fan, X.; Jin, T. Designing of improved drugs for COVID-19: Crystal structure of SARS-CoV-2 main protease Mpro. Signal Transduct. Target. Ther., 2020, 5(1), 67. doi: 10.1038/s41392-020-0178-y PMID: 32388537
  108. Hussein, R.K.; Elkhair, H.M. Molecular docking identification for the efficacy of some zinc complexes with chloroquine and hydroxychloroquine against main protease of COVID-19. J. Mol. Struct., 2021, 1231, 129979. doi: 10.1016/j.molstruc.2021.129979 PMID: 33518801
  109. Ronsisvalle, S.; Panarello, F.; Di Mauro, R.; Bernardini, R.; Volti, G.L.; Cantarella, G. Anti-malarial drugs are not created equal for SARS-CoV-2 treatment: A computational analysis evidence. Curr. Pharm. Des., 2021, 27(10), 1323-1329. doi: 10.2174/1381612826666201210092736 PMID: 33302855
  110. Li, Z.; Li, X.; Huang, Y.Y.; Wu, Y.; Liu, R.; Zhou, L.; Lin, Y.; Wu, D.; Zhang, L.; Liu, H.; Xu, X.; Yu, K.; Zhang, Y.; Cui, J.; Zhan, C.G.; Wang, X.; Luo, H.B. Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs. Proc. Natl. Acad. Sci. USA, 2020, 117(44), 27381-27387. doi: 10.1073/pnas.2010470117 PMID: 33051297
  111. Achutha, A.S.; Pushpa, V.L.; Suchitra, S. Theoretical insights into the Anti-SARS-CoV-2 activity of chloroquine and its analogs and in silico screening of main protease inhibitors. J. Proteome Res., 2020, 19(11), 4706-4717. doi: 10.1021/acs.jproteome.0c00683 PMID: 32960061
  112. Tripathi, P.K.; Upadhyay, S.; Singh, M.; Raghavendhar, S.; Bhardwaj, M.; Sharma, P.; Patel, A.K. Screening and evaluation of approved drugs as inhibitors of main protease of SARS-CoV-2. Int. J. Biol. Macromol., 2020, 164, 2622-2631. doi: 10.1016/j.ijbiomac.2020.08.166 PMID: 32853604
  113. Nandi, S.; Kumar, M.; Saxena, M.; Saxena, A.K. The antiviral and antimalarial drug repurposing in quest of chemotherapeutics to combat COVID-19 utilizing structure-based molecular docking. Comb. Chem. High Throughput Screen., 2021, 24(7), 1055-1068. doi: 10.2174/1386207323999200824115536 PMID: 32838713
  114. Baildya, N.; Ghosh, N.N.; Chattopadhyay, A.P. Inhibitory activity of hydroxychloroquine on COVID-19 main protease: An insight from MD-simulation studies. J. Mol. Struct., 2020, 1219, 128595. doi: 10.1016/j.molstruc.2020.128595 PMID: 32834108
  115. Mukherjee, S.; Dasgupta, S.; Adhikary, T.; Adhikari, U.; Panja, S.S. Structural insight to hydroxychloroquine-3C-like proteinase complexation from SARS-CoV-2: Inhibitor modelling study through molecular docking and MD-simulation study. J. Biomol. Struct. Dyn., 2021, 39(18), 7322-7334. doi: 10.1080/07391102.2020.1804458 PMID: 32772895
  116. Braz, H.L.B.; Silveira, J.A.M.; Marinho, A.D.; de Moraes, M.E.A.; Moraes Filho, M.O.; Monteiro, H.S.A.; Jorge, R.J.B. In silico study of azithromycin, chloroquine and hydroxychloroquine and their potential mechanisms of action against SARS-CoV-2 infection. Int. J. Antimicrob. Agents, 2020, 56(3), 106119. doi: 10.1016/j.ijantimicag.2020.106119 PMID: 32738306
  117. Silva Arouche, T.D.; Reis, A.F.; Martins, A.Y.; S Costa, J.F.; Carvalho Junior, R.N.; J C Neto, A.M. Interactions between remdesivir, ribavirin, favipiravir, galidesivir, hydroxychloroquine and chloroquine with fragment molecular of the COVID-19 main protease with inhibitor N3 complex (PDB ID:6LU7) using molecular docking. J. Nanosci. Nanotechnol., 2020, 20(12), 7311-7323. doi: 10.1166/jnn.2020.18955 PMID: 32711596
