Login Register Cart 0
Generic placeholder image

Current Bioactive Compounds

Editor-in-Chief

ISSN (Print): 1573-4072
ISSN (Online): 1875-6646

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Review Article

Consensus Docking in Drug Discovery

Author(s): Giulio Poli and Tiziano Tuccinardi *

Volume 16, Issue 3, 2020

Page: [182 - 190] Pages: 9

DOI: 10.2174/1573407214666181023114820

Price: $65

Abstract

Background: Molecular docking is probably the most popular and profitable approach in computer-aided drug design, being the staple technique for predicting the binding mode of bioactive compounds and for performing receptor-based virtual screening studies. The growing attention received by docking, as well as the need for improving its reliability in pose prediction and virtual screening performance, has led to the development of a wide plethora of new docking algorithms and scoring functions. Nevertheless, it is unlikely to identify a single procedure outperforming the other ones in terms of reliability and accuracy or demonstrating to be generally suitable for all kinds of protein targets.

Methods: In this context, consensus docking approaches are taking hold in computer-aided drug design. These computational protocols consist in docking ligands using multiple docking methods and then comparing the binding poses predicted for the same ligand by the different methods. This analysis is usually carried out calculating the root-mean-square deviation among the different docking results obtained for each ligand, in order to identify the number of docking methods producing the same binding pose.

Results: The consensus docking approaches demonstrated to improve the quality of docking and virtual screening results compared to the single docking methods. From a qualitative point of view, the improvement in pose prediction accuracy was obtained by prioritizing ligand binding poses produced by a high number of docking methods, whereas with regards to virtual screening studies, high hit rates were obtained by prioritizing the compounds showing a high level of pose consensus.

Conclusion: In this review, we provide an overview of the results obtained from the performance assessment of various consensus docking protocols and we illustrate successful case studies where consensus docking has been applied in virtual screening studies.

Keywords: Consensus docking, molecular modeling, virtual screening, docking reliability, computer-aided drug design, scoring functions.

