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Current Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 0929-8673
ISSN (Online): 1875-533X

Review Article

Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS

Author(s): Gabriela Bitencourt-Ferreira, Camila Rizzotto and Walter Filgueira de Azevedo Junior*

Volume 28, Issue 9, 2021

Published on: 15 May, 2020

Page: [1746 - 1756] Pages: 11

DOI: 10.2174/0929867327666200515101820

Price: $65

Abstract

Background: Analysis of atomic coordinates of protein-ligand complexes can provide three-dimensional data to generate computational models to evaluate binding affinity and thermodynamic state functions. Application of machine learning techniques can create models to assess protein-ligand potential energy and binding affinity. These methods show superior predictive performance when compared with classical scoring functions available in docking programs.

Objective: Our purpose here is to review the development and application of the program SAnDReS. We describe the creation of machine learning models to assess the binding affinity of protein-ligand complexes.

Methods: SAnDReS implements machine learning methods available in the scikit-learn library. This program is available for download at https://github.com/azevedolab/sandres. SAnDReS uses crystallographic structures, binding and thermodynamic data to create targeted scoring functions.

Results: Recent applications of the program SAnDReS to drug targets such as Coagulation factor Xa, cyclin-dependent kinases and HIV-1 protease were able to create targeted scoring functions to predict inhibition of these proteins. These targeted models outperform classical scoring functions.

Conclusion: Here, we reviewed the development of machine learning scoring functions to predict binding affinity through the application of the program SAnDReS. Our studies show the superior predictive performance of the SAnDReS-developed models when compared with classical scoring functions available in the programs such as AutoDock4, Molegro Virtual Docker and AutoDock Vina.

Keywords: Machine learning, SAnDReS, cyclin-dependent kinase, protein-ligand interactions, binding affinity, Gibbs free energy.

