Batch-learning self-organizing map for predicting functions of poorly-characterized proteins massively accumulated

Abstract

As the result of the decoding of large numbers of genome sequences, numerous proteins whose functions cannot be identified by the homology search of amino acid sequences have accumulated and remain of no use to science and industry. Establishment of novel prediction methods for protein function is urgently needed. We previously developed Batch-Learning SOM (BL-SOM) for genome informatics; here, we developed BL-SOM to predict functions of proteins on the basis of similarity in oligopeptide composition of proteins. Oligopeptides are component parts of a protein and involved in formation of its functional motifs and structural parts. Concerning oligopeptide frequencies in 110,000 proteins classified into 2853 function-known COGs (clusters of orthologous groups), BL-SOM could faithfully reproduce the COG classifications, and therefore, proteins whose functions have been unidentified with homology searches could be related to function-known proteins. BL-SOM was applied to predict protein functions of large numbers of proteins obtained from metagenome analyses. © 2009 Springer-Verlag Berlin Heidelberg.