Literature DB >> 27709317

Docking-undocking combination applied to the D3R Grand Challenge 2015.

Sergio Ruiz-Carmona1, Xavier Barril2,3.   

Abstract

Novel methods for drug discovery are constantly under development and independent exercises to test and validate them for different goals are extremely useful. The drug discovery data resource (D3R) Grand Challenge 2015 offers an excellent opportunity as an external assessment and validation experiment for Computer-Aided Drug Discovery methods. The challenge comprises two protein targets and prediction tests: binding mode and ligand ranking. We have faced both of them with the same strategy: pharmacophore-guided docking followed by dynamic undocking (a new method tested experimentally here) and, where possible, critical assessment of the results based on pre-existing information. In spite of using methods that are qualitative in nature, our results for binding mode and ligand ranking were amongst the best on Hsp90. Results for MAP4K4 were less positive and we track the different performance across systems to the level of previous knowledge about accessible conformational states. We conclude that docking is quite effective if supplemented by dynamic undocking and empirical information (e.g. binding hot spots, productive protein conformations). This setup is well suited for virtual screening, a frequent application that was not explicitly tested in this edition of the D3R Grand Challenge 2015. Protein flexibility remains as the main cause for hard failures.

Entities:  

Keywords:  D3R; Docking; Drug discovery data resource; Dynamic; GC2015; Grand Challenge; Protein flexibility; Undocking

Mesh:

Substances:

Year:  2016        PMID: 27709317     DOI: 10.1007/s10822-016-9979-z

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  42 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Structure-activity relationships in purine-based inhibitor binding to HSP90 isoforms.

Authors:  Lisa Wright; Xavier Barril; Brian Dymock; Louisa Sheridan; Allan Surgenor; Mandy Beswick; Martin Drysdale; Adam Collier; Andy Massey; Nick Davies; Alex Fink; Christophe Fromont; Wynne Aherne; Kathy Boxall; Swee Sharp; Paul Workman; Roderick E Hubbard
Journal:  Chem Biol       Date:  2004-06

Review 3.  Virtual screening in structure-based drug discovery.

Authors:  X Barril; R E Hubbard; S D Morley
Journal:  Mini Rev Med Chem       Date:  2004-09       Impact factor: 3.862

4.  Molecular simulation methods in drug discovery: a prospective outlook.

Authors:  Xavier Barril; F Javier Luque
Journal:  J Comput Aided Mol Des       Date:  2011-12-08       Impact factor: 3.686

5.  Accurate Binding Free Energy Predictions in Fragment Optimization.

Authors:  Thomas B Steinbrecher; Markus Dahlgren; Daniel Cappel; Teng Lin; Lingle Wang; Goran Krilov; Robert Abel; Richard Friesner; Woody Sherman
Journal:  J Chem Inf Model       Date:  2015-10-21       Impact factor: 4.956

6.  Assessing predictions of protein-protein interaction: the CAPRI experiment.

Authors:  Joël Janin
Journal:  Protein Sci       Date:  2005-02       Impact factor: 6.725

7.  Predicting absolute ligand binding free energies to a simple model site.

Authors:  David L Mobley; Alan P Graves; John D Chodera; Andrea C McReynolds; Brian K Shoichet; Ken A Dill
Journal:  J Mol Biol       Date:  2007-06-08       Impact factor: 5.469

8.  Incorporating protein flexibility into docking and structure-based drug design.

Authors:  Xavier Barril; Xavier Fradera
Journal:  Expert Opin Drug Discov       Date:  2006-09       Impact factor: 6.098

Review 9.  The SAMPL4 host-guest blind prediction challenge: an overview.

Authors:  Hari S Muddana; Andrew T Fenley; David L Mobley; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2014-03-06       Impact factor: 3.686

10.  Incorporation of protein flexibility and conformational energy penalties in docking screens to improve ligand discovery.

Authors:  Marcus Fischer; Ryan G Coleman; James S Fraser; Brian K Shoichet
Journal:  Nat Chem       Date:  2014-05-25       Impact factor: 24.427

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  2 in total

1.  Docking of small molecules to farnesoid X receptors using AutoDock Vina with the Convex-PL potential: lessons learned from D3R Grand Challenge 2.

Authors:  Maria Kadukova; Sergei Grudinin
Journal:  J Comput Aided Mol Des       Date:  2017-09-14       Impact factor: 3.686

2.  Development of an Automatic Pipeline for Participation in the CELPP Challenge.

Authors:  Marina Miñarro-Lleonar; Sergio Ruiz-Carmona; Daniel Alvarez-Garcia; Peter Schmidtke; Xavier Barril
Journal:  Int J Mol Sci       Date:  2022-04-26       Impact factor: 6.208

  2 in total

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