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PMID: 16110343 Published · ppublish English Comparative Study Journal Article Research Support, Non-U.S. Gov't

Comparative genomics and disorder prediction identify biologically relevant SH3 protein interactions.

PLoS computational biology ·Vol. 1 ·No. 3 ·2005-08-00 ·Pages e26

Beltrao P, Serrano L

Abstract

Protein interaction networks are an important part of the post-genomic effort to integrate a part-list view of the cell into system-level understanding. Using a set of 11 yeast genomes we show that combining comparative genomics and secondary structure information greatly increases consensus-based prediction of SH3 targets. Benchmarking of our method against positive and negative standards gave 83% accuracy with 26% coverage. The concept of an optimal divergence time for effective comparative genomics studies was analyzed, demonstrating that genomes of species that diverged very recently from Saccharomyces cerevisiae(S. mikatae, S. bayanus, and S. paradoxus), or a long time ago (Neurospora crassa and Schizosaccharomyces pombe), contain less information for accurate prediction of SH3 targets than species within the optimal divergence time proposed. We also show here that intrinsically disordered SH3 domain targets are more probable sites of interaction than equivalent sites within ordered regions. Our findings highlight several novel S. cerevisiae SH3 protein interactions, the value of selection of optimal divergence times in comparative genomics studies, and the importance of intrinsic disorder for protein interactions. Based on our results we propose novel roles for the S. cerevisiae proteins Abp1p in endocytosis and Hse1p in endosome protein sorting.

MeSH Terms
Base Sequence Computational Biology Conserved Sequence Evolution, Molecular Fungal Proteins/chemistry,genetics,metabolism Genome, Fungal/genetics Genomics Ligands Protein Binding Protein Interaction Mapping/methods Protein Structure, Secondary Time Factors src Homology Domains
Chemicals
Fungal Proteins Ligands
Authors & Affiliations
2 authors, click to expand affiliations / ORCID
Beltrao Pedro
EMBL Structural and Computational Biology, Heidelberg, Germany. beltrao@embl-heidelberg.de
Serrano Luis
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Article Info
Journal
PLoS computational biology
Abbr.
PLoS Comput Biol
ISSN
1553-734X
DOI
10.1371/journal.pcbi.0010026
Email
beltrao@embl-heidelberg.de
Published
2005-08-00
Epub
2005-00-12
Pages
e26
Language
English
Region
United States
NLM ID
101238922
PMCID
PMC1187863
Subset
IM
External Links
PubMed source DOI: 10.1371/journal.pcbi.0010026 PMC Full Text View journal details
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