Protein function assignment through mining cross-species protein-protein interactions.
BACKGROUND
As we move into the post genome-sequencing era, an immediate challenge is how to make best use of the large amount of high-throughput experimental data to assign functions to currently uncharacterized proteins. We here describe CSIDOP, a new method for protein function assignment based on shared interacting domain patterns extracted from cross-species protein-protein interaction data.


RESULTS
The proposed method is assessed both biologically and statistically over the genome of H. sapiens. The CSIDOP method is capable of making protein function prediction with accuracy of 95.42% using 2,972 gene ontology (GO) functional categories. In addition, we are able to assign novel functional annotations for 181 previously uncharacterized proteins in H. sapiens. Furthermore, we demonstrate that for proteins that are characterized by GO, the CSIDOP may predict extra functions. This is attractive as a protein normally executes a variety of functions in different processes and its current GO annotation may be incomplete.


CONCLUSIONS
It can be shown through experimental results that the CSIDOP method is reliable and practical in use. The method will continue to improve as more high quality interaction data becomes available and is readily scalable to a genome-wide application.
Chen
Xue-Wen
XW
Bioinformatics and Computational Life-Sciences Laboratory, Information and Telecommunication Technology Center (ITTC), Department of Electrical Engineering and Computer Science, The University of Kansas, Lawrence, Kansas, USA.
Liu
Mei
M
Ward
Robert
R
D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008722:Methods; D054730:Protein Interaction Domains and Motifs; D011506:Proteins / Q000502:physiology*; D040901:Proteomics / Q000379:methods*
D011506:Proteins