Graphical Abstract

[1]
Oshiro, C.M.; Kuntz, I.D.; Dixon, J.S. Flexible ligand docking using a genetic algorithm. J. Comput. Aided Mol. Des., 1995, 9(2), 113-130.
[http://dx.doi.org/10.1007/BF00124402] [PMID: 7608743]
[2]
Ferrarini, P.L.; Betti, L.; Cavallini, T.; Giannaccini, G.; Lucacchini, A.; Manera, C.; Martinelli, A.; Ortore, G.; Saccomanni, G.; Tuccinardi, T. Study on affinity profile toward native human and bovine adenosine receptors of a series of 1,8-naphthyridine derivatives. J. Med. Chem., 2004, 47(12), 3019-3031.
[http://dx.doi.org/10.1021/jm030977p] [PMID: 15163184]
[3]
Bello, M.; Martínez-Archundia, M.; Correa-Basurto, J. Automated docking for novel drug discovery. Expert Opin. Drug Discov., 2013, 8(7), 821-834.
[http://dx.doi.org/10.1517/17460441.2013.794780] [PMID: 23642085]
[4]
Godugu, D.; Rupula, K.; Beedu, S.R. Inhibitory action of andrographolide on cyclooxygenase-2 enzyme activity: In silico and in vitro studies. Curr. Bioact. Compd., 2016, 12(1), 44-50.
[http://dx.doi.org/10.2174/1573407212666160310235908]
[5]
Diniz, E.M.L.P.; Poiani, J.G.C.; Taft, C.A.; da Silva, C.H.T.P. Structure-based drug design, molecular dynamics and ADME/Tox to investigate protein kinase anti-cancer agents. Curr. Bioact. Compd., 2017, 13(3), 213-222.
[http://dx.doi.org/10.2174/1573407212666160607093114]
[6]
Mondal, S.; Mukherjee, S.; Malakar, S.; Debnath, S.; Roy, P.; Sinha Babu, S.P. Studying the biological activities and molecular docking of some novel benzosultams and benzosultones. Curr. Bioact. Compd., 2017, 13(4), 347-355.
[http://dx.doi.org/10.2174/1573407212666161028160745]
[7]
El Sayed, M.T.; Voronkov, A.; Ogungbe, I.V.; El-Hallouty, S.M.; Ahmed, K.M.; Vladimir, B.; Balaki, K. Anti cancer, molecular docking and structure activity relationship studies of some novel synthetic spiroindolo[3,2-b]carbazoles as predicted HER2 and BrK kinase inhibitors. Curr. Bioact. Compd., 2018, 14(2), 119-133.
[http://dx.doi.org/10.2174/1573407213666170213154357]
[8]
Irwin, J.J.; Shoichet, B.K. Docking screens for novel ligands conferring new biology. J. Med. Chem., 2016, 59(9), 4103-4120.
[http://dx.doi.org/10.1021/acs.jmedchem.5b02008] [PMID: 26913380]
[9]
Kawano, D.F.; Taft, C.A.; da Silva, C.H.T.P. Prospecting for new inhibitors of anaplastic lymphoma kinase, a clinically relevant oncogenic drug target. Curr. Bioact. Compd., 2017, 13(3), 236-243.
[http://dx.doi.org/10.2174/1573407212666160607092819]
[10]
Chen, Y-C. Beware of docking! Trends Pharmacol. Sci., 2015, 36(2), 78-95.
[http://dx.doi.org/10.1016/j.tips.2014.12.001] [PMID: 25543280]
[11]
Bader, A.; Tuccinardi, T.; Granchi, C.; Martinelli, A.; Macchia, M.; Minutolo, F.; De Tommasi, N.; Braca, A. Phenylpropanoids and flavonoids from Phlomis kurdica as inhibitors of human lactate dehydrogenase. Phytochemistry, 2015, 116(1), 262-268.
[http://dx.doi.org/10.1016/j.phytochem.2015.03.007] [PMID: 25890391]
[12]
Milella, L.; Milazzo, S.; De Leo, M.; Vera Saltos, M.B.; Faraone, I.; Tuccinardi, T.; Lapillo, M.; De Tommasi, N.; Braca, A. α-Glucosidase and α-Amylase Inhibitors from Arcytophyllum thymifolium. J. Nat. Prod., 2016, 79(8), 2104-2112.
[http://dx.doi.org/10.1021/acs.jnatprod.6b00484] [PMID: 27509358]