[1]
Labute, P. Methods of exploring protein-ligand interactions to guide medicinal chemistry efforts. Methods Mol. Biol., 2018, 1705, 159-177.
[http://dx.doi.org/10.1007/978-1-4939-7465-8_7] [PMID: 29188562]
[2]
de Azevedo, W.F. Jr.; Canduri, F.; Simões de Oliveira, J.; Basso, L.A.; Palma, M.S.; Pereira, J.H.; Santos, D.S. Molecular model of shikimate kinase from Mycobacterium tuberculosis. Biochem. Biophys. Res. Commun., 2002, 295(1), 142-148.
[http://dx.doi.org/10.1016/S0006-291X(02)00632-0] [PMID: 12083781]
[3]
Zhao, Q.; Lu, Y.; Zhao, Y.; Li, R.; Luan, F.; Cordeiro, M.N. Rational design of multi-target estrogen receptors ERα and ERβ by QSAR approaches. Curr. Drug Targets, 2017, 18(5), 576-591.
[http://dx.doi.org/10.2174/1389450117666160401125542] [PMID: 27033186]
[4]
Kontoyianni, M.; Lacy, B. Toward computational understanding of molecular recognition in the human metabolizing cytochrome P450s. Curr. Med. Chem., 2018, 25(28), 3353-3373.
[http://dx.doi.org/10.2174/0929867325666180226104126] [PMID: 29484977]
[5]
Rathore, R.S.; Sumakanth, M.; Reddy, M.S.; Reddanna, P.; Rao, A.A.; Erion, M.D.; Reddy, M.R. Advances in binding free energies calculations: QM/MM-based free energy perturbation method for drug design. Curr. Pharm. Des., 2013, 19(26), 4674-4686.
[http://dx.doi.org/10.2174/1381612811319260002] [PMID: 23260025]
[6]
de Azevedo, W.F. Jr. Molecular dynamics simulations of protein targets identified in Mycobacterium tuberculosis. Curr. Med. Chem., 2011, 18(9), 1353-1366.
[http://dx.doi.org/10.2174/092986711795029519] [PMID: 21366529]
[7]
Li, J.; Fu, A.; Zhang, L. An overview of scoring functions used for protein-ligand interactions in molecular docking. Interdiscip. Sci., 2020.
[http://dx.doi.org/10.1007/s12539-019-00327-w] [PMID: 30877639]
[8]
Li, H.; Peng, J.; Sidorov, P.; Leung, Y.; Leung, K.S.; Wong, M.H.; Lu, G.; Ballester, P.J. Classical scoring functions for docking are unable to exploit large volumes of structural and interaction data. Bioinformatics, 2019, 35(20), 3989-3995.
[http://dx.doi.org/10.1093/bioinformatics/btz183] [PMID: 30873528]
[9]
Sieg, J.; Flachsenberg, F.; Rarey, M. In need of bias control: evaluating chemical data for machine learning in structure-based virtual screening. J. Chem. Inf. Model., 2019, 59(3), 947-961.
[http://dx.doi.org/10.1021/acs.jcim.8b00712] [PMID: 30835112]
[10]
Yasuo, N.; Sekijima, M. Improved method of structure-based virtual screening via interaction-energy-based learning. J. Chem. Inf. Model., 2019, 59(3), 1050-1061.
[http://dx.doi.org/10.1021/acs.jcim.8b00673] [PMID: 30808172]
[11]
Nogueira, M.S.; Koch, O. The development of target-specific machine learning models as scoring functions for docking-based target prediction. J. Chem. Inf. Model., 2019, 59(3), 1238-1252.
[http://dx.doi.org/10.1021/acs.jcim.8b00773] [PMID: 30802041]
[12]
Zhenin, M.; Bahia, M.S.; Marcou, G.; Varnek, A.; Senderowitz, H.; Horvath, D. Rescoring of docking poses under Occam’s Razor: are there simpler solutions? J. Comput. Aided Mol. Des., 2018, 32(9), 877-888.
[http://dx.doi.org/10.1007/s10822-018-0155-5] [PMID: 30173397]
[13]
Sunseri, J.; King, J.E.; Francoeur, P.G.; Koes, D.R. Convolutional neural network scoring and minimization in the D3R 2017 community challenge. J. Comput. Aided Mol. Des., 2019, 33(1), 19-34.
[http://dx.doi.org/10.1007/s10822-018-0133-y] [PMID: 29992528]
[14]
Hochuli, J.; Helbling, A.; Skaist, T.; Ragoza, M.; Koes, D.R. Visualizing convolutional neural network protein-ligand scoring. J. Mol. Graph. Model., 2018, 84, 96-108.
