Literature DB >> 11606123

Ligand-protein database: linking protein-ligand complex structures to binding data.

O Roche1, R Kiyama, C L Brooks.   

Abstract

In computational structure-based drug design, the scoring functions are the cornerstones to the success of design/discovery. Many approaches have been explored to improve their reliability and accuracy, leading to three families of scoring functions: force-field-based, knowledge-based, and empirical. The last family is the most widely used in association with docking algorithms because of its speed, even though such empirical scoring functions produce far too many false positives to be fully reliable. In this work, we describe a World Wide Web accessible database that gathers the structural information from known complexes of the PDB with experimental binding data. This database, the Ligand-Protein DataBase (LPDB), is designed to allow the selection of complexes based on various properties of receptors and ligands for the design and parametrization of new scoring functions or to assess and improve existing ones. Moreover, for each complex, a continuum of ligand positions ranging from the crystallographic position to points on the surface of the protein receptor allows an assessment of the energetic behavior of particular scoring functions.

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Substances:

Year:  2001        PMID: 11606123     DOI: 10.1021/jm000467k

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  35 in total

1.  Robust scoring functions for protein-ligand interactions with quantum chemical charge models.

Authors:  Jui-Chih Wang; Jung-Hsin Lin; Chung-Ming Chen; Alex L Perryman; Arthur J Olson
Journal:  J Chem Inf Model       Date:  2011-10-07       Impact factor: 4.956

2.  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

3.  Docking validation resources: protein family and ligand flexibility experiments.

Authors:  Sudipto Mukherjee; Trent E Balius; Robert C Rizzo
Journal:  J Chem Inf Model       Date:  2010-10-29       Impact factor: 4.956

4.  Calculation of absolute protein-ligand binding affinity using path and endpoint approaches.

Authors:  Michael S Lee; Mark A Olson
Journal:  Biophys J       Date:  2005-11-11       Impact factor: 4.033

5.  Development of quantitative structure-binding affinity relationship models based on novel geometrical chemical descriptors of the protein-ligand interfaces.

Authors:  Shuxing Zhang; Alexander Golbraikh; Alexander Tropsha
Journal:  J Med Chem       Date:  2006-05-04       Impact factor: 7.446

6.  Lessons for fragment library design: analysis of output from multiple screening campaigns.

Authors:  I-Jen Chen; Roderick E Hubbard
Journal:  J Comput Aided Mol Des       Date:  2009-06-03       Impact factor: 3.686

7.  Scoring confidence index: statistical evaluation of ligand binding mode predictions.

Authors:  Maria I Zavodszky; Andrew W Stumpff-Kane; David J Lee; Michael Feig
Journal:  J Comput Aided Mol Des       Date:  2009-01-20       Impact factor: 3.686

8.  Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

Authors:  Elizabeth Durham; Brent Dorr; Nils Woetzel; René Staritzbichler; Jens Meiler
Journal:  J Mol Model       Date:  2009-02-21       Impact factor: 1.810

9.  MedusaScore: an accurate force field-based scoring function for virtual drug screening.

Authors:  Shuangye Yin; Lada Biedermannova; Jiri Vondrasek; Nikolay V Dokholyan
Journal:  J Chem Inf Model       Date:  2008-08-02       Impact factor: 4.956

Review 10.  Practically useful: what the Rosetta protein modeling suite can do for you.

Authors:  Kristian W Kaufmann; Gordon H Lemmon; Samuel L Deluca; Jonathan H Sheehan; Jens Meiler
Journal:  Biochemistry       Date:  2010-04-13       Impact factor: 3.162

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