[13]
Aghazadeh Tabrizi, M.; Baraldi, P.G.; Ruggiero, E.; Saponaro, G.; Baraldi, S.; Poli, G.; Tuccinardi, T.; Ravani, A.; Vincenzi, F.; Borea, P.A.; Varani, K. Synthesis and structure activity relationship investigation of triazolo[1,5-a]pyrimidines as CB2 cannabinoid receptor inverse agonists. Eur. J. Med. Chem., 2016, 113, 11-27.
[http://dx.doi.org/10.1016/j.ejmech.2016.02.032] [PMID: 26922225]
[14]
Salmaso, V.; Moro, S. Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Front. Pharmacol., 2018, 9, 923.
[http://dx.doi.org/10.3389/fphar.2018.00923] [PMID: 30186166]
[15]
Carpi, S.; Polini, B.; Poli, G.; Alcantara Barata, G.; Fogli, S.; Romanini, A.; Tuccinardi, T.; Guella, G.; Frontini, F.P.; Nieri, P.; Di Giuseppe, G. Anticancer activity of Euplotin C, isolated from the marine ciliate Euplotes crassus, against human melanoma cells. Mar. Drugs, 2018, 16(5), 166.
[http://dx.doi.org/10.3390/md16050166] [PMID: 29772645]
[16]
Ramírez, D.; Caballero, J. Is it reliable to take the molecular docking top scoring position as the best solution without considering available structural data? Molecules, 2018, 23(5), 1038.
[http://dx.doi.org/10.3390/molecules23051038] [PMID: 29710787]
[17]
Ballante, F.; Marshall, G.R. An automated strategy for binding-pose selection and docking assessment in structure-based drug design. J. Chem. Inf. Model., 2016, 56(1), 54-72.
[http://dx.doi.org/10.1021/acs.jcim.5b00603] [PMID: 26682916]
[18]
Tuccinardi, T.; Poli, G.; Dell’Agnello, M.; Granchi, C.; Minutolo, F.; Martinelli, A. Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands. J. Enzyme Inhib. Med. Chem., 2015, 30(4), 662-670.
[http://dx.doi.org/10.3109/14756366.2014.959946] [PMID: 25265323]
[19]
Wang, J-C.; Lin, J-H. Scoring functions for prediction of protein-ligand interactions. Curr. Pharm. Des., 2013, 19(12), 2174-2182.
[http://dx.doi.org/10.2174/1381612811319120005] [PMID: 23016847]
[20]
Ferreira, L.G.; Dos Santos, R.N.; Oliva, G.; Andricopulo, A.D. Molecular docking and structure-based drug design strategies. Molecules, 2015, 20(7), 13384-13421.
[http://dx.doi.org/10.3390/molecules200713384] [PMID: 26205061]
[21]
Li, Y.; Su, M.; Liu, Z.; Li, J.; Liu, J.; Han, L.; Wang, R. Assessing protein-ligand interaction scoring functions with the CASF-2013 benchmark. Nat. Protoc., 2018, 13(4), 666-680.
[http://dx.doi.org/10.1038/nprot.2017.114] [PMID: 29517771]
[22]
Kooistra, A.J.; Vischer, H.F.; McNaught-Flores, D.; Leurs, R.; de Esch, I.J.P.; de Graaf, C. Function-specific virtual screening for GPCR ligands using a combined scoring method. Sci. Rep., 2016, 6(1), 28288.
[http://dx.doi.org/10.1038/srep28288] [PMID: 27339552]
[23]
Pu, C.; Yan, G.; Shi, J.; Li, R. Assessing the performance of docking scoring function, FEP, MM-GBSA, and QM/MM-GBSA approaches on a series of PLK1 inhibitors. MedChemComm, 2017, 8(7), 1452-1458.
[http://dx.doi.org/10.1039/C7MD00184C] [PMID: 30108856]
[24]
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]
[25]
Levin, N.M.B.; Pintro, V.O.; Bitencourt-Ferreira, G.; de Mattos, B.B.; de Castro Silvério, A.; de Azevedo, W.F. 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]
[26]