[http://dx.doi.org/10.1016/j.jmgm.2018.06.005] [PMID: 29940506]
[15]
Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. Development of a machine-learning model to predict Gibbs free energy of binding for protein-ligand complexes. Biophys. Chem., 2018, 240, 63-69.
[http://dx.doi.org/10.1016/j.bpc.2018.05.010] [PMID: 29906639]
[16]
Ericksen, S.S.; Wu, H.; Zhang, H.; Michael, L.A.; Newton, M.A.; Hoffmann, F.M.; Wildman, S.A. Machine learning consensus scoring improves performance across targets in structure-based virtual screening. J. Chem. Inf. Model., 2017, 57(7), 1579-1590.
[http://dx.doi.org/10.1021/acs.jcim.7b00153] [PMID: 28654262]
[17]
Das, S.; Krein, M.P.; Breneman, C.M. Binding affinity prediction with property-encoded shape distribution signatures. J. Chem. Inf. Model., 2010, 50(2), 298-308.
[http://dx.doi.org/10.1021/ci9004139] [PMID: 20095526]
[18]
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.
[http://dx.doi.org/10.1093/bioinformatics/btq112] [PMID: 20236947]
[19]
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.
[http://dx.doi.org/10.1021/ci500091r] [PMID: 24528282]
[20]
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: Computacional Intelligence Methods for Bioinformatics and Biostatitics; Serio, C.D.I.; Liò, P.; Nonis, A., Eds.; Springer: Cambridge, UK, 2014, pp. 231-241.
[http://dx.doi.org/10.1007/978-3-319-24462-4_20]
[21]
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.
[http://dx.doi.org/10.1371/journal.pone.0085678] [PMID: 24475049]
[22]
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.
[http://dx.doi.org/10.1007/978-1-4939-9752-7_1] [PMID: 31452095]
[23]
Durrant, J.D.; McCammon, J.A. NNScore: a neural-network-based scoring function for the characterization of protein-ligand complexes. J. Chem. Inf. Model., 2010, 50(10), 1865-1871.
[http://dx.doi.org/10.1021/ci100244v] [PMID: 20845954]
[24]
Durrant, J.D.; McCammon, J.A. NNScore 2.0: a neural-network receptor-ligand scoring function. J. Chem. Inf. Model., 2011, 51(11), 2897-2903.
[http://dx.doi.org/10.1021/ci2003889] [PMID: 22017367]
[25]
Durrant, J.D.; Friedman, A.J.; Rogers, K.E.; McCammon, J.A. Comparing neural-network scoring functions and the state of the art: applications to common library screening. J. Chem. Inf. Model., 2013, 53(7), 1726-1735.
[http://dx.doi.org/10.1021/ci400042y] [PMID: 23734946]
[26]
Stepniewska-Dziubinska, M.M.; Zielenkiewicz, P.; Siedlecki, P. Development and evaluation of a deep learning model for protein-ligand binding affinity prediction. Bioinformatics, 2018, 34(21), 3666-3674.
[http://dx.doi.org/10.1093/bioinformatics/bty374] [PMID: 29757353]
[27]
da Silva, A.D.; Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. Taba: a tool to analyze the binding affinity. J. Comput. Chem., 2020, 41(1), 69-73.
[http://dx.doi.org/10.1002/jcc.26048] [PMID: 31410856]
[28]
Bitencourt-Ferreira, G.; da Silva, A.D.; de Azevedo, W.F. Jr. Application of machine learning techniques to predict binding affinity for drug targets. A Study of cyclin-dependent kinase 2. Curr. Med. Chem., 2019.
[http://dx.doi.org/10.2174/2213275912666191102162959] [PMID: 31729287]
[29]
Xavier, M.M.; Heck, G.S.; Avila, M.B.; Levin, N.M.B.; Pintro, V.O.; Carvalho, N.L.; Azevedo, W.F. Jr. 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.
[http://dx.doi.org/10.2174/1386207319666160927111347] [PMID: 27686428]
[30]
Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. SAnDRes: a computational tool for docking. Methods Mol. Biol., 2019, 2053, 51-65.