  118. Marinho, E.M.; Batista de Andrade Neto, J.; Silva, J.; Rocha da Silva, C.; Cavalcanti, B.C.; Marinho, E.S.; Nobre Júnior, H.V. Virtual screening based on molecular docking of possible inhibitors of COVID-19 main protease. Microb. Pathog., 2020, 148, 104365. doi: 10.1016/j.micpath.2020.104365 PMID: 32619669
  119. Fantini, J.; Chahinian, H.; Yahi, N. Synergistic antiviral effect of hydroxychloroquine and azithromycin in combination against SARS-CoV-2: What molecular dynamics studies of virus-host interactions reveal. Int. J. Antimicrob. Agents, 2020, 56(2), 106020. doi: 10.1016/j.ijantimicag.2020.106020
  120. Enmozhi, S.K.; Raja, K.; Sebastine, I.; Joseph, J. Andrographolide as a potential inhibitor of SARS-CoV-2 main protease: An in silico approach. J. Biomol. Struct. Dyn., 2020, 5, 1-7. doi: 10.1080/07391102.2020.1760136 PMID: 32329419
  121. Hagar, M.; Ahmed, H.A.; Aljohani, G.; Alhaddad, O.A. Investigation of some antiviral N-heterocycles as COVID 19 drug: Molecular docking and DFT calculations. Int. J. Mol. Sci., 2020, 21(11), 3922. doi: 10.3390/ijms21113922 PMID: 32486229
  122. Rehman, M.T.; AlAjmi, M.F.; Hussain, A. Natural compounds as inhibitors of SARS-CoV-2 main protease (3CLpro): A molecular docking and simulation approach to combat COVID-19. Curr. Pharm. Des., 2021, 27(33), 3577-3589. doi: 10.2174/18734286MTEx9NTUg2 PMID: 33200697
  123. Hoffmann, M.; Mösbauer, K.; Hofmann-Winkler, H.; Kaul, A.; Kleine-Weber, H.; Krüger, N.; Gassen, N.C.; Müller, M.A.; Drosten, C.; Pöhlmann, S. Chloroquine does not inhibit infection of human lung cells with SARS-CoV-2. Nature, 2020, 585(7826), 588-590. doi: 10.1038/s41586-020-2575-3 PMID: 32698190
  124. Kupferschmidt, K. Big studies dim hopes for hydroxychloroquine. Science, 2020, 368(6496), 1166-1167. doi: 10.1126/science.368.6496.1166 PMID: 32527806
  125. Kuhn, D.; Weskamp, N.; Hüllermeier, E.; Klebe, G. Functional classification of protein kinase binding sites using Cavbase. ChemMedChem, 2007, 2(10), 1432-1447. doi: 10.1002/cmdc.200700075 PMID: 17694525
  126. Cao, D.S.; Zhou, G.H.; Liu, S.; Zhang, L.X.; Xu, Q.S.; He, M.; Liang, Y.Z. Large-scale prediction of human kinase–inhibitor interactions using protein sequences and molecular topological structures. Anal. Chim. Acta, 2013, 792, 10-18. doi: 10.1016/j.aca.2013.07.003 PMID: 23910962
  127. Rask-Andersen, M.; Masuram, S.; Schiöth, H.B. The druggable genome: Evaluation of drug targets in clinical trials suggests major shifts in molecular class and indication. Annu. Rev. Pharmacol. Toxicol., 2014, 54(1), 9-26. doi: 10.1146/annurev-pharmtox-011613-135943 PMID: 24016212
  128. Carles, F.; Bourg, S.; Meyer, C.; Bonnet, P. PKIDB: A curated, annotated and updated database of protein kinase inhibitors in clinical trials. Molecules, 2018, 23(4), 908. doi: 10.3390/molecules23040908 PMID: 29662024
  129. Li, L.; Koh, C.C.; Reker, D.; Brown, J.B.; Wang, H.; Lee, N.K.; Liow, H.; Dai, H.; Fan, H.M.; Chen, L.; Wei, D.Q. Predicting protein-ligand interactions based on bow-pharmacological space and Bayesian additive regression trees. Sci. Rep., 2019, 9(1), 7703. doi: 10.1038/s41598-019-43125-6 PMID: 31118426