Poli, G.; Jha, V.; Martinelli, A.; Supuran, C.T.; Tuccinardi, T. Development of a fingerprint-based scoring function for the prediction of the binding mode of carbonic anhydrase II inhibitors. Int. J. Mol. Sci., 2018, 19(7), 1851.
[http://dx.doi.org/10.3390/ijms19071851] [PMID: 29937490]
[27]
Charifson, P.S.; Corkery, J.J.; Murcko, M.A.; Walters, W.P. Consensus scoring: A method for obtaining improved hit rates from docking databases of three-dimensional structures into proteins. J. Med. Chem., 1999, 42(25), 5100-5109.
[http://dx.doi.org/10.1021/jm990352k] [PMID: 10602695]
[28]
Wang, R.; Wang, S. How does consensus scoring work for virtual library screening? An idealized computer experiment. J. Chem. Inf. Comput. Sci., 2001, 41(5), 1422-1426.
[http://dx.doi.org/10.1021/ci010025x] [PMID: 11604043]
[29]
Liu, S.; Fu, R.; Zhou, L-H.; Chen, S-P. Application of consensus scoring and principal component analysis for virtual screening against β-secretase (BACE-1). PLoS One, 2012, 7(6)e38086
[http://dx.doi.org/10.1371/journal.pone.0038086] [PMID: 22701601]
[30]
Poli, G.; Tuccinardi, T.; Rizzolio, F.; Caligiuri, I.; Botta, L.; Granchi, C.; Ortore, G.; Minutolo, F.; Schenone, S.; Martinelli, A. Identification of new Fyn kinase inhibitors using a FLAP-based approach. J. Chem. Inf. Model., 2013, 53(10), 2538-2547.
[http://dx.doi.org/10.1021/ci4002553] [PMID: 24001328]
[31]
Park, H.; Eom, J-W.; Kim, Y-H. Consensus scoring approach to identify the inhibitors of AMP-activated protein kinase α2 with virtual screening. J. Chem. Inf. Model., 2014, 54(7), 2139-2146.
[http://dx.doi.org/10.1021/ci500214e] [PMID: 24915156]
[32]
Kelemen, Á.A.; Kiss, R.; Ferenczy, G.G.; Kovács, L.; Flachner, B.; Lőrincz, Z.; Keserű, G.M. Structure-based consensus scoring scheme for selecting class A aminergic GPCR fragments. J. Chem. Inf. Model., 2016, 56(2), 412-422.
[http://dx.doi.org/10.1021/acs.jcim.5b00598] [PMID: 26760056]
[33]
Onawole, A.T.; Kolapo, T.U.; Sulaiman, K.O.; Adegoke, R.O. Structure based virtual screening of the Ebola virus trimeric glycoprotein using consensus scoring. Comput. Biol. Chem., 2018, 72, 170-180.
[http://dx.doi.org/10.1016/j.compbiolchem.2017.11.006] [PMID: 29361403]
[34]
Granchi, C.; Caligiuri, I.; Bertelli, E.; Poli, G.; Rizzolio, F.; Macchia, M.; Martinelli, A.; Minutolo, F.; Tuccinardi, T. Development of terphenyl-2-methyloxazol-5(4H)-one derivatives as selective reversible MAGL inhibitors. J. Enzyme Inhib. Med. Chem., 2017, 32(1), 1240-1252.
[http://dx.doi.org/10.1080/14756366.2017.1375484] [PMID: 28936880]
[35]
Poli, G.; Lapillo, M.; Granchi, C.; Caciolla, J.; Mouawad, N.; Caligiuri, I.; Rizzolio, F.; Langer, T.; Minutolo, F.; Tuccinardi, T. Binding investigation and preliminary optimisation of the 3-amino-1,2,4-triazin-5(2H)-one core for the development of new Fyn inhibitors. J. Enzyme Inhib. Med. Chem., 2018, 33(1), 956-961.
[http://dx.doi.org/10.1080/14756366.2018.1469017] [PMID: 29747534]
[36]
Pini, E.; Poli, G.; Tuccinardi, T.; Chiarelli, L.R.; Mori, M.; Gelain, A.; Costantino, L.; Villa, S.; Meneghetti, F.; Barlocco, D. New chromane-based derivatives as inhibitors of Mycobacterium tuberculosis salicylate synthase (MbtI): Preliminary biological evaluation and molecular modeling studies. Molecules, 2018, 23(7), 1506.