[http://dx.doi.org/10.1007/978-1-4939-9752-7_4] [PMID: 31452098]
[31]
Heck, G.S.; Pintro, V.O.; Pereira, R.R.; de Ávila, M.B.; Levin, N.M.B.; de Azevedo, W.F., Jr. Supervised machine learning methods applied to predict ligand-binding affinity. Curr. Med. Chem., 2017, 24(23), 2459-2470.
[http://dx.doi.org/10.2174/0929867324666170623092503] [PMID: 28641555]
[32]
Levin, N.M.B.; Pintro, V.O.; de Ávila, M.B.; de Mattos, B.B.; de Azevedo, W.F. Jr. Understanding the structural basis for inhibition of cyclin-dependent kinases. New pieces in the molecular puzzle. Curr. Drug Targets, 2017, 18(9), 1104-1111.
[http://dx.doi.org/10.2174/1389450118666161116130155] [PMID: 27848884]
[33]
de Ávila, M.B.; Xavier, M.M.; Pintro, V.O.; de Azevedo, W.F. Jr. Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2. Biochem. Biophys. Res. Commun., 2017, 494(1-2), 305-310.
[http://dx.doi.org/10.1016/j.bbrc.2017.10.035] [PMID: 29017921]
[34]
Amaral, M.E.A.; Nery, L.R.; Leite, C.E.; de Azevedo, W.F. Jr.; Campos, M.M. Pre-clinical effects of metformin and aspirin on the cell lines of different breast cancer subtypes. Invest. New Drugs, 2018, 36(5), 782-796.
[http://dx.doi.org/10.1007/s10637-018-0568-y] [PMID: 29392539]
[35]
Pintro, V.O.; de Azevedo, W.F. Jr. optimized virtual screening workflow: towards target-based polynomial scoring functions for HIV-1 protease. Comb. Chem. High Throughput Screen., 2017, 20(9), 820-827.
[http://dx.doi.org/10.2174/1386207320666171121110019] [PMID: 29165067]
[36]
Levin, N.M.B.; Pintro, V.O.; Bitencourt-Ferreira, G.; de Mattos, B.B.; de Castro Silvério, A.; de Azevedo, W.F. Jr. Development of CDK-targeted scoring functions for prediction of binding affinity. Biophys. Chem., 2018, 235, 1-8.
[http://dx.doi.org/10.1016/j.bpc.2018.01.004] [PMID: 29407904]
[37]
de Ávila, M.B.; de Azevedo, W.F., Jr Development of machine learning models to predict inhibition of 3-dehydroquinate dehydratase. Chem. Biol. Drug Des., 2018, 92(2), 1468-1474.
[http://dx.doi.org/10.1111/cbdd.13312] [PMID: 29676519]
[38]
Russo, S.; de Azevedo, W.F., Jr Advances in the understanding of the cannabinoid receptor 1 - focusing on the inverse agonists interactions. Curr. Med. Chem., 2019, 26(10), 1908-1919.
[http://dx.doi.org/10.2174/0929867325666180417165247] [PMID: 29667549]
[39]
Ribeiro, F.F.; Mendonca Junior, F.J.B.; Ghasemi, J.B.; Ishiki, H.M.; Scotti, M.T.; Scotti, L. Docking of natural products against neurodegenerative diseases: general concepts. Comb. Chem. High Throughput Screen., 2018, 21(3), 152-160.
[http://dx.doi.org/10.2174/1386207321666180313130314] [PMID: 29532756]
[40]
Maltarollo, V.G.; Kronenberger, T.; Windshugel, B.; Wrenger, C.; Trossini, G.H.G.; Honorio, K.M. Advances and challenges in drug design of PPARδ ligands. Curr. Drug Targets, 2018, 19(2), 144-154.
[http://dx.doi.org/10.2174/1389450118666170414113159] [PMID: 28413978]
[41]
Lawal, M.M.; Sanusi, Z.K.; Govender, T.; Maguire, G.E.M.; Honarparvar, B.; Kruger, H.G. From recognition to reaction mechanism: an overview on the interactions between HIV-1 protease and its natural targets. Curr. Med. Chem., 2020, 27(15), 2514-2549.
[http://dx.doi.org/10.2174/0929867325666181113122900] [PMID: 30421668]
[42]
Smith, R.D.; Clark, J.J.; Ahmed, A.; Orban, Z.J.; Dunbar, J.B., Jr; Carlson, H.A. Updates to binding MOAD (Mother of All Databases): polypharmacology tools and their utility in drug repurposing. J. Mol. Biol., 2019, 431(13), 2423-2433.
[http://dx.doi.org/10.1016/j.jmb.2019.05.024] [PMID: 31125569]
[43]
Freitas, P.G.; Elias, T.C.; Pinto, I.A.; Costa, L.T.; de Carvalho, P.V.S.D.; Omote, D.Q.; Camps, I.; Ishikawa, T.; Arcuri, H.A.; Vinga, S.; Oliveira, A.L.; Junior, W.F.A.; da Silveira, N.J.F. Computational approach to the discovery of phytochemical molecules with therapeutic potential targets to the PKCZ protein. Lett. Drug Des. Discov., 2018, 15(5), 488-499.
[http://dx.doi.org/10.2174/1570180814666170810120150]
[44]
Abbasi, W.A.; Asif, A.; Ben-Hur, A.; Minhas, F.U.A.A. Learning protein binding affinity using privileged information. BMC Bioinformatics, 2018, 19(1), 425.
[http://dx.doi.org/10.1186/s12859-018-2448-z] [PMID: 30442086]
[45]
Singh, A.; Somvanshi, P.; Grover, A. Drug repurposing against arabinosyl transferase (EmbC) of Mycobacterium tuberculosis: essential dynamics and free energy minima based binding mechanics analysis. Gene, 2019, 693, 114-126.
[http://dx.doi.org/10.1016/j.gene.2019.01.029] [PMID: 30716439]
[46]
Zhang, W.; Li, W.; Zhang, J.; Wang, N. Data integration of hybrid microarray and single cell expression data to enhance gene network inference. Curr. Bioinform., 2019, 14(3), 255-268.
[http://dx.doi.org/10.2174/1574893614666190104142228]
[47]
Volkart, P.A.; Bitencourt-Ferreira, G.; Souto, A.A.; de Azevedo, W.F. Jr. Cyclin-dependent kinase 2 in cellular senescence and cancer. A structural and functional review. Curr. Drug Targets, 2019, 20(7), 716-726.
[http://dx.doi.org/10.2174/1389450120666181204165344] [PMID: 30516105]
[48]
Cavada, B.S.; Osterne, V.J.S.; Lossio, C.F.; Pinto-Junior, V.R.; Oliveira, M.V.; Silva, M.T.L.; Leal, R.B.; Nascimento, K.S. One century of ConA and 40 years of ConBr research: a structural review. Int. J. Biol. Macromol., 2019, 134, 901-911.
[http://dx.doi.org/10.1016/j.ijbiomac.2019.05.100] [PMID: 31108148]
[49]
de Ávila, M.B.; Bitencourt-Ferreira, G.; de Azevedo, W.F. Jr. structural basis for inhibition of Enoyl-[Acyl Carrier Protein] reductase (InhA) from Mycobacterium tuberculosis. Curr. Med. Chem., 2020, 27(5), 745-759.
[http://dx.doi.org/10.2174/0929867326666181203125229] [PMID: 30501592]
[50]
Russo, S.; de Azevedo, W.F. Jr. Computational analysis of dipyrone metabolite 4-aminoantipyrine as a cannabinoid receptor 1 agonist. Curr. Med. Chem., 2019.
[http://dx.doi.org/10.2174/0929867326666190906155339] [PMID: 31490743]
[51]
Jiang, M.; Li, Z.; Bian, Y.; Wei, Z. A novel protein descriptor for the prediction of drug binding sites. BMC Bioinformatics, 2019, 20(1), 478.
[http://dx.doi.org/10.1186/s12859-019-3058-0] [PMID: 31533611]
[52]
Safarizadeh, H.; Garkani-Nejad, Z. Investigation of MI-2 analogues as MALT1 inhibitors to treat of diffuse large B-Cell lymphoma through combined molecular dynamics simulation, molecular docking and QSAR techniques and design of new inhibitors‎. J. Mol. Struct., 2019, 1180, 708-722.
[http://dx.doi.org/10.1016/j.molstruc.2018.12.022]
[53]
Masand, V.H.; El-Sayed, N.N.E.; Bambole, M.U.; Patil, V.R.; Thakur, S.D. Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors. J. Mol. Struct., 2019, 1175, 481-487.
[http://dx.doi.org/10.1016/j.molstruc.2018.07.080]
[54]
Gemovic, B.; Sumonja, N.; Davidovic, R.; Perovic, V.; Veljkovic, N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr. Med. Chem., 2019, 26(21), 3890-3910.
[http://dx.doi.org/10.2174/0929867325666180214113704] [PMID: 29446725]
[55]
Lushington, G.H. Chemistry, screening and the democracy of publishing. Comb. Chem. High Throughput Screen., 2019, 22(5), 288-289.
[http://dx.doi.org/10.2174/1386207322999190715161959] [PMID: 31446889]
[56]