  130. Mathai, N.; Stork, C.; Kirchmair, J. BonMOLière: Small-sized libraries of readily purchasable compounds, optimized to produce genuine hits in biological screens across the protein space. Int. J. Mol. Sci., 2021, 22(15), 7773. doi: 10.3390/ijms22157773 PMID: 34360558
  131. Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589. doi: 10.1038/s41586-021-03819-2 PMID: 34265844
  132. Stern, J.; Hedelius, B.; Fisher, O.; Billings, W.M.; Della Corte, D. Evaluation of deep neural network prospr for accurate protein distance predictions on CASP14 targets. Int. J. Mol. Sci., 2021, 22(23), 12835. doi: 10.3390/ijms222312835 PMID: 34884640
  133. Roche, R.; Bhattacharya, S.; Bhattacharya, D. Hybridized distance- and contact-based hierarchical structure modeling for folding soluble and membrane proteins. PLOS Comput. Biol., 2021, 17(2), e1008753. doi: 10.1371/journal.pcbi.1008753 PMID: 33621244
  134. Cretin, G.; Galochkina, T.; de Brevern, A.G.; Gelly, J.C. PYTHIA: Deep learning approach for local protein conformation prediction. Int. J. Mol. Sci., 2021, 22(16), 8831. doi: 10.3390/ijms22168831 PMID: 34445537
  135. Callaway, E. What’s next for AlphaFold and the AI protein-folding revolution. Nature, 2022, 604(7905), 234-238. doi: 10.1038/d41586-022-00997-5 PMID: 35418629
  136. Sapoval, N.; Aghazadeh, A.; Nute, M.G.; Antunes, D.A.; Balaji, A.; Baraniuk, R.; Barberan, C.J.; Dannenfelser, R.; Dun, C.; Edrisi, M.; Elworth, R.A.L.; Kille, B.; Kyrillidis, A.; Nakhleh, L.; Wolfe, C.R.; Yan, Z.; Yao, V.; Treangen, T.J. Current progress and open challenges for applying deep learning across the biosciences. Nat. Commun., 2022, 13(1), 1728. doi: 10.1038/s41467-022-29268-7 PMID: 35365602
  137. Bayly-Jones, C.; Whisstock, J.C. Mining folded proteomes in the era of accurate structure prediction. PLOS Comput. Biol., 2022, 18(3), e1009930. doi: 10.1371/journal.pcbi.1009930 PMID: 35333855
  138. Ornes, S. Researchers turn to deep learning to decode protein structures. Proc. Natl. Acad. Sci. USA, 2022, 119(10), e2202107119. doi: 10.1073/pnas.2202107119 PMID: 35235461
  139. Orlando, G.; Raimondi, D.; Duran-Romaña, R.; Moreau, Y.; Schymkowitz, J.; Rousseau, F. PyUUL provides an interface between biological structures and deep learning algorithms. Nat. Commun., 2022, 13(1), 961. doi: 10.1038/s41467-022-28327-3 PMID: 35181656
  140. Lee, D.; Xiong, D.; Wierbowski, S.; Li, L.; Liang, S.; Yu, H. Deep learning methods for 3D structural proteome and interactome modeling. Curr. Opin. Struct. Biol., 2022, 73, 102329. doi: 10.1016/j.sbi.2022.102329 PMID: 35139457
  141. Pakhrin, S.C.; Shrestha, B.; Adhikari, B.; Kc, D.B. Deep learning-based advances in protein structure prediction. Int. J. Mol. Sci., 2021, 22(11), 5553. doi: 10.3390/ijms22115553 PMID: 34074028
  142. Baek, M.; DiMaio, F.; Anishchenko, I.; Dauparas, J.; Ovchinnikov, S.; Lee, G.R.; Wang, J.; Cong, Q.; Kinch, L.N.; Schaeffer, R.D.; Millán, C.; Park, H.; Adams, C.; Glassman, C.R.; DeGiovanni, A.; Pereira, J.H.; Rodrigues, A.V.; van Dijk, A.A.; Ebrecht, A.C.; Opperman, D.J.; Sagmeister, T.; Buhlheller, C.; Pavkov-Keller, T.; Rathinaswamy, M.K.; Dalwadi, U.; Yip, C.K.; Burke, J.E.; Garcia, K.C.; Grishin, N.V.; Adams, P.D.; Read, R.J.; Baker, D. Accurate prediction of protein structures and interactions using a three-track neural network. Science, 2021, 373(6557), 871-876. doi: 10.1126/science.abj8754 PMID: 34282049
  143. Anderson, E.; Havener, T.M.; Zorn, K.M.; Foil, D.H.; Lane, T.R.; Capuzzi, S.J.; Morris, D.; Hickey, A.J.; Drewry, D.H.; Ekins, S. Synergistic drug combinations and machine learning for drug repurposing in chordoma. Sci. Rep., 2020, 10(1), 12982. doi: 10.1038/s41598-020-70026-w PMID: 32737414
  144. Noor, A.; Bindal, P.; Ramirez, M.; Vredenburgh, J. Chordoma: A case report and review of literature. Am. J. Case Rep., 2020, 21, e918927. doi: 10.12659/AJCR.918927 PMID: 31969553
  145. Wójcikowski, M.; Siedlecki, P.; Ballester, P.J. Building machine-learning scoring functions for structure-based prediction of intermolecular binding affinity. Methods Mol. Biol., 2019, 2053, 1-12. doi: 10.1007/978-1-4939-9752-7_1 PMID: 31452095
  146. Xavier, M.M.; Heck, G.S.; Avila, M.B.; Levin, N.M.B.; Pintro, V.O.; Carvalho, N.L.; Azevedo, W.F., J.r. SAnDReS a computational tool for statistical analysis of docking results and development of scoring functions. Comb. Chem. High Throughput Screen., 2016, 19(10), 801-812. PMID: 27686428
  147. da Silva, A.D.; Bitencourt-Ferreira, G.; Azevedo, W.F., J.r. Taba: A tool to analyze the binding affinity. J. Comput. Chem., 2020, 41(1), 69-73. doi: 10.1002/jcc.26048 PMID: 31410856
  148. McNutt, A.T.; Francoeur, P.; Aggarwal, R.; Masuda, T.; Meli, R.; Ragoza, M.; Sunseri, J.; Koes, D.R. GNINA 1.0: Molecular docking with deep learning. J. Cheminform., 2021, 13(1), 43. doi: 10.1186/s13321-021-00522-2 PMID: 34108002
  149. Sunseri, J.; Koes, D.R. Virtual Screening with Gnina 1.0. Molecules, 2021, 26(23), 7369. doi: 10.3390/molecules26237369 PMID: 34885952
  150. Koes, D.R.; Baumgartner, M.P.; Camacho, C.J. Lessons learned in empirical scoring with smina from the CSAR 2011 benchmarking exercise. J. Chem. Inf. Model., 2013, 53(8), 1893-1904. doi: 10.1021/ci300604z PMID: 23379370
  151. Baumgartner, M.P.; Evans, D.A. Lessons learned in induced fit docking and metadynamics in the drug design data resource grand challenge 2. J. Comput. Aided Mol. Des., 2018, 32(1), 45-58. doi: 10.1007/s10822-017-0081-y PMID: 29127581
  152. Canduri, F.; Teodoro, L.G.V.L.; Fadel, V.; Lorenzi, C.C.B.; Hial, V.; Gomes, R.A.S.; Neto, J.R.; de Azevedo, W.F., J.r. Structure of human uropepsin at 2.45 Å resolution. Acta Crystallogr. D Biol. Crystallogr., 2001, 57(11), 1560-1570. doi: 10.1107/S0907444901013865 PMID: 11679720
  153. de Azevedo, W.F., J.r. ; Canduri, F.; dos Santos, D.M.; Silva, R.G.; de Oliveira, J.S.; de Carvalho, L.P.S.; Basso, L.A.; Mendes, M.A.; Palma, M.S.; Santos, D.S. Crystal structure of human purine nucleoside phosphorylase at 2.3Å resolution. Biochem. Biophys. Res. Commun., 2003, 308(3), 545-552. doi: 10.1016/S0006-291X(03)01431-1 PMID: 12914785
  154. Pereira, J.H.; de Oliveira, J.S.; Canduri, F.; Dias, M.V.; Palma, M.S.; Basso, L.A.; Santos, D.S.; de Azevedo, W.F., J.r. Structure of shikimate kinase from Mycobacterium tuberculosis reveals the binding of shikimic acid. Acta Crystallogr. D Biol. Crystallogr., 2004, 60(Pt 12), 2310-2319.
  155. Azevedo, W.F.; Leclerc, S.; Meijer, L.; Havlicek, L.; Strnad, M.; Kim, S.H. Inhibition of cyclin-dependent kinases by purine analogues: Crystal structure of human CDK2 complexed with roscovitine. Eur. J. Biochem., 1997, 243(1-2), 518-526. doi: 10.1111/j.1432-1033.1997.0518a.x PMID: 9030780
  156. Dias, M.V.B.; Vasconcelos, I.B.; Prado, A.M.X.; Fadel, V.; Basso, L.A.; de Azevedo, W.F., J.r. ; Santos, D.S. Crystallographic studies on the binding of isonicotinyl-NAD adduct to wild-type and isoniazid resistant 2-trans-enoyl-ACP (CoA) reductase from Mycobacterium tuberculosis. J. Struct. Biol., 2007, 159(3), 369-380. doi: 10.1016/j.jsb.2007.04.009 PMID: 17588773
  157. Bezerra, G.A.; Oliveira, T.M.; Moreno, F.B.M.B.; de Souza, E.P.; da Rocha, B.A.M.; Benevides, R.G.; Delatorre, P.; de Azevedo, W.F., J.r. ; Cavada, B.S. Structural analysis of Canavalia maritima and Canavalia gladiata lectins complexed with different dimannosides: New insights into the understanding of the structure–biological activity relationship in legume lectins. J. Struct. Biol., 2007, 160(2), 168-176. doi: 10.1016/j.jsb.2007.07.012 PMID: 17881248
  158. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461. PMID: 19499576
  159. Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898. doi: 10.1021/acs.jcim.1c00203 PMID: 34278794
  160. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791. doi: 10.1002/jcc.21256 PMID: 19399780
  161. Ain, Q.U.; Aleksandrova, A.; Roessler, F.D.; Ballester, P.J. Machine-learning scoring functions to improve structure-based binding affinity prediction and virtual screening. Wiley Interdiscip. Rev. Comput. Mol. Sci., 2015, 5(6), 405-424. doi: 10.1002/wcms.1225 PMID: 27110292
  162. Quiroga, R.; Villarreal, M.A. Vinardo: A scoring function based on autodock vina improves scoring, docking, and virtual screening. PLoS One, 2016, 11(5), e0155183. doi: 10.1371/journal.pone.0155183 PMID: 27171006
  163. Shulga, D.A.; Ivanov, N.N.; Palyulin, V.A. In silico structure-based approach for group efficiency estimation in fragment-based drug design using evaluation of fragment contributions. Molecules, 2022, 27(6), 1985. doi: 10.3390/molecules27061985 PMID: 35335347
  164. Wang, D.D.; Chan, M.T. Protein-ligand binding affinity prediction based on profiles of intermolecular contacts. Comput. Struct. Biotechnol. J., 2022, 20, 1088-1096. doi: 10.1016/j.csbj.2022.02.004 PMID: 35317230
  165. Singh, N.; Chaput, L.; Villoutreix, B.O. Fast rescoring protocols to improve the performance of structure-based virtual screening performed on protein–protein interfaces. J. Chem. Inf. Model., 2020, 60(8), 3910-3934. doi: 10.1021/acs.jcim.0c00545 PMID: 32786511
  166. Lu, H.; Wei, Z.; Wang, C.; Guo, J.; Zhou, Y.; Wang, Z.; Liu, H. Redesigning Vina@QNLM for ultra-large-scale molecular docking and screening on a sunway supercomputer. Front Chem., 2021, 9, 750325. doi: 10.3389/fchem.2021.750325 PMID: 34778205
  167. Sharma, P.; Vijayan, V.; Pant, P.; Sharma, M.; Vikram, N.; Kaur, P.; Singh, T.P.; Sharma, S. Identification of potential drug candidates to combat COVID-19: A structural study using the main protease (mpro) of SARS-CoV-2. J. Biomol. Struct. Dyn., 2021, 39(17), 6649-6659. doi: 10.1080/07391102.2020.1798286 PMID: 32741313
  168. Gupta, A.; Rani, C.; Pant, P.; Vijayan, V.; Vikram, N.; Kaur, P.; Singh, T.P.; Sharma, S.; Sharma, P. Structure-based virtual screening and biochemical validation to discover a potential inhibitor of the SARS-CoV-2 main protease. ACS Omega, 2020, 5(51), 33151-33161. doi: 10.1021/acsomega.0c04808 PMID: 33398250
  169. Shytaj, I.L.; Fares, M.; Gallucci, L.; Lucic, B.; Tolba, M.M.; Zimmermann, L.; Adler, J.M.; Xing, N.; Bushe, J.; Gruber, A.D.; Ambiel, I.; Taha Ayoub, A.; Cortese, M.; Neufeldt, C.J.; Stolp, B.; Sobhy, M.H.; Fathy, M.; Zhao, M.; Laketa, V.; Diaz, R.S.; Sutton, R.E.; Chlanda, P.; Boulant, S.; Bartenschlager, R.; Stanifer, M.L.; Fackler, O.T.; Trimpert, J.; Savarino, A.; Lusic, M. The FDA-approved drug cobicistat synergizes with remdesivir to inhibit SARS-CoV-2 replication in vitro and decreases viral titers and disease progression in Syrian Hamsters. MBio, 2022, 13(2), e03705-21. doi: 10.1128/mbio.03705-21 PMID: 35229634
  170. Musarrat, F.; Chouljenko, V.; Dahal, A.; Nabi, R.; Chouljenko, T.; Jois, S.D.; Kousoulas, K.G. The anti-HIV drug nelfinavir mesylate (Viracept) is a potent inhibitor of cell fusion caused by the SARSCoV-2 spike (S) glycoprotein warranting further evaluation as an antiviral against COVID-19 infections. J. Med. Virol., 2020, 92(10), 2087-2095. doi: 10.1002/jmv.25985 PMID: 32374457
  171. Jalalvand, A.; Khatouni, S.B.; Najafi, Z.B.; Fatahinia, F.; Ismailzadeh, N.; Farahmand, B. Computational drug repurposing study of antiviral drugs against main protease, RNA polymerase, and spike proteins of SARS-CoV-2 using molecular docking method. J. Basic Clin. Physiol. Pharmacol., 2022, 33(1), 85-95. doi: 10.1515/jbcpp-2020-0369 PMID: 34265888
  172. Ohashi, H.; Watashi, K.; Saso, W.; Shionoya, K.; Iwanami, S.; Hirokawa, T.; Shirai, T.; Kanaya, S.; Ito, Y.; Kim, K.S.; Nomura, T.; Suzuki, T.; Nishioka, K.; Ando, S.; Ejima, K.; Koizumi, Y.; Tanaka, T.; Aoki, S.; Kuramochi, K.; Suzuki, T.; Hashiguchi, T.; Maenaka, K.; Matano, T.; Muramatsu, M.; Saijo, M.; Aihara, K.; Iwami, S.; Takeda, M.; McKeating, J.A.; Wakita, T. Potential anti-COVID-19 agents, cepharanthine and nelfinavir, and their usage for combination treatment. iScience, 2021, 24(4), 102367. doi: 10.1016/j.isci.2021.102367 PMID: 33817567
  173. Tatar, G.; Salmanli, M.; Dogru, Y.; Tuzuner, T. Evaluation of the effects of chlorhexidine and several flavonoids as antiviral purposes on SARS-CoV-2 main protease: Molecular docking, molecular dynamics simulation studies. J. Biomol. Struct. Dyn., 2022, 40(17), 7656-7665. PMID: 33749547
  174. Rivero-Segura, N.A.; Gomez-Verjan, J.C. In silico screening of natural products isolated from Mexican herbal medicines against COVID-19. Biomolecules, 2021, 11(2), 216. doi: 10.3390/biom11020216 PMID: 33557097
  175. Zhu, Y.; Scholle, F.; Kisthardt, S.C.; Xie, D.Y. Flavonols and dihydroflavonols inhibit the main protease activity of SARS-CoV-2 and the replication of human coronavirus 229E. Virology, 2022, 571, 21-33. doi: 10.1016/j.virol.2022.04.005 PMID: 35439707
  176. Bahun, M.; Jukić, M.; Oblak, D.; Kranjc, L.; Bajc, G.; Butala, M.; Bozovičar, K.; Bratkovič, T.; Podlipnik, Č.; Poklar Ulrih, N. Inhibition of the SARS-CoV-2 3CLpro main protease by plant polyphenols. Food Chem., 2022, 373(Pt B), 131594. doi: 10.1016/j.foodchem.2021.131594 PMID: 34838409
  177. Mavian, C.; Coman, R.M.; Zhang, X.; Pomeroy, S.; Ostrov, D.A.; Dunn, B.M.; Sleasman, J.W.; Goodenow, M.M. Molecular docking-based screening for novel inhibitors of the human immunodeficiency virus type 1 protease that effectively reduce the viral replication in human cells. J. AIDS Clin. Res., 2021, 12(5), 841. PMID: 34950525
  178. Wei, Y.; Yang, J.; Kishore Sakharkar, M.; Wang, X.; Liu, Q.; Du, J.; Zhang, J.J. Evaluating the inhibitory effect of eight compounds from Daphne papyracea against the NS3/4A protease of hepatitis C virus. Nat. Prod. Res., 2020, 34(11), 1607-1610. doi: 10.1080/14786419.2018.1519825 PMID: 30449158
  179. Viegas, D.J.; Edwards, T.G.; Bloom, D.C.; Abreu, P.A. Virtual screening identified compounds that bind to cyclin dependent kinase 2 and prevent herpes simplex virus type 1 replication and reactivation in neurons. Antiviral Res., 2019, 172, 104621. doi: 10.1016/j.antiviral.2019.104621 PMID: 31634495
  180. Friesner, R.A.; Banks, J.L.; Murphy, R.B.; Halgren, T.A.; Klicic, J.J.; Mainz, D.T.; Repasky, M.P.; Knoll, E.H.; Shelley, M.; Perry, J.K.; Shaw, D.E.; Francis, P.; Shenkin, P.S. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J. Med. Chem., 2004, 47(7), 1739-1749. doi: 10.1021/jm0306430 PMID: 15027865
  181. Ewing, T.J.A.; Makino, S.; Skillman, A.G.; Kuntz, I.D. DOCK 4.0: search strategies for automated molecular docking of flexible molecule databases. J. Comput. Aided Mol. Des., 2001, 15(5), 411-428. doi: 10.1023/A:1011115820450 PMID: 11394736
  182. Tahir ul Qamar, M.; Zhu, X.T.; Chen, L.L.; Alhussain, L.; Alshiekheid, M.A.; Theyab, A.; Algahtani, M. Target-specific machine learning scoring function improved structure-based virtual screening performance for SARS-CoV-2 drugs development. Int. J. Mol. Sci., 2022, 23(19), 11003. doi: 10.3390/ijms231911003 PMID: 36232307
  183. Gilson, M.K.; Liu, T.; Baitaluk, M.; Nicola, G.; Hwang, L.; Chong, J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res., 2016, 44(D1), D1045-D1053. doi: 10.1093/nar/gkv1072 PMID: 26481362
  184. 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, L.W.; Xu, W.; Xiao, G.; Qin, C.; Shi, Z.; Jiang, H.; Rao, Z.; Yang, H. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature, 2020, 582(7811), 289-293. doi: 10.1038/s41586-020-2223-y PMID: 32272481
  185. Wójcikowski, M.; Zielenkiewicz, P.; Siedlecki, P. Open Drug Discovery Toolkit (ODDT): A new open-source player in the drug discovery field. J. Cheminform., 2015, 7(1), 26. doi: 10.1186/s13321-015-0078-2 PMID: 26101548
  186. de Azevedo, W.F., J.r. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem., 2011, 18(9), 1353-1366. doi: 10.2174/092986711795029519 PMID: 21366529
  187. Bitencourt-Ferreira, G.; de Azevedo, W.F., J.r. Molecular dynamics simulations with NAMD2. Methods Mol. Biol., 2019, 2053, 109-124. doi: 10.1007/978-1-4939-9752-7_8 PMID: 31452102
