Literature DB >> 11151003

Design, docking, and evaluation of multiple libraries against multiple targets.

M L Lamb1, K W Burdick, S Toba, M M Young, A G Skillman, X Zou, J R Arnold, I D Kuntz.   

Abstract

We present a general approach to the design, docking, and virtual screening of multiple combinatorial libraries against a family of proteins. The method consists of three main stages: docking the scaffold, selecting the best substituents at each site of diversity, and comparing the resultant molecules within and between the libraries. The core "divide-and-conquer" algorithm for side-chain selection, developed from an earlier version (Sun et al., J Comp Aided Mol Design 1998;12:597-604), provides a way to explore large lists of substituents with linear rather than combinatorial time dependence. We have applied our method to three combinatorial libraries and three serine proteases: trypsin, chymotrypsin, and elastase. We show that the scaffold docking procedure, in conjunction with a novel vector-based orientation filter, reproduces crystallographic binding modes. In addition, the free-energy-based scoring procedure (Zou et al., J Am Chem Soc 1999;121:8033-8043) is able to reproduce experimental binding data for P1 mutants of macromolecular protease inhibitors. Finally, we show that our method discriminates between a peptide library and virtual libraries built on benzodiazepine and tetrahydroisoquinolinone scaffolds. Implications of the docking results for library design are explored.

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Year:  2001        PMID: 11151003     DOI: 10.1002/1097-0134(20010215)42:3<296::aid-prot20>3.0.co;2-f

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  10 in total

1.  Protein-protein docking with multiple residue conformations and residue substitutions.

Authors:  David M Lorber; Maria K Udo; Brian K Shoichet
Journal:  Protein Sci       Date:  2002-06       Impact factor: 6.725

2.  Surrogate docking: structure-based virtual screening at high throughput speed.

Authors:  Sukjoon Yoon; Andrew Smellie; David Hartsough; Anton Filikov
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

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

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  Protein Sci       Date:  2006-11-22       Impact factor: 6.725

Review 4.  In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling.

Authors:  S Ekins; J Mestres; B Testa
Journal:  Br J Pharmacol       Date:  2007-06-04       Impact factor: 8.739

5.  Localization of ligand binding site in proteins identified in silico.

Authors:  Michal Brylinski; Marek Kochanczyk; Elzbieta Broniatowska; Irena Roterman
Journal:  J Mol Model       Date:  2007-03-30       Impact factor: 1.810

Review 6.  What in silico molecular docking can do for the 'bench-working biologists'.

Authors:  Marius Mihăşan
Journal:  J Biosci       Date:  2012-12       Impact factor: 1.826

7.  Customizable de novo design strategies for DOCK: Application to HIVgp41 and other therapeutic targets.

Authors:  William J Allen; Brian C Fochtman; Trent E Balius; Robert C Rizzo
Journal:  J Comput Chem       Date:  2017-09-22       Impact factor: 3.376

8.  Computational analysis of binding of P1 variants to trypsin.

Authors:  B O Brandsdal; J Aqvist; A O Smalås
Journal:  Protein Sci       Date:  2001-08       Impact factor: 6.725

9.  Rapid Identification of Inhibitors and Prediction of Ligand Selectivity for Multiple Proteins: Application to Protein Kinases.

Authors:  Zhiwei Ma; Sheng-You Huang; Fei Cheng; Xiaoqin Zou
Journal:  J Phys Chem B       Date:  2021-03-02       Impact factor: 2.991

10.  Prediction of functional sites based on the fuzzy oil drop model.

Authors:  Michał Bryliński; Katarzyna Prymula; Wiktor Jurkowski; Marek Kochańczyk; Ewa Stawowczyk; Leszek Konieczny; Irena Roterman
Journal:  PLoS Comput Biol       Date:  2007-04-12       Impact factor: 4.475

  10 in total

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