Jehangir, I.; Ahmad, S.F.; Jehangir, M.; Jamal, A.; Khan, M. Integration of bioinformatics and in vitro analysis reveal anti-leishmanial effects of azithromycin and nystatin. Curr. Bioinform., 2019, 14(5), 450-459.
[http://dx.doi.org/10.2174/1574893614666181217142344]
[57]
Nowaczyk, A.; Fijałkowski, Ł.; Zaręba, P.; Sałat, K. Docking and pharmacodynamic studies on hGAT1 inhibition activity in the presence of selected neuronal and astrocytic inhibitors. Part I. J. Mol. Graph. Model., 2018, 85, 171-181.
[http://dx.doi.org/10.1016/j.jmgm.2018.09.003] [PMID: 30219588]
[58]
Neco, A.H.B.; Pinto-Junior, V.R.; Araripe, D.A.; Santiago, M.Q.; Osterne, V.J.S.; Lossio, C.F.; Nobre, C.A.S.; Oliveira, M.V.; Silva, M.T.L.; Martins, M.G.Q.; Cajazeiras, J.B.; Marques, G.F.O.; Costa, D.R.; Nascimento, K.S.; Assreuy, A.M.S.; Cavada, B.S. Structural analysis, molecular docking and molecular dynamics of an edematogenic lectin from Centrolobium microchaete seeds. Int. J. Biol. Macromol., 2018, 117, 124-133.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.05.166] [PMID: 29802925]
[59]
Tong, J.; Lei, S.; Qin, S.; Wang, Y. QSAR studies of TIBO derivatives as HIV-1 reverse transcriptase inhibitors using HQSAR, CoMFA and CoMSIA. J. Mol. Struct., 2018, 1168, 56-64.
[http://dx.doi.org/10.1016/j.molstruc.2018.05.005]
[60]
Leal, R.B.; Pinto-Junior, V.R.; Osterne, V.J.S.; Wolin, I.A.V.; Nascimento, A.P.M.; Neco, A.H.B.; Araripe, D.A.; Welter, P.G.; Neto, C.C.; Correia, J.L.A.; Rocha, C.R.C.; Nascimento, K.S.; Cavada, B.S. Crystal structure of DlyL, a mannose-specific lectin from Dioclea lasiophylla Mart. Ex Benth seeds that display cytotoxic effects against C6 glioma cells. Int. J. Biol. Macromol., 2018, 114, 64-76.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.03.080] [PMID: 29559315]
[61]
Joy, M.; Elrashedy, A.A.; Mathew, B.; Pillay, A.S.; Mathews, A.; Dev, S.; Soliman, M.E.S.; Sudarsanakumar, C. Discovery of new class of methoxy carrying isoxazole derivatives as COX-II inhibitors: investigation of a detailed molecular dynamics study. J. Mol. Struct., 2018, 1157, 19-28.
[http://dx.doi.org/10.1016/j.molstruc.2017.11.109]
[62]
Cavada, B.S.; Araripe, D.A.; Silva, I.B.; Pinto-Junior, V.R.; Osterne, V.J.S.; Neco, A.H.B.; Laranjeira, E.P.P.; Lossio, C.F.; Correia, J.L.A.; Pires, A.F.; Assreuy, A.M.S.; Nascimento, K.S. Structural studies and nociceptive activity of a native lectin from Platypodium elegans seeds (nPELa). Int. J. Biol. Macromol., 2018, 107(Pt A), 236-246.
[http://dx.doi.org/10.1016/j.ijbiomac.2017.08.174] [PMID: 28867234]
[63]
Lemos, A.; Melo, R.; Preto, A.J.; Almeida, J.G.; Moreira, I.S.; Dias Soeiro Cordeiro, M.N. In silico studies targeting g-protein coupled receptors for drug research against parkinson’s disease. Curr. Neuropharmacol., 2018, 16(6), 786-848.
[http://dx.doi.org/10.2174/1570159X16666180308161642] [PMID: 29521236]
[64]
Mohd Usman, M.S.; Bharbhuiya, T.K.; Mondal, S.; Rani, S.; Kyal, C.; Kumari, R. Combined protein and ligand based physicochemical aspects of molecular recognition for the discovery of CDK9 inhibitor. Gene Rep., 2018, 13, 212-219.
[http://dx.doi.org/10.1016/j.genrep.2018.10.011]
[65]
Pinto-Junior, V.R.; Osterne, V.J.; Santiago, M.Q.; Correia, J.L.; Pereira-Junior, F.N.; Leal, R.B.; Pereira, M.G.; Chicas, L.S.; Nagano, C.S.; Rocha, B.A.; Silva-Filho, J.C.; Ferreira, W.P.; Rocha, C.R.; Nascimento, K.S.; Assreuy, A.M.; Cavada, B.S. Structural studies of a vasorelaxant lectin from Dioclea reflexa Hook seeds: crystal structure, molecular docking and dynamics. Int. J. Biol. Macromol., 2017, 98, 12-23.
