Functional equivalency inferred from "authoritative sources" in networks of homologous proteins.
A one-on-one mapping of protein functionality across different species is a critical component of comparative analysis. This paper presents a heuristic algorithm for discovering the Most Likely Functional Counterparts (MoLFunCs) of a protein, based on simple concepts from network theory. A key feature of our algorithm is utilization of the user's knowledge to assign high confidence to selected functional identification. We show use of the algorithm to retrieve functional equivalents for 7 membrane proteins, from an exploration of almost 40 genomes form multiple online resources. We verify the functional equivalency of our dataset through a series of tests that include sequence, structure and function comparisons. Comparison is made to the OMA methodology, which also identifies one-on-one mapping between proteins from different species. Based on that comparison, we believe that incorporation of user's knowledge as a key aspect of the technique adds value to purely statistical formal methods.
Natarajan
Shreedhar
S
Biophysics and Computational Biology, University of Illinois, Urbana-Champaign, Illinois, USA.
Jakobsson
Eric
E
D000465:Algorithms; D020816:Amino Acid Motifs; D000818:Animals; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D030562:Databases, Protein; D016678:Genome; D020407:Internet; D051663:Kv1.2 Potassium Channel / Q000737:chemistry; D015233:Models, Statistical; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D051381:Rats
D051663:Kv1.2 Potassium Channel; D011506:Proteins