On Position-Specific Scoring Matrix for Protein Function Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 3.84 |
Motivation: The success of genome sequencing has resulted in many protein sequences without functional annotation. We present ConFunc, an automated Gene Ontology (GO)-based protein function prediction approach, which uses conserved residues to generate sequence profiles to infer function. ConFunc split sets of sequences identified by PSI-BLAