Literature DB >> 11560060

Sequence- and structure-based protein function prediction from genomic information.

S M Baxter1, J S Fetrow.   

Abstract

Existing functional annotation transfer is fraught with inaccuracies that may hinder forward interpretation and mining of genomic data. Hand-curation of the annotation placed into databases is not practical. In lieu of experimental evidence, computational biological approaches offer high-throughput tools to predict function accurately; however, these methods are still notably deficient in defining and describing the complexity of protein function. Enriching genomic sequences obtained from sequencing efforts and expression array methods with protein function information and classification will be an efficient first step for incorporating genomic data into drug discovery programs.

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Year:  2001        PMID: 11560060

Source DB:  PubMed          Journal:  Curr Opin Drug Discov Devel        ISSN: 1367-6733


  7 in total

1.  Folding free energy function selects native-like protein sequences in the core but not on the surface.

Authors:  Alfonso Jaramillo; Lorenz Wernisch; Stéphanie Héry; Shoshana J Wodak
Journal:  Proc Natl Acad Sci U S A       Date:  2002-10-04       Impact factor: 11.205

2.  PANDORA: keyword-based analysis of protein sets by integration of annotation sources.

Authors:  Noam Kaplan; Avishay Vaaknin; Michal Linial
Journal:  Nucleic Acids Res       Date:  2003-10-01       Impact factor: 16.971

3.  Computational approaches to protein-protein interaction.

Authors:  Giacomo Franzot; Oliviero Carugo
Journal:  J Struct Funct Genomics       Date:  2003

4.  The protein structure prediction problem could be solved using the current PDB library.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Proc Natl Acad Sci U S A       Date:  2005-01-14       Impact factor: 11.205

5.  DNAWorks: an automated method for designing oligonucleotides for PCR-based gene synthesis.

Authors:  David M Hoover; Jacek Lubkowski
Journal:  Nucleic Acids Res       Date:  2002-05-15       Impact factor: 16.971

6.  Combining modularity, conservation, and interactions of proteins significantly increases precision and coverage of protein function prediction.

Authors:  Samira Jaeger; Christine T Sers; Ulf Leser
Journal:  BMC Genomics       Date:  2010-12-20       Impact factor: 3.969

7.  Integrating protein-protein interactions and text mining for protein function prediction.

Authors:  Samira Jaeger; Sylvain Gaudan; Ulf Leser; Dietrich Rebholz-Schuhmann
Journal:  BMC Bioinformatics       Date:  2008-07-22       Impact factor: 3.169

  7 in total

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