Literature DB >> 15961475

In silico identification of functional regions in proteins.

Guy Nimrod1, Fabian Glaser, David Steinberg, Nir Ben-Tal, Tal Pupko.   

Abstract

MOTIVATION: In silico prediction of functional regions on protein surfaces, i.e. sites of interaction with DNA, ligands, substrates and other proteins, is of utmost importance in various applications in the emerging fields of proteomics and structural genomics. When a sufficient number of homologs is found, powerful prediction schemes can be based on the observation that evolutionarily conserved regions are often functionally important, typically, only the principal functionally important region of the protein is detected, while secondary functional regions with weaker conservation signals are overlooked. Moreover, it is challenging to unambiguously identify the boundaries of the functional regions.
METHODS: We present a new methodology, called PatchFinder, that automatically identifies patches of conserved residues that are located in close proximity to each other on the protein surface. PatchFinder is based on the following steps: (1) Assignment of conservation scores to each amino acid position on the protein surface. (2) Assignment of a score to each putative patch, based on its likelihood to be functionally important. The patch of maximum likelihood is considered to be the main functionally important region, and the search is continued for non-overlapping patches of secondary importance.
RESULTS: We examined the accuracy of the method using the IGPS enzyme, the SH2 domain and a benchmark set of 112 proteins. These examples demonstrated that PatchFinder is capable of identifying both the main and secondary functional patches. AVAILABILITY: The PatchFinder program is available at: http://ashtoret.tau.ac.il/~nimrodg/

Mesh:

Substances:

Year:  2005        PMID: 15961475     DOI: 10.1093/bioinformatics/bti1023

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


  24 in total

1.  Association of putative concave protein-binding sites with the fluctuation behavior of residues.

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Journal:  Protein Sci       Date:  2006-10       Impact factor: 6.725

2.  Inhibition of protein-protein interactions with low molecular weight compounds.

Authors:  Marilyn M Matthews; David J Weber; Paul S Shapiro; Andrew Coop; Alexander D Mackerell
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3.  INTEGRATING COMPUTATIONAL PROTEIN FUNCTION PREDICTION INTO DRUG DISCOVERY INITIATIVES.

Authors:  Marianne A Grant
Journal:  Drug Dev Res       Date:  2011-02       Impact factor: 4.360

4.  Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction.

Authors:  Katarzyna Prymula; Tomasz Jadczyk; Irena Roterman
Journal:  J Comput Aided Mol Des       Date:  2010-11-21       Impact factor: 3.686

5.  Identification of DNA-binding proteins using structural, electrostatic and evolutionary features.

Authors:  Guy Nimrod; András Szilágyi; Christina Leslie; Nir Ben-Tal
Journal:  J Mol Biol       Date:  2009-02-20       Impact factor: 5.469

6.  DNABINDPROT: fluctuation-based predictor of DNA-binding residues within a network of interacting residues.

Authors:  Pemra Ozbek; Seren Soner; Burak Erman; Turkan Haliloglu
Journal:  Nucleic Acids Res       Date:  2010-05-16       Impact factor: 16.971

7.  Functional region prediction with a set of appropriate homologous sequences--an index for sequence selection by integrating structure and sequence information with spatial statistics.

Authors:  Wataru Nemoto; Hiroyuki Toh
Journal:  BMC Struct Biol       Date:  2012-05-29

8.  Detecting patches of protein sites of influenza A viruses under positive selection.

Authors:  Christina Tusche; Lars Steinbrück; Alice C McHardy
Journal:  Mol Biol Evol       Date:  2012-03-16       Impact factor: 16.240

9.  Using shifts in amino acid frequency and substitution rate to identify latent structural characters in base-excision repair enzymes.

Authors:  Ramiro Barrantes-Reynolds; Susan S Wallace; Jeffrey P Bond
Journal:  PLoS One       Date:  2011-10-06       Impact factor: 3.240

10.  ResBoost: characterizing and predicting catalytic residues in enzymes.

Authors:  Ron Alterovitz; Aaron Arvey; Sriram Sankararaman; Carolina Dallett; Yoav Freund; Kimmen Sjölander
Journal:  BMC Bioinformatics       Date:  2009-06-27       Impact factor: 3.169

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