Literature DB >> 27573981

Optimal strategies for virtual screening of induced-fit and flexible target in the 2015 D3R Grand Challenge.

Zhaofeng Ye1,2, Matthew P Baumgartner1, Bentley M Wingert1, Carlos J Camacho3.   

Abstract

Induced fit or protein flexibility can make a given structure less useful for docking and/or scoring. The 2015 Drug Design Data Resource (D3R) Grand Challenge provided a unique opportunity to prospectively test optimal strategies for virtual screening in these type of targets: heat shock protein 90 (HSP90), a protein with multiple ligand-induced binding modes; and mitogen-activated protein kinase kinase kinase kinase 4 (MAP4K4), a kinase with a large flexible pocket. Using previously known co-crystal structures, we tested predictions from methods that keep the receptor structure fixed and used (a) multiple receptor/ligand co-crystals as binding templates for minimization or docking ("close"), (b) methods that align or dock to a single receptor ("cross"), and (c) a hybrid approach that chose from multiple bound ligands as initial templates for minimization to a single receptor ("min-cross"). Pose prediction using our "close" models resulted in average ligand RMSDs of 0.32 and 1.6 Å for HSP90 and MAP4K4, respectively, the most accurate models of the community-wide challenge. On the other hand, affinity ranking using our "cross" methods performed well overall despite the fact that a fixed receptor cannot model ligand-induced structural changes,. In addition, "close" methods that leverage the co-crystals of the different binding modes of HSP90 also predicted the best affinity ranking. Our studies suggest that analysis of changes on the receptor structure upon ligand binding can help select an optimal virtual screening strategy.

Entities:  

Keywords:  Affinity ranking; D3R; Drug discovery; Induced fit; Pose prediction; Virtual screening

Mesh:

Substances:

Year:  2016        PMID: 27573981      PMCID: PMC5079819          DOI: 10.1007/s10822-016-9941-0

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


  39 in total

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Authors:  Renxiao Wang; Luhua Lai; Shaomeng Wang
Journal:  J Comput Aided Mol Des       Date:  2002-01       Impact factor: 3.686

2.  The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures.

Authors:  Renxiao Wang; Xueliang Fang; Yipin Lu; Shaomeng Wang
Journal:  J Med Chem       Date:  2004-06-03       Impact factor: 7.446

3.  Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.

Authors:  Richard A Friesner; Jay L Banks; Robert B Murphy; Thomas A Halgren; Jasna J Klicic; Daniel T Mainz; Matthew P Repasky; Eric H Knoll; Mee Shelley; Jason K Perry; David E Shaw; Perry Francis; Peter S Shenkin
Journal:  J Med Chem       Date:  2004-03-25       Impact factor: 7.446

4.  Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields.

Authors:  Paolo Tosco; Thomas Balle
Journal:  J Mol Model       Date:  2010-04-11       Impact factor: 1.810

5.  Empirical scoring functions: I. The development of a fast empirical scoring function to estimate the binding affinity of ligands in receptor complexes.

Authors:  M D Eldridge; C W Murray; T R Auton; G V Paolini; R P Mee
Journal:  J Comput Aided Mol Des       Date:  1997-09       Impact factor: 3.686

6.  The development of a simple empirical scoring function to estimate the binding constant for a protein-ligand complex of known three-dimensional structure.

Authors:  H J Böhm
Journal:  J Comput Aided Mol Des       Date:  1994-06       Impact factor: 3.686

7.  Structure-Based Design of GNE-495, a Potent and Selective MAP4K4 Inhibitor with Efficacy in Retinal Angiogenesis.

Authors:  Chudi O Ndubaku; Terry D Crawford; Huifen Chen; Jason W Boggs; Joy Drobnick; Seth F Harris; Rajiv Jesudason; Erin McNamara; Jim Nonomiya; Amy Sambrone; Stephen Schmidt; Tanya Smyczek; Philip Vitorino; Lan Wang; Ping Wu; Stacey Yeung; Jinhua Chen; Kevin Chen; Charles Z Ding; Tao Wang; Zijin Xu; Stephen E Gould; Lesley J Murray; Weilan Ye
Journal:  ACS Med Chem Lett       Date:  2015-06-29       Impact factor: 4.345

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

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Journal:  J Med Chem       Date:  2012-07-05       Impact factor: 7.446

9.  CSAR benchmark exercise of 2010: combined evaluation across all submitted scoring functions.

Authors:  Richard D Smith; James B Dunbar; Peter Man-Un Ung; Emilio X Esposito; Chao-Yie Yang; Shaomeng Wang; Heather A Carlson
Journal:  J Chem Inf Model       Date:  2011-08-29       Impact factor: 4.956

10.  PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure.

Authors:  David Ryan Koes; Carlos J Camacho
Journal:  Nucleic Acids Res       Date:  2012-04-20       Impact factor: 16.971

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  13 in total

1.  A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor.

Authors:  Maria E Mavrogeni; Filippos Pronios; Danae Zareifi; Sofia Vasilakaki; Olivier Lozach; Leonidas Alexopoulos; Laurent Meijer; Vassilios Myrianthopoulos; Emmanuel Mikros
Journal:  Future Med Chem       Date:  2018-10-16       Impact factor: 3.808

2.  Optimal affinity ranking for automated virtual screening validated in prospective D3R grand challenges.

Authors:  Bentley M Wingert; Rick Oerlemans; Carlos J Camacho
Journal:  J Comput Aided Mol Des       Date:  2017-09-16       Impact factor: 3.686

3.  Improved pose and affinity predictions using different protocols tailored on the basis of data availability.

Authors:  Philip Prathipati; Chioko Nagao; Shandar Ahmad; Kenji Mizuguchi
Journal:  J Comput Aided Mol Des       Date:  2016-10-06       Impact factor: 3.686

4.  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

5.  Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2.

Authors:  Matthew P Baumgartner; David A Evans
Journal:  J Comput Aided Mol Des       Date:  2017-11-10       Impact factor: 3.686

6.  A cross docking pipeline for improving pose prediction and virtual screening performance.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2017-08-23       Impact factor: 3.686

7.  Cross-docking benchmark for automated pose and ranking prediction of ligand binding.

Authors:  Shayne D Wierbowski; Bentley M Wingert; Jim Zheng; Carlos J Camacho
Journal:  Protein Sci       Date:  2019-11-28       Impact factor: 6.725

Review 8.  Improving small molecule virtual screening strategies for the next generation of therapeutics.

Authors:  Bentley M Wingert; Carlos J Camacho
Journal:  Curr Opin Chem Biol       Date:  2018-06-17       Impact factor: 8.822

9.  Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges.

Authors:  Duc Duy Nguyen; Zixuan Cang; Kedi Wu; Menglun Wang; Yin Cao; Guo-Wei Wei
Journal:  J Comput Aided Mol Des       Date:  2018-08-16       Impact factor: 3.686

10.  Defining the Kv2.1-syntaxin molecular interaction identifies a first-in-class small molecule neuroprotectant.

Authors:  Chung-Yang Yeh; Zhaofeng Ye; Aubin Moutal; Shivani Gaur; Amanda M Henton; Stylianos Kouvaros; Jami L Saloman; Karen A Hartnett-Scott; Thanos Tzounopoulos; Rajesh Khanna; Elias Aizenman; Carlos J Camacho
Journal:  Proc Natl Acad Sci U S A       Date:  2019-07-15       Impact factor: 11.205

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