Literature DB >> 19153135

The protein-small-molecule database, a non-redundant structural resource for the analysis of protein-ligand binding.

Izhar Wallach1, Ryan Lilien.   

Abstract

MOTIVATION: An enabling resource for drug discovery and protein function prediction is a large, accurate and actively maintained collection of protein/small-molecule complex structures. Models of binding are typically constructed from these structural libraries by generalizing the observed interaction patterns. Consequently, the quality of the model is dependent on the quality of the structural library. An ideal library should be non-biased and comprehensive, contain high-resolution structures and be actively maintained.
RESULTS: We present a new protein/small-molecule database (the PSMDB) that offers a non-redundant set of holo PDB complexes. The database was designed to allow frequent updates through a fully automated process without manual annotation or filtering. Our method of database construction addresses redundancy at both the protein and the small-molecule level. By efficiently handling structures with covalently bound ligands, we allow our database to include a larger number of structures than previous methods. Multiple versions of the database are available at our web site, including structures of split complexes--the proteins without their binding ligands and the non-covalently bound ligands within their native coordinate frame. AVAILABILITY: http://compbio.cs.toronto.edu/psmdb

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Year:  2009        PMID: 19153135     DOI: 10.1093/bioinformatics/btp035

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  15 in total

1.  Structure- and sequence-based function prediction for non-homologous proteins.

Authors:  Lee Sael; Meghana Chitale; Daisuke Kihara
Journal:  J Struct Funct Genomics       Date:  2012-01-22

2.  Detecting local ligand-binding site similarity in nonhomologous proteins by surface patch comparison.

Authors:  Lee Sael; Daisuke Kihara
Journal:  Proteins       Date:  2012-01-24

3.  eFindSite: improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands.

Authors:  Michal Brylinski; Wei P Feinstein
Journal:  J Comput Aided Mol Des       Date:  2013-07-10       Impact factor: 3.686

4.  Large-scale binding ligand prediction by improved patch-based method Patch-Surfer2.0.

Authors:  Xiaolei Zhu; Yi Xiong; Daisuke Kihara
Journal:  Bioinformatics       Date:  2014-10-29       Impact factor: 6.937

5.  PatchSurfers: Two methods for local molecular property-based binding ligand prediction.

Authors:  Woong-Hee Shin; Mark Gregory Bures; Daisuke Kihara
Journal:  Methods       Date:  2015-09-30       Impact factor: 3.608

6.  Determination of ligand binding modes in weak protein-ligand complexes using sparse NMR data.

Authors:  Biswaranjan Mohanty; Martin L Williams; Bradley C Doak; Mansha Vazirani; Olga Ilyichova; Geqing Wang; Wolfgang Bermel; Jamie S Simpson; David K Chalmers; Glenn F King; Mehdi Mobli; Martin J Scanlon
Journal:  J Biomol NMR       Date:  2016-10-24       Impact factor: 2.835

7.  The utility of geometrical and chemical restraint information extracted from predicted ligand-binding sites in protein structure refinement.

Authors:  Michal Brylinski; Seung Yup Lee; Hongyi Zhou; Jeffrey Skolnick
Journal:  J Struct Biol       Date:  2010-09-17       Impact factor: 2.867

8.  Unleashing the power of meta-threading for evolution/structure-based function inference of proteins.

Authors:  Michal Brylinski
Journal:  Front Genet       Date:  2013-06-19       Impact factor: 4.599

9.  Open Babel: An open chemical toolbox.

Authors:  Noel M O'Boyle; Michael Banck; Craig A James; Chris Morley; Tim Vandermeersch; Geoffrey R Hutchison
Journal:  J Cheminform       Date:  2011-10-07       Impact factor: 5.514

10.  Constructing patch-based ligand-binding pocket database for predicting function of proteins.

Authors:  Lee Sael; Daisuke Kihara
Journal:  BMC Bioinformatics       Date:  2012-03-13       Impact factor: 3.169

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