Literature DB >> 27549813

Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015.

Diogo Santos-Martins1.   

Abstract

Here is reported the development of a novel scoring function that performs remarkably well at identifying the native binding pose of a subset of HSP90 inhibitors containing aminopyrimidine or resorcinol based scaffolds. This scoring function is called PocketScore, and consists of the interaction energy between a ligand and three residues in the binding pocket: Asp93, Thr184 and a water molecule. We integrated PocketScore into a molecular docking workflow, and used it to participate in the Drug Design Data Resource (D3R) Grand Challenge 2015 (GC2015). PocketScore was able to rank 180 molecules of the GC2015 according to their binding affinity with satisfactory performance. These results indicate that the specific residues considered by PocketScore are determinant to properly model the interaction between HSP90 and its subset of inhibitors containing aminopyrimidine or resorcinol based scaffolds. Moreover, the development of PocketScore aimed at improving docking power while neglecting the prediction of binding affinities, suggesting that accurate identification of native binding poses is a determinant factor for the performance of virtual screens.

Entities:  

Keywords:  Docking power; Drug Design Data Resource; Grand Challenge 2015; HSP90; Molecular docking; Virtual screening

Mesh:

Substances:

Year:  2016        PMID: 27549813     DOI: 10.1007/s10822-016-9943-y

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


  27 in total

1.  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

2.  Rapid context-dependent ligand desolvation in molecular docking.

Authors:  Michael M Mysinger; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2010-09-27       Impact factor: 4.956

3.  Comparative assessment of scoring functions on a diverse test set.

Authors:  Tiejun Cheng; Xun Li; Yan Li; Zhihai Liu; Renxiao Wang
Journal:  J Chem Inf Model       Date:  2009-04       Impact factor: 4.956

4.  Improving performance of docking-based virtual screening by structural filtration.

Authors:  Fedor N Novikov; Viktor S Stroylov; Oleg V Stroganov; Ghermes G Chilov
Journal:  J Mol Model       Date:  2009-12-30       Impact factor: 1.810

5.  Docking and Scoring with Target-Specific Pose Classifier Succeeds in Native-Like Pose Identification But Not Binding Affinity Prediction in the CSAR 2014 Benchmark Exercise.

Authors:  Regina Politi; Marino Convertino; Konstantin Popov; Nikolay V Dokholyan; Alexander Tropsha
Journal:  J Chem Inf Model       Date:  2016-04-20       Impact factor: 4.956

6.  Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets.

Authors:  Hongjian Li; Kwong-Sak Leung; Man-Hon Wong; Pedro J Ballester
Journal:  Mol Inform       Date:  2015-02-12       Impact factor: 3.353

7.  Unveiling the full potential of flexible receptor docking using multiple crystallographic structures.

Authors:  Xavier Barril; S David Morley
Journal:  J Med Chem       Date:  2005-06-30       Impact factor: 7.446

8.  Lessons Learned over Four Benchmark Exercises from the Community Structure-Activity Resource.

Authors:  Heather A Carlson
Journal:  J Chem Inf Model       Date:  2016-06-27       Impact factor: 4.956

9.  Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking.

Authors:  Michael M Mysinger; Michael Carchia; John J Irwin; Brian K Shoichet
Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

10.  Identification, design and bio-evaluation of novel Hsp90 inhibitors by ligand-based virtual screening.

Authors:  JianMin Jia; XiaoLi Xu; Fang Liu; XiaoKe Guo; MingYe Zhang; MengChen Lu; LiLi Xu; JinLian Wei; Jia Zhu; ShengLie Zhang; ShengMiao Zhang; HaoPeng Sun; QiDong You
Journal:  PLoS One       Date:  2013-04-02       Impact factor: 3.240

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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

Review 2.  Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges.

Authors:  Isabella A Guedes; Felipe S S Pereira; Laurent E Dardenne
Journal:  Front Pharmacol       Date:  2018-09-24       Impact factor: 5.810

  2 in total

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