Literature DB >> 17123961

Efficient molecular docking of NMR structures: application to HIV-1 protease.

Sheng-You Huang1, Xiaoqin Zou.   

Abstract

Docking ligands into an ensemble of NMR conformers is essential to structure-based drug discovery if only NMR structures are available for the target. However, sequentially docking ligands into each NMR conformer through standard single-receptor-structure docking, referred to as sequential docking, is computationally expensive for large-scale database screening because of the large number of NMR conformers involved. Recently, we developed an efficient ensemble docking algorithm to consider protein structural variations in ligand binding. The algorithm simultaneously docks ligands into an ensemble of protein structures and achieves comparable performance to sequential docking without significant increase in computational time over single-structure docking. Here, we applied this algorithm to docking with NMR structures. The HIV-1 protease was used for validation in terms of docking accuracy and virtual screening. Ensemble docking of the NMR structures identified 91% of the known inhibitors under the criterion of RMSD < 2.0 A for the best-scored conformation, higher than the average success rate of single docking of individual crystal structures (66%). In the virtual screening test, on average, ensemble docking of the NMR structures obtained higher enrichments than single-structure docking of the crystal structures. In contrast, docking of either the NMR minimized average structure or a single NMR conformer performed less satisfactorily on both binding mode prediction and virtual screening, indicating that a single NMR structure may not be suitable for docking calculations. The success of ensemble docking of the NMR structures suggests an efficient alternative method for standard single docking of crystal structures and for considering protein flexibility.

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Year:  2006        PMID: 17123961      PMCID: PMC2222846          DOI: 10.1110/ps.062501507

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  26 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.  A method for including protein flexibility in protein-ligand docking: improving tools for database mining and virtual screening.

Authors:  H B Broughton
Journal:  J Mol Graph Model       Date:  2000-06       Impact factor: 2.518

Review 3.  Molecular recognition and docking algorithms.

Authors:  Natasja Brooijmans; Irwin D Kuntz
Journal:  Annu Rev Biophys Biomol Struct       Date:  2003-01-28

4.  Modeling correlated main-chain motions in proteins for flexible molecular recognition.

Authors:  Maria I Zavodszky; Ming Lei; M F Thorpe; Anthony R Day; Leslie A Kuhn
Journal:  Proteins       Date:  2004-11-01

5.  Testing a flexible-receptor docking algorithm in a model binding site.

Authors:  Binqing Q Wei; Larry H Weaver; Anna M Ferrari; Brian W Matthews; Brian K Shoichet
Journal:  J Mol Biol       Date:  2004-04-09       Impact factor: 5.469

6.  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 7.  Comparing protein-ligand docking programs is difficult.

Authors:  Jason C Cole; Christopher W Murray; J Willem M Nissink; Richard D Taylor; Robin Taylor
Journal:  Proteins       Date:  2005-08-15

8.  An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Comput Chem       Date:  2006-11-30       Impact factor: 3.376

9.  Ensemble docking of multiple protein structures: considering protein structural variations in molecular docking.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  Proteins       Date:  2007-02-01

10.  An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Comput Chem       Date:  2006-11-30       Impact factor: 3.376

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  34 in total

1.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

2.  Construction and test of ligand decoy sets using MDock: community structure-activity resource benchmarks for binding mode prediction.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-03       Impact factor: 4.956

3.  The intrinsic dynamics of enzymes plays a dominant role in determining the structural changes induced upon inhibitor binding.

Authors:  Ahmet Bakan; Ivet Bahar
Journal:  Proc Natl Acad Sci U S A       Date:  2009-08-17       Impact factor: 11.205

4.  An inverse docking approach for identifying new potential anti-cancer targets.

Authors:  Sam Z Grinter; Yayun Liang; Sheng-You Huang; Salman M Hyder; Xiaoqin Zou
Journal:  J Mol Graph Model       Date:  2011-01-19       Impact factor: 2.518

5.  Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark.

Authors:  Sam Z Grinter; Chengfei Yan; Sheng-You Huang; Lin Jiang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2013-05-21       Impact factor: 4.956

6.  Molecular recognition in the case of flexible targets.

Authors:  Anthony Ivetac; J Andrew McCammon
Journal:  Curr Pharm Des       Date:  2011       Impact factor: 3.116

7.  Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23.

Authors:  Hector A Velazquez; Demian Riccardi; Zhousheng Xiao; Leigh Darryl Quarles; Charless Ryan Yates; Jerome Baudry; Jeremy C Smith
Journal:  Chem Biol Drug Des       Date:  2017-11-03       Impact factor: 2.817

8.  Discovery of a nanomolar inhibitor of the human glyoxalase-I enzyme using structure-based poly-pharmacophore modelling and molecular docking.

Authors:  Nizar A Al-Shar'i; Qosay A Al-Balas; Rand A Al-Waqfi; Mohammad A Hassan; Amer E Alkhalifa; Nehad M Ayoub
Journal:  J Comput Aided Mol Des       Date:  2019-10-19       Impact factor: 3.686

9.  Inclusion of solvation and entropy in the knowledge-based scoring function for protein-ligand interactions.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2010-02-22       Impact factor: 4.956

10.  Improving structure-based function prediction using molecular dynamics.

Authors:  Dariya S Glazer; Randall J Radmer; Russ B Altman
Journal:  Structure       Date:  2009-07-15       Impact factor: 5.006

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