Literature DB >> 20000587

Recipes for the selection of experimental protein conformations for virtual screening.

Manuel Rueda1, Giovanni Bottegoni, Ruben Abagyan.   

Abstract

The use of multiple X-ray protein structures has been reported to be an efficient alternative for the representation of the binding pocket flexibility needed for accurate small molecules docking. However, the docking performance of the individual single conformations varies widely, and adding certain conformations to an ensemble is even counterproductive. Here we used a very large and diverse benchmark of 1068 X-ray protein conformations of 99 therapeutically relevant proteins, first, to compare the performance of the ensemble and single-conformation docking and, second, to find the properties of the best-performing conformers that can be used to select a smaller set of conformers for ensemble docking. The conformer selection has been validated through retrospective virtual screening experiments aimed at separating known ligand binders from decoys. We found that the conformers cocrystallized with the largest ligands displayed high selectivity for binders, and when combined in ensembles they consistently provided better results than randomly chosen protein conformations. The use of ensembles encompassing between 3 and 5 experimental conformations consistently improved the docking accuracy and binders vs decoys separation.

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Year:  2010        PMID: 20000587      PMCID: PMC2811216          DOI: 10.1021/ci9003943

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  43 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.  Information decay in molecular docking screens against holo, apo, and modeled conformations of enzymes.

Authors:  Susan L McGovern; Brian K Shoichet
Journal:  J Med Chem       Date:  2003-07-03       Impact factor: 7.446

Review 3.  Application and limitations of X-ray crystallographic data in structure-based ligand and drug design.

Authors:  Andrew M Davis; Simon J Teague; Gerard J Kleywegt
Journal:  Angew Chem Int Ed Engl       Date:  2003-06-23       Impact factor: 15.336

Review 4.  Implications of protein flexibility for drug discovery.

Authors:  Simon J Teague
Journal:  Nat Rev Drug Discov       Date:  2003-07       Impact factor: 84.694

5.  Comparative study of several algorithms for flexible ligand docking.

Authors:  Badry D Bursulaya; Maxim Totrov; Ruben Abagyan; Charles L Brooks
Journal:  J Comput Aided Mol Des       Date:  2003-11       Impact factor: 3.686

6.  Protein flexibility in ligand docking and virtual screening to protein kinases.

Authors:  Claudio N Cavasotto; Ruben A Abagyan
Journal:  J Mol Biol       Date:  2004-03-12       Impact factor: 5.469

7.  Soft docking and multiple receptor conformations in virtual screening.

Authors:  Anna Maria Ferrari; Binqing Q Wei; Luca Costantino; Brian K Shoichet
Journal:  J Med Chem       Date:  2004-10-07       Impact factor: 7.446

Review 8.  Structure-based chemogenomics: analysis of protein family landscapes.

Authors:  Bernard Pirard
Journal:  Methods Mol Biol       Date:  2009

9.  Identification of a minimal subset of receptor conformations for improved multiple conformation docking and two-step scoring.

Authors:  Sukjoon Yoon; William J Welsh
Journal:  J Chem Inf Comput Sci       Date:  2004 Jan-Feb

10.  Sensitivity of molecular docking to induced fit effects in influenza virus neuraminidase.

Authors:  Louise Birch; Christopher W Murray; Michael J Hartshorn; Ian J Tickle; Marcel L Verdonk
Journal:  J Comput Aided Mol Des       Date:  2002-12       Impact factor: 3.686

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  56 in total

1.  Molecular mechanism of serotonin transporter inhibition elucidated by a new flexible docking protocol.

Authors:  Mari Gabrielsen; Rafał Kurczab; Aina W Ravna; Irina Kufareva; Ruben Abagyan; Zdzisław Chilmonczyk; Andrzej J Bojarski; Ingebrigt Sylte
Journal:  Eur J Med Chem       Date:  2011-10-20       Impact factor: 6.514

2.  Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2012-01-15       Impact factor: 3.686

3.  Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach.

Authors:  Marco A C Neves; Sérgio Simões; M Luisa Sá e Melo
Journal:  J Comput Aided Mol Des       Date:  2010-10-20       Impact factor: 3.686

4.  Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures.

Authors:  Yan Li; Dong Joon Kim; Weiya Ma; Ronald A Lubet; Ann M Bode; Zigang Dong
Journal:  J Chem Inf Model       Date:  2011-10-12       Impact factor: 4.956

5.  Molecular motions in drug design: the coming age of the metadynamics method.

Authors:  Xevi Biarnés; Salvatore Bongarzone; Attilio Vittorio Vargiu; Paolo Carloni; Paolo Ruggerone
Journal:  J Comput Aided Mol Des       Date:  2011-02-17       Impact factor: 3.686

Review 6.  Receptor-ligand molecular docking.

Authors:  Isabella A Guedes; Camila S de Magalhães; Laurent E Dardenne
Journal:  Biophys Rev       Date:  2013-12-21

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

Authors:  Sergio Ruiz-Carmona; Xavier Barril
Journal:  J Comput Aided Mol Des       Date:  2016-10-05       Impact factor: 3.686

8.  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

9.  Enhancing Virtual Screening Performance of Protein Kinases with Molecular Dynamics Simulations.

Authors:  Tavina L Offutt; Robert V Swift; Rommie E Amaro
Journal:  J Chem Inf Model       Date:  2016-10-03       Impact factor: 4.956

10.  Structure-based predictions of activity cliffs.

Authors:  Jarmila Husby; Giovanni Bottegoni; Irina Kufareva; Ruben Abagyan; Andrea Cavalli
Journal:  J Chem Inf Model       Date:  2015-05-11       Impact factor: 4.956

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