Literature DB >> 15922590

Automatic annotation of protein function.

Alfonso Valencia1.   

Abstract

The annotation of protein function at genomic scale is essential for day-to-day work in biology and for any systematic approach to the modeling of biological systems. Currently, functional annotation is essentially based on the expansion of the relatively small number of experimentally determined functions to large collections of proteins. The task of systematic annotation faces formidable practical problems related to the accuracy of the input experimental information, the reliability of current systems for transferring information between related sequences, and the reproducibility of the links between database information and the original experiments reported in publications. These technical difficulties merely lie on the surface of the deeper problem of the evolution of protein function in the context of protein sequences and structures. Given the mixture of technical and scientific challenges, it is not surprising that errors are introduced, and expanded, in database annotations. In this situation, a more realistic option is the development of a reliability index for database annotations, instead of depending exclusively on efforts to correct databases. Several groups have attempted to compare the database annotations of similar proteins, which constitutes the first steps toward the calibration of the relationship between sequence and annotation space.

Mesh:

Substances:

Year:  2005        PMID: 15922590     DOI: 10.1016/j.sbi.2005.05.010

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  42 in total

1.  Real-time ligand binding pocket database search using local surface descriptors.

Authors:  Rayan Chikhi; Lee Sael; Daisuke Kihara
Journal:  Proteins       Date:  2010-07

2.  ETAscape: analyzing protein networks to predict enzymatic function and substrates in Cytoscape.

Authors:  Benjamin J Bachman; Eric Venner; Rhonald C Lua; Serkan Erdin; Olivier Lichtarge
Journal:  Bioinformatics       Date:  2012-06-11       Impact factor: 6.937

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

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

4.  Missing in action: enzyme functional annotations in biological databases.

Authors:  Nicholas Furnham; John S Garavelli; Rolf Apweiler; Janet M Thornton
Journal:  Nat Chem Biol       Date:  2009-08       Impact factor: 15.040

5.  Toward an online repository of Standard Operating Procedures (SOPs) for (meta)genomic annotation.

Authors:  Samuel V Angiuoli; Aaron Gussman; William Klimke; Guy Cochrane; Dawn Field; George Garrity; Chinnappa D Kodira; Nikos Kyrpides; Ramana Madupu; Victor Markowitz; Tatiana Tatusova; Nick Thomson; Owen White
Journal:  OMICS       Date:  2008-06

Review 6.  A simple recipe for the non-expert bioinformaticist for building experimentally-testable hypotheses for proteins with no known homologs.

Authors:  Alexander Zawaira; Youtaro Shibayama
Journal:  J Struct Funct Genomics       Date:  2012-09-07

7.  Using deep RNA sequencing for the structural annotation of the Laccaria bicolor mycorrhizal transcriptome.

Authors:  Peter E Larsen; Geetika Trivedi; Avinash Sreedasyam; Vincent Lu; Gopi K Podila; Frank R Collart
Journal:  PLoS One       Date:  2010-07-06       Impact factor: 3.240

8.  Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP.

Authors:  Troy Hawkins; Meghana Chitale; Daisuke Kihara
Journal:  BMC Bioinformatics       Date:  2010-05-19       Impact factor: 3.169

9.  A statistical model of protein sequence similarity and function similarity reveals overly-specific function predictions.

Authors:  Brenton Louie; Roger Higdon; Eugene Kolker
Journal:  PLoS One       Date:  2009-10-21       Impact factor: 3.240

10.  Combining structure and sequence information allows automated prediction of substrate specificities within enzyme families.

Authors:  Marc Röttig; Christian Rausch; Oliver Kohlbacher
Journal:  PLoS Comput Biol       Date:  2010-01-08       Impact factor: 4.475

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