[http://dx.doi.org/10.3390/molecules23071506] [PMID: 29933627]
[37]
Paul, N.; Rognan, D. ConsDock: A new program for the consensus analysis of protein-ligand interactions. Proteins, 2002, 47(4), 521-533.
[http://dx.doi.org/10.1002/prot.10119] [PMID: 12001231]
[38]
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]
[39]
Rarey, M.; Kramer, B.; Lengauer, T.; Klebe, G. A fast flexible docking method using an incremental construction algorithm. J. Mol. Biol., 1996, 261(3), 470-489.
[http://dx.doi.org/10.1006/jmbi.1996.0477] [PMID: 8780787]
[40]
Diller, D.J.; Merz, K.M. High throughput docking for library design and library prioritization. Proteins, 2001, 43(2), 113-124.
[http://dx.doi.org/10.1002/1097-0134(20010501)43:2<113:AID-PROT1023>3.0.CO;2-T] [PMID: 11276081]
[41]
Plewczynski, D.; Łaźniewski, M.; von Grotthuss, M.; Rychlewski, L.; Ginalski, K. VoteDock: consensus docking method for prediction of protein-ligand interactions. J. Comput. Chem., 2011, 32(4), 568-581.
[http://dx.doi.org/10.1002/jcc.21642] [PMID: 20812324]
[42]
Wang, R.; Fang, X.; Lu, Y.; Wang, S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J. Med. Chem., 2004, 47(12), 2977-2980.
[http://dx.doi.org/10.1021/jm030580l] [PMID: 15163179]
[43]
Wang, R.; Lu, Y.; Fang, X.; Wang, S. An extensive test of 14 scoring functions using the PDBbind refined set of 800 protein-ligand complexes. J. Chem. Inf. Comput. Sci., 2004, 44(6), 2114-2125.
[http://dx.doi.org/10.1021/ci049733j] [PMID: 15554682]
[44]
Wang, R.; Fang, X.; Lu, Y.; Yang, C-Y.; Wang, S. The PDBbind database: methodologies and updates. J. Med. Chem., 2005, 48(12), 4111-4119.
[http://dx.doi.org/10.1021/jm048957q] [PMID: 15943484]
[45]
Houston, D.R.; Walkinshaw, M.D. Consensus docking: improving the reliability of docking in a virtual screening context. J. Chem. Inf. Model., 2013, 53(2), 384-390.
[http://dx.doi.org/10.1021/ci300399w] [PMID: 23351099]
[46]
Huang, N.; Shoichet, B.K.; Irwin, J.J. Benchmarking sets for molecular docking. J. Med. Chem., 2006, 49(23), 6789-6801.
[http://dx.doi.org/10.1021/jm0608356] [PMID: 17154509]
[47]
Tuccinardi, T.; Poli, G.; Romboli, V.; Giordano, A.; Martinelli, A. Extensive consensus docking evaluation for ligand pose prediction and virtual screening studies. J. Chem. Inf. Model., 2014, 54(10), 2980-2986.
[http://dx.doi.org/10.1021/ci500424n] [PMID: 25211541]
[48]
Poli, G.; Martinelli, A.; Tuccinardi, T. Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies. J. Enzyme Inhib. Med. Chem., 2016, 31(sup2), 167-173.
[http://dx.doi.org/10.1080/14756366.2016.1193736 ] [PMID: 27311630]
[49]
Arciniega, M.; Lange, O.F. Improvement of virtual screening results by docking data feature analysis. J. Chem. Inf. Model., 2014, 54(5), 1401-1411.
[http://dx.doi.org/10.1021/ci500028u] [PMID: 24796936]
[50]
Pandini, A.; Fraccalvieri, D.; Bonati, L. Artificial neural networks for efficient clustering of conformational ensembles and their potential for medicinal chemistry. Curr. Top. Med. Chem., 2013, 13(5), 642-651.
[http://dx.doi.org/10.2174/1568026611313050007] [PMID: 23548025]
[51]
