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Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1762))

Abstract

Docking simulations help us understand molecular interactions. Here we present a hands-on tutorial to utilize FlexAID (Flexible Artificial Intelligence Docking), an open source molecular docking software between ligands such as small molecules or peptides and macromolecules such as proteins and nucleic acids. The tutorial uses the NRGsuite PyMOL plugin graphical user interface to set up and visualize docking simulations in real time as well as detect and refine target cavities. The ease of use of FlexAID and the NRGsuite combined with its superior performance relative to widely used docking software provides nonexperts with an important tool to understand molecular interactions with direct applications in structure-based drug design and virtual high-throughput screening.

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References

  1. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H et al (2000) The Protein Data Bank. Nucleic Acids Res 28:235–242

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Najmanovich RJ (2017) Evolutionary studies of ligand binding sites in proteins. Curr Opin Struct Biol 45:85–90. https://doi.org/10.1016/j.sbi.2016.11.024

    Article  CAS  PubMed  Google Scholar 

  3. Gohlke H, Klebe G (2002) Approaches to the description and prediction of the binding affinity of small-molecule ligands to macromolecular receptors. Angew Chem Int Ed 41:2645–2676

    Article  Google Scholar 

  4. Meng X-Y, Zhang H-X, Mezei M, Cui M (2011) Molecular docking: a powerful approach for structure-based drug discovery. CAD 7:146–157. https://doi.org/10.2174/157340911795677602

    Article  CAS  Google Scholar 

  5. Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov 3:935–949. https://doi.org/10.1038/nrd1549

    Article  CAS  PubMed  Google Scholar 

  6. Jacob RB, Andersen T, McDougal OM (2012) Accessible high-throughput virtual screening molecular docking software for students and educators. PLoS Comput Biol 8:e1002499. https://doi.org/10.1371/journal.pcbi.1002499

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  7. Neudert G, Klebe G (2011) DSX: a knowledge-based scoring function for the assessment of protein-ligand complexes. J Chem Inf Model 51:2731–2745. https://doi.org/10.1021/ci200274q

    Article  CAS  PubMed  Google Scholar 

  8. Li Y, Liu Z, Li J, Han L, Liu J, Zhao Z et al (2014) Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set. J Chem Inf Model 54:1700–1716. https://doi.org/10.1021/ci500080q

    Article  CAS  PubMed  Google Scholar 

  9. Englebienne P, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 4. Are popular scoring functions accurate for this class of proteins? J Chem Inf Model 49:1568–1580. https://doi.org/10.1021/ci8004308

    Article  CAS  PubMed  Google Scholar 

  10. Grudinin S, Kadukova M, Eisenbarth A, Marillet S, Cazals F (2016) Predicting binding poses and affinities for protein–ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. J Comput Aided Mol Des 30(9):791–804. https://doi.org/10.1007/s10822-016-9976-2

    Article  CAS  PubMed  Google Scholar 

  11. Mobley DL, Dill KA (2009) Binding of small-molecule ligands to proteins: “what you see” is not always “what you get”. Structure 17:489–498. https://doi.org/10.1016/j.str.2009.02.010

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Wong SE, Lightstone FC (2011) Accounting for water molecules in drug design. Expert Opin Drug Discov 6:65–74. https://doi.org/10.1517/17460441.2011.534452

    Article  CAS  PubMed  Google Scholar 

  13. Corbeil CR, Moitessier N (2009) Docking ligands into flexible and solvated macromolecules. 3. Impact of input ligand conformation, protein flexibility, and water molecules on the accuracy of docking programs. J Chem Inf Model 49:997–1009. https://doi.org/10.1021/ci8004176

    Article  CAS  PubMed  Google Scholar 

  14. Limongelli V, Marinelli L, Cosconati S, La Motta C, Sartini S, Mugnaini L et al (2012) Sampling protein motion and solvent effect during ligand binding. Proc Natl Acad Sci U S A 109:1467–1472. https://doi.org/10.1073/pnas.1112181108

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Therrien E, Weill N, Tomberg A, Corbeil CR, Lee D, Moitessier N (2014) Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy. J Chem Inf Model 54:3198–3210. https://doi.org/10.1021/ci500299h

    Article  CAS  PubMed  Google Scholar 

  16. Zhao S, Goodsell DS, Olson AJ (2001) Analysis of a data set of paired uncomplexed protein structures: new metrics for side-chain flexibility and model evaluation. Proteins Struct Funct Genet 43:271–279

    Article  CAS  PubMed  Google Scholar 

  17. Boehr DD, Nussinov R, Wright PE (2009) The role of dynamic conformational ensembles in biomolecular recognition. Nat Chem Biol 5:789–796. https://doi.org/10.1038/nchembio.232

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Najmanovich RJ, Kuttner J, Sobolev V, Edelman M (2000) Side-chain flexibility in proteins upon ligand binding. Proteins Struct Funct Genet 39:261–268

    Article  CAS  PubMed  Google Scholar 

  19. Gaudreault F, Chartier M, Najmanovich RJ (2012) Side-chain rotamer changes upon ligand binding: common, crucial, correlate with entropy and rearrange hydrogen bonding. Bioinformatics 28:i423–i430. https://doi.org/10.1093/bioinformatics/bts395