  188. Santos, L.H.S.; Ferreira, R.S.; Caffarena, E.R. Integrating molecular docking and molecular dynamics simulations. Methods Mol. Biol., 2019, 2053, 13-34. doi: 10.1007/978-1-4939-9752-7_2 PMID: 31452096
  189. Hatamipour, M.; Hadizadeh, F.; Jaafari, M.R.; Khashyarmanesh, Z.; Sathyapalan, T.; Sahebkar, A. Anti-proliferative potential of fluorinated curcumin analogues: Experimental and computational analysis and review of the literature. Curr. Med. Chem., 2022, 29(8), 1459-1471. doi: 10.2174/0929867328666210910141316 PMID: 34514978
  190. Kim, C.; Kim, E. Rational drug design approach of receptor tyrosine kinase type III inhibitors. Curr. Med. Chem., 2020, 26(42), 7623-7640. doi: 10.2174/0929867325666180622143548 PMID: 29932031
  191. Hernández-Rodríguez, M.; Rosales-Hernández, M.C.; Mendieta-Wejebe, J.E.; Martínez-Archundia, M.; Basurto, J.C. Current tools and methods in Molecular Dynamics (MD) simulations for drug design. Curr. Med. Chem., 2016, 23(34), 3909-3924. doi: 10.2174/0929867323666160530144742 PMID: 27237821
  192. Azam, F.; Eid, E.E.M.; Almutairi, A. Targeting SARS-CoV-2 main protease by teicoplanin: A mechanistic insight by docking, MM/GBSA and molecular dynamics simulation. J. Mol. Struct., 2021, 1246, 131124. doi: 10.1016/j.molstruc.2021.131124 PMID: 34305175
  193. Dutta, K.; Elmezayen, A.D.; Al-Obaidi, A.; Zhu, W.; Morozova, O.V.; Shityakov, S.; Khalifa, I. Seq12, Seq12m, and Seq13m, peptide analogues of the spike glycoprotein shows antiviral properties against SARS-CoV-2: An in silico study through molecular docking, molecular dynamics simulation, and MM-PB/GBSA calculations. J. Mol. Struct., 2021, 1246, 131113. doi: 10.1016/j.molstruc.2021.131113 PMID: 34305174
  194. Zarezade, V.; Rezaei, H.; Shakerinezhad, G.; Safavi, A.; Nazeri, Z.; Veisi, A.; Azadbakht, O.; Hatami, M.; Sabaghan, M.; Shajirat, Z. The identification of novel inhibitors of human angiotensin-converting enzyme 2 and main protease of Sars-Cov-2: A combination of in silico methods for treatment of COVID-19. J. Mol. Struct., 2021, 1237, 130409. doi: 10.1016/j.molstruc.2021.130409 PMID: 33840836
  195. Sepay, N.; Sekar, A.; Halder, U.C.; Alarifi, A.; Afzal, M. Anti-COVID-19 terpenoid from marine sources: A docking, admet and molecular dynamics study. J. Mol. Struct., 2021, 1228, 129433. doi: 10.1016/j.molstruc.2020.129433 PMID: 33071352
  196. Walsh, I.; Fishman, D.; Garcia-Gasulla, D.; Titma, T.; Pollastri, G.; Capriotti, E.; Casadio, R.; Capella-Gutierrez, S.; Cirillo, D.; Del Conte, A.; Dimopoulos, A.C.; Del Angel, V.D.; Dopazo, J.; Fariselli, P.; Fernández, J.M.; Huber, F.; Kreshuk, A.; Lenaerts, T.; Martelli, P.L.; Navarro, A.; Broin, P.Ó.; Piñero, J.; Piovesan, D.; Reczko, M.; Ronzano, F.; Satagopam, V.; Savojardo, C.; Spiwok, V.; Tangaro, M.A.; Tartari, G.; Salgado, D.; Valencia, A.; Zambelli, F.; Harrow, J.; Psomopoulos, F.E.; Tosatto, S.C.E. DOME: Recommendations for supervised machine learning validation in biology. Nat. Methods, 2021, 18(10), 1122-1127. doi: 10.1038/s41592-021-01205-4 PMID: 34316068

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers