Literature DB >> 19324930

FINDSITE: a combined evolution/structure-based approach to protein function prediction.

Jeffrey Skolnick1, Michal Brylinski.   

Abstract

A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the approximately 50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used.

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Year:  2009        PMID: 19324930      PMCID: PMC2691936          DOI: 10.1093/bib/bbp017

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  149 in total

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Journal:  Brief Bioinform       Date:  2008-03-15       Impact factor: 11.622

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Journal:  Nucleic Acids Res       Date:  2007-12-23       Impact factor: 16.971

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Journal:  Nucleic Acids Res       Date:  2008-05-31       Impact factor: 16.971

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  43 in total

1.  The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation.

Authors:  Mu Gao; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2012-02-21       Impact factor: 11.205

2.  PDID: database of molecular-level putative protein-drug interactions in the structural human proteome.

Authors:  Chen Wang; Gang Hu; Kui Wang; Michal Brylinski; Lei Xie; Lukasz Kurgan
Journal:  Bioinformatics       Date:  2015-10-26       Impact factor: 6.937

3.  Are protein-protein interfaces special regions on a protein's surface?

Authors:  Sam Tonddast-Navaei; Jeffrey Skolnick
Journal:  J Chem Phys       Date:  2015-12-28       Impact factor: 3.488

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

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Journal:  Proteins       Date:  2012-01-24

5.  FINDSITE-metal: integrating evolutionary information and machine learning for structure-based metal-binding site prediction at the proteome level.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proteins       Date:  2010-12-06

6.  Elastic network normal modes provide a basis for protein structure refinement.

Authors:  Pawel Gniewek; Andrzej Kolinski; Robert L Jernigan; Andrzej Kloczkowski
Journal:  J Chem Phys       Date:  2012-05-21       Impact factor: 3.488

7.  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

8.  Comparison of structure-based and threading-based approaches to protein functional annotation.

Authors:  Michal Brylinski; Jeffrey Skolnick
Journal:  Proteins       Date:  2010-01

9.  Toward prediction of functional protein pockets using blind docking and pocket search algorithms.

Authors:  Csaba Hetényi; David van der Spoel
Journal:  Protein Sci       Date:  2011-03-30       Impact factor: 6.725

10.  Pocket detection and interaction-weighted ligand-similarity search yields novel high-affinity binders for Myocilin-OLF, a protein implicated in glaucoma.

Authors:  Bharath Srinivasan; Sam Tonddast-Navaei; Jeffrey Skolnick
Journal:  Bioorg Med Chem Lett       Date:  2017-07-12       Impact factor: 2.823

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