Xing, J.; Li, Q.; Zhang, S.; Liu, H.; Zhao, L.; Cheng, H.; Zhang, Y.; Zhou, J.; Zhang, H. Identification of dipeptidyl peptidase IV inhibitors: virtual screening, synthesis and biological evaluation. Chem. Biol. Drug Des., 2014, 84(3), 364-377.
[http://dx.doi.org/10.1111/cbdd.12327] [PMID: 24674599]
[52]
Granchi, C.; Capecchi, A.; Del Frate, G.; Martinelli, A.; Macchia, M.; Minutolo, F.; Tuccinardi, T. Development and validation of a docking-based virtual screening platform for the identification of new lactate dehydrogenase inhibitors. Molecules, 2015, 20(5), 8772-8790.
[http://dx.doi.org/10.3390/molecules20058772] [PMID: 25988609]
[53]
Klingler, F.M.; Moser, D.; Büttner, D.; Wichelhaus, T.A.; Löhr, F.; Dötsch, V.; Proschak, E. Probing metallo-β-lactamases with molecular fragments identified by consensus docking. Bioorg. Med. Chem. Lett., 2015, 25(22), 5243-5246.
[http://dx.doi.org/10.1016/j.bmcl.2015.09.056] [PMID: 26463134]
[54]
Congreve, M.; Carr, R.; Murray, C.; Jhoti, H. A ‘rule of three’ for fragment-based lead discovery? Drug Discov. Today, 2003, 8(19), 876-877.
[http://dx.doi.org/10.1016/S1359-6446(03)02831-9] [PMID: 14554012]
[55]
Xing, J.; Yang, L.; Li, H.; Li, Q.; Zhao, L.; Wang, X.; Zhang, Y.; Zhou, M.; Zhou, J.; Zhang, H. Identification of anthranilamide derivatives as potential factor Xa inhibitors: drug design, synthesis and biological evaluation. Eur. J. Med. Chem., 2015, 95, 388-399.
[http://dx.doi.org/10.1016/j.ejmech.2015.03.052] [PMID: 25839438]
[56]
Poli, G.; Giuntini, N.; Martinelli, A.; Tuccinardi, T. Application of a FLAP-consensus docking mixed strategy for the identification of new fatty acid amide hydrolase inhibitors. J. Chem. Inf. Model., 2015, 55(3), 667-675.
[http://dx.doi.org/10.1021/ci5006806] [PMID: 25746133]
[57]
Poli, G.; Gelain, A.; Porta, F.; Asai, A.; Martinelli, A.; Tuccinardi, T. Identification of a new STAT3 dimerization inhibitor through a pharmacophore-based virtual screening approach. J. Enzyme Inhib. Med. Chem., 2016, 31(6), 1011-1017.
[http://dx.doi.org/10.3109/14756366.2015.1079184] [PMID: 26308397]
[58]
Poli, G.; Scarpino, A.; Aissaoui, M.; Granchi, C.; Minutolo, F.; Martinelli, A.; Tuccinardi, T. Identification of lactate dehydrogenase 5 inhibitors using pharmacophore- driven consensus docking. Curr. Bioact. Compd., 2018, 14(2), 197-204.
[http://dx.doi.org/10.2174/1573407213666170208102317]
[59]
Tuccinardi, T.; Poli, G.; Corchia, I.; Granchi, C.; Lapillo, M.; Macchia, M.; Minutolo, F.; Ortore, G.; Martinelli, A. A Virtual screening study for lactate dehydrogenase 5 inhibitors by using a pharmacophore-based approach. Mol. Inform., 2016, 35(8-9), 434-439.
[http://dx.doi.org/10.1002/minf.201501026] [PMID: 27546047]
[60]
Chiarelli, L.R.; Mori, M.; Barlocco, D.; Beretta, G.; Gelain, A.; Pini, E.; Porcino, M.; Mori, G.; Stelitano, G.; Costantino, L.; Lapillo, M.; Bonanni, D.; Poli, G.; Tuccinardi, T.; Villa, S.; Meneghetti, F. Discovery and development of novel salicylate synthase (MbtI) furanic inhibitors as antitubercular agents. Eur. J. Med. Chem., 2018, 155, 754-763.
[http://dx.doi.org/10.1016/j.ejmech.2018.06.033] [PMID: 29940465]

Rights & Permissions Print Cite
Article Metrics
12
2
© 2024 Bentham Science Publishers | Privacy Policy