Literature DB >> 24590723

Virtual high-throughput ligand screening.

T Andrew Binkowski1, Wei Jiang, Benoit Roux, Wayne F Anderson, Andrzej Joachimiak.   

Abstract

In Structural Genomics projects, virtual high-throughput ligand screening can be utilized to provide important functional details for newly determined protein structures. Using a variety of publicly available software tools, it is possible to computationally model, predict, and evaluate how different ligands interact with a given protein. At the Center for Structural Genomics of Infectious Diseases (CSGID) a series of protein analysis, docking and molecular dynamics software is scripted into a single hierarchical pipeline allowing for an exhaustive investigation of protein-ligand interactions. The ability to conduct accurate computational predictions of protein-ligand binding is a vital component in improving both the efficiency and economics of drug discovery. Computational simulations can minimize experimental efforts, the slowest and most cost prohibitive aspect of identifying new therapeutics.

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Year:  2014        PMID: 24590723      PMCID: PMC4073479          DOI: 10.1007/978-1-4939-0354-2_19

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  30 in total

1.  Computational binding studies of human pp60c-src SH2 domain with a series of nonpeptide, phosphophenyl-containing ligands.

Authors:  D J Price; W L Jorgensen
Journal:  Bioorg Med Chem Lett       Date:  2000-09-18       Impact factor: 2.823

2.  CASTp: Computed Atlas of Surface Topography of proteins.

Authors:  T Andrew Binkowski; Shapor Naghibzadeh; Jie Liang
Journal:  Nucleic Acids Res       Date:  2003-07-01       Impact factor: 16.971

Review 3.  Virtual screening of chemical libraries.

Authors:  Brian K Shoichet
Journal:  Nature       Date:  2004-12-16       Impact factor: 49.962

4.  ZINC--a free database of commercially available compounds for virtual screening.

Authors:  John J Irwin; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2005 Jan-Feb       Impact factor: 4.956

5.  Rescoring docking hit lists for model cavity sites: predictions and experimental testing.

Authors:  Alan P Graves; Devleena M Shivakumar; Sarah E Boyce; Matthew P Jacobson; David A Case; Brian K Shoichet
Journal:  J Mol Biol       Date:  2008-01-30       Impact factor: 5.469

Review 6.  CHARMM: the biomolecular simulation program.

Authors:  B R Brooks; C L Brooks; A D Mackerell; L Nilsson; R J Petrella; B Roux; Y Won; G Archontis; C Bartels; S Boresch; A Caflisch; L Caves; Q Cui; A R Dinner; M Feig; S Fischer; J Gao; M Hodoscek; W Im; K Kuczera; T Lazaridis; J Ma; V Ovchinnikov; E Paci; R W Pastor; C B Post; J Z Pu; M Schaefer; B Tidor; R M Venable; H L Woodcock; X Wu; W Yang; D M York; M Karplus
Journal:  J Comput Chem       Date:  2009-07-30       Impact factor: 3.376

7.  Anatomy of protein pockets and cavities: measurement of binding site geometry and implications for ligand design.

Authors:  J Liang; H Edelsbrunner; C Woodward
Journal:  Protein Sci       Date:  1998-09       Impact factor: 6.725

8.  Thermodynamic stability of water molecules in the bacteriorhodopsin proton channel: a molecular dynamics free energy perturbation study.

Authors:  B Roux; M Nina; R Pomès; J C Smith
Journal:  Biophys J       Date:  1996-08       Impact factor: 4.033

9.  Analytical shape computation of macromolecules: II. Inaccessible cavities in proteins.

Authors:  J Liang; H Edelsbrunner; P Fu; P V Sudhakar; S Subramaniam
Journal:  Proteins       Date:  1998-10-01

10.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

Authors:  Garrett M Morris; Ruth Huey; William Lindstrom; Michel F Sanner; Richard K Belew; David S Goodsell; Arthur J Olson
Journal:  J Comput Chem       Date:  2009-12       Impact factor: 3.376

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  3 in total

1.  The dye SYPRO orange binds to amylin amyloid fibrils but not pre-fibrillar intermediates.

Authors:  Amy G Wong; Daniel P Raleigh
Journal:  Protein Sci       Date:  2016-08-23       Impact factor: 6.725

2.  Combined ligand and structure-based virtual screening approaches for identification of novel AChE inhibitors.

Authors:  Kader Şahİn; Serdar DurdaĞi
Journal:  Turk J Chem       Date:  2020-06-01       Impact factor: 1.239

3.  Hybrid In Silico and TR-FRET-Guided Discovery of Novel BCL-2 Inhibitors.

Authors:  Kader Sahin; Muge Didem Orhan; Timucin Avsar; Serdar Durdagi
Journal:  ACS Pharmacol Transl Sci       Date:  2021-04-15
  3 in total

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