[http://dx.doi.org/10.1016/j.ijbiomac.2017.01.092] [PMID: 28130130]
[66]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res., 2000, 28(1), 235-242.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[67]
Berman, H.M.; Battistuz, T.; Bhat, T.N.; Bluhm, W.F.; Bourne, P.E.; Burkhardt, K.; Feng, Z.; Gilliland, G.L.; Iype, L.; Jain, S.; Fagan, P.; Marvin, J.; Padilla, D.; Ravichandran, V.; Schneider, B.; Thanki, N.; Weissig, H.; Westbrook, J.D.; Zardecki, C. The protein data bank. Acta Crystallogr. D Biol. Crystallogr., 2002, 58(Pt 6 No 1), 899-907.
[http://dx.doi.org/10.1107/S0907444902003451]
[68]
Westbrook, J.; Feng, Z.; Chen, L.; Yang, H.; Berman, H.M. The protein data bank and structural genomics. Nucleic Acids Res., 2003, 31(1), 489-491.
[http://dx.doi.org/10.1093/nar/gkg068] [PMID: 12520059]
[69]
Thomsen, R.; Christensen, M.H. MolDock: a new technique for high-accuracy molecular docking. J. Med. Chem., 2006, 49(11), 3315-3321.
[http://dx.doi.org/10.1021/jm051197e] [PMID: 16722650]
[70]
Heberlé, G.; de Azevedo, W.F., Jr Bio-inspired algorithms applied to molecular docking simulations. Curr. Med. Chem., 2011, 18(9), 1339-1352.
[http://dx.doi.org/10.2174/092986711795029573] [PMID: 21366530]
[71]
Bitencourt-Ferreira, G.; de Azevedo, W.F., Jr Molegro virtual docker for docking. Methods Mol. Biol., 2019, 2053, 149-167.
[http://dx.doi.org/10.1007/978-1-4939-9752-7_10] [PMID: 31452104]
[72]
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.
[http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]
[73]
Bitencourt-Ferreira, G.; Pintro, V.O.; de Azevedo, W.F. Jr. Docking with AutoDock4. Methods Mol. Biol., 2019, 2053, 125-148.
[http://dx.doi.org/10.1007/978-1-4939-9752-7_9] [PMID: 31452103]
[74]
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.
[http://dx.doi.org/10.1002/jcc.21334] [PMID: 19499576]
[75]
Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol., 1996, 58(1), 267-288.
[http://dx.doi.org/10.1111/j.2517-6161.1996.tb02080.x]
[76]
Tikhonov, A.N. On the regularization of ill-posed problems. Dokl. Akad. Nauk SSSR, 1963, 153(1), 49-52.
[77]
Zou, H.; Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol., 2005, 67(2), 301-220.
[http://dx.doi.org/10.1111/j.1467-9868.2005.00503.x]
[78]
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V.; Verplas, J.; Passos, A.; Cournapeau, D.; Brucher, M.; Perrot, M.; Duchesnay, E. Scikitlearn: machine learning in python. J. Mach. Learn. Res., 2011, 12, 2825-2830.
[79]
de Azevedo, W.F. Jr.; Dias, R. Evaluation of ligand-binding affinity using polynomial empirical scoring functions. Bioorg. Med. Chem., 2008, 16(20), 9378-9382.
[http://dx.doi.org/10.1016/j.bmc.2008.08.014] [PMID: 18829335]
[80]
Dias, R.; Timmers, L.F.; Caceres, R.A.; de Azevedo, W.F. Jr. Evaluation of molecular docking using polynomial empirical scoring functions. Curr. Drug Targets, 2008, 9(12), 1062-1070.
[http://dx.doi.org/10.2174/138945008786949450] [PMID: 19128216]
[81]
Ducati, R.G.; Basso, L.A.; Santos, D.S.; de Azevedo, W.F. Jr. Crystallographic and docking studies of purine nucleoside phosphorylase from Mycobacterium tuberculosis. Bioorg. Med. Chem., 2010, 18(13), 4769-4774.
[http://dx.doi.org/10.1016/j.bmc.2010.05.009] [PMID: 20570524]
[82]
de Azevedo, W.F. Jr.; Dias, R. Experimental approaches to evaluate the thermodynamics of protein-drug interactions. Curr. Drug Targets, 2008, 9(12), 1071-1076.
[http://dx.doi.org/10.2174/138945008786949441] [PMID: 19128217]
[83]
Liu, T.; Lin, Y.; Wen, X.; Jorissen, R.N.; Gilson, M.K. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res., 2007, 35(Database issue), D198-D201.