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  20. Gaudreault F, Najmanovich RJ (2015) FlexAID: revisiting docking on non-native-complex structures. J Chem Inf Model 55:1323–1336. https://doi.org/10.1021/acs.jcim.5b00078

    Article  CAS  PubMed  Google Scholar 

  21. Frappier V, Najmanovich RJ (2014) A coarse-grained elastic network atom contact model and its use in the simulation of protein dynamics and the prediction of the effect of mutations. PLoS Comput Biol 10:e1003569. https://doi.org/10.1371/journal.pcbi.1003569

    Article  PubMed Central  PubMed  Google Scholar 

  22. Wang R, Fang X, Lu Y, Yang C-Y, Wang S (2005) The PDBbind database: methodologies and updates. J Med Chem 48:4111–4119. https://doi.org/10.1021/jm048957q

    Article  CAS  PubMed  Google Scholar 

  23. Trott O, Olson AJ (2010) AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461. https://doi.org/10.1002/jcc.21334

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Rarey M, Kramer B, Lengauer T, Klebe G (1996) A fast flexible docking method using an incremental construction algorithm. J Mol Biol 261:470–489. https://doi.org/10.1006/jmbi.1996.0477

    Article  CAS  PubMed  Google Scholar 

  25. Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, Schmidtke P et al (2014) rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. PLoS Comput Biol 10(4):e1003571. https://doi.org/10.1371/journal.pcbi.1003571

    Article  PubMed Central  PubMed  Google Scholar 

  26. Gaudreault F, Morency L-P, Najmanovich RJ (2015) NRGsuite: a PyMOL plugin to perform docking simulations in real time using FlexAID. Bioinformatics 31:3856–3858. https://doi.org/10.1093/bioinformatics/btv458

    CAS  PubMed Central  PubMed  Google Scholar 

  27. Letourneau D, Lorin A, Lefebvre A, Frappier V, Gaudreault F, Najmanovich R et al (2012) StAR-related lipid transfer domain protein 5 binds primary bile acids. J Lipid Res 53(12):2677–2689. https://doi.org/10.1194/jlr.M031245

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  28. Duchêne D, Colombo E, Désilets A, Boudreault P-L, Leduc R, Marsault E et al (2014) Analysis of subpocket selectivity and identification of potent selective inhibitors for matriptase and matriptase-2. J Med Chem 57:10198–10204. https://doi.org/10.1021/jm5015633

    Article  PubMed  Google Scholar 

  29. Chartier M, Morency L-P, Zylber MI, Najmanovich RJ (2017) Large-scale detection of drug off-targets: hypotheses for drug repurposing and understanding side-effects. BMC Pharmacol Toxicol 18:1046. https://doi.org/10.1186/s40360-017-0128-7

    Article  Google Scholar 

  30. Seto JT, Rott R (1966) Functional significance of sialidose during influenza virus multiplication. Virology 30:731–737

    Article  CAS  PubMed  Google Scholar 

  31. Moscona A (2005) Neuraminidase inhibitors for influenza. N Engl J Med 353:1363–1373. https://doi.org/10.1056/NEJMra050740

    Article  CAS  PubMed  Google Scholar 

  32. Varghese JN, Laver WG, Colman PM (1983) Structure of the influenza virus glycoprotein antigen neuraminidase at 2.9 Å resolution. Nature 303:35–40. https://doi.org/10.1038/303035a0

    Article  CAS  PubMed  Google Scholar 

  33. Itzstein von M, Wu WY, Kok GB, Pegg MS, Dyason JC, Jin B et al (1993) Rational design of potent sialidase-based inhibitors of influenza virus replication. Nature 363:418–423. https://doi.org/10.1038/363418a0

    Article  Google Scholar 

  34. Talele T, Khedkar S, Rigby A (2010) Successful applications of computer aided drug discovery: moving drugs from concept to the clinic. Curr Top Med Chem 10:127–141. https://doi.org/10.2174/156802610790232251

    Article  CAS  PubMed  Google Scholar 

  35. O’Boyle NM (2012) Towards a universal SMILES representation – a standard method to generate canonical SMILES based on the InChI. J Chem 4:22. https://doi.org/10.1186/1758-2946-4-22

    Article  Google Scholar 

  36. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230. https://doi.org/10.1093/nar/gkt1223

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

R.J.N. is part of PROTEO (the Québec network for research on protein function, structure, and engineering) and GRASP (Groupe de Recherche Axé sur la Structure des Protéines). The authors would like to thank the users of FlexAID and the NRGsuite for numerous bug reports and feedbacks, thus contributing to their development, and Florence Min for critical reading of the manuscript.

Funding: L.P.M. is the recipient of a Ph.D. fellowship from the Fonds de Recherche du Québec—Nature et Technologies (FRQ-NT).

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Correspondence to Rafael Najmanovich .

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Morency, LP., Gaudreault, F., Najmanovich, R. (2018). Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design. In: Gore, M., Jagtap, U. (eds) Computational Drug Discovery and Design. Methods in Molecular Biology, vol 1762. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7756-7_18

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  • DOI: https://doi.org/10.1007/978-1-4939-7756-7_18

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