[http://dx.doi.org/10.1093/nar/gkl999] [PMID: 17145705]
[84]
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.
[http://dx.doi.org/10.1093/nar/gkv1072] [PMID: 26481362]
[85]
Benson, M.L.; Smith, R.D.; Khazanov, N.A.; Dimcheff, B.; Beaver, J.; Dresslar, P.; Nerothin, J.; Carlson, H.A. Binding MOAD, a high-quality protein-ligand database. Nucleic Acids Res., 2008, 36(Database issue), D674-D678.
[http://dx.doi.org/10.1093/nar/gkm911] [PMID: 18055497]
[86]
Ahmed, A.; Smith, R.D.; Clark, J.J.; Dunbar, J.B., Jr; Carlson, H.A. Recent improvements to Binding MOAD: a resource for protein-ligand binding affinities and structures. Nucleic Acids Res., 2015, 43(Database issue), D465-D469.
[http://dx.doi.org/10.1093/nar/gku1088] [PMID: 25378330]
[87]
Liu, Z.; Li, Y.; Han, L.; Li, J.; Liu, J.; Zhao, Z.; Nie, W.; Liu, Y.; Wang, R. PDB-wide collection of binding data: current status of the PDBbind database. Bioinformatics, 2015, 31(3), 405-412.
[http://dx.doi.org/10.1093/bioinformatics/btu626] [PMID: 25301850]
[88]
Zar, J.H. Significance testing of the spearman rank correlation coefficient. J. Am. Stat. Assoc., 1972, 67(339), 578-580.
[http://dx.doi.org/10.1080/01621459.1972.10481251]
[89]
Cheng, Y.; Prusoff, W.H. Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem. Pharmacol., 1973, 22(23), 3099-3108.
[http://dx.doi.org/10.1016/0006-2952(73)90196-2] [PMID: 4202581]
[90]
Salonen, L.M.; Bucher, C.; Banner, D.W.; Haap, W.; Mary, J.L.; Benz, J.; Kuster, O.; Seiler, P.; Schweizer, W.B.; Diederich, F. Cation-pi interactions at the active site of factor Xa: dramatic enhancement upon stepwise N-alkylation of ammonium ions. Angew. Chem. Int. Ed. Engl., 2009, 48(4), 811-814.
[http://dx.doi.org/10.1002/anie.200804695] [PMID: 19101972]
[91]
de Azevedo, W.F. Jr.; 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.
[http://dx.doi.org/10.1111/j.1432-1033.1997.0518a.x] [PMID: 9030780]
[92]
de Azevedo, W.F. Jr.; Mueller-Dieckmann, H.J.; Schulze-Gahmen, U.; Worland, P.J.; Sausville, E.; Kim, S.H. Structural basis for specificity and potency of a flavonoid inhibitor of human CDK2, a cell cycle kinase. Proc. Natl. Acad. Sci. USA, 1996, 93(7), 2735-2740.
[http://dx.doi.org/10.1073/pnas.93.7.2735] [PMID: 8610110]
[93]
Kim, S.H.; Schulze-Gahmen, U.; Brandsen, J.; de Azevedo, W.F. Jr. Structural basis for chemical inhibition of CDK2. Prog. Cell Cycle Res., 1996, 2, 137-145.
[http://dx.doi.org/10.1007/978-1-4615-5873-6_14] [PMID: 9552391]
[94]
Canduri, F.; de Azevedo, W.F. Jr. Structural basis for interaction of inhibitors with cyclin-dependent kinase 2. Curr. Comput. Aided Drug Des., 2005, 1(1), 53-64.
[http://dx.doi.org/10.2174/1573409052952233]
[95]
Krystof, V.; Cankar, P.; Frysová, I.; Slouka, J.; Kontopidis, G.; Dzubák, P.; Hajdúch, M.; Srovnal, J.; de Azevedo, W.F. Jr.; Orság, M.; Paprskárová, M.; Rolcík, J.; Látr, A.; Fischer, P.M.; Strnad, M. 4-arylazo-3,5-diamino-1H-pyrazole CDK inhibitors: SAR study, crystal structure in complex with CDK2, selectivity and cellular effects. J. Med. Chem., 2006, 49(22), 6500-6509.
[http://dx.doi.org/10.1021/jm0605740] [PMID: 17064068]
[96]
Li, J.; Vervoorts, J.; Carloni, P.; Rossetti, G.; Lüscher, B. Structural prediction of the interaction of the tumor suppressor p27KIP1 with cyclin A/CDK2 identifies a novel catalytically relevant determinant. BMC Bioinformatics, 2017, 18(1), 15.
[http://dx.doi.org/10.1186/s12859-016-1411-0] [PMID: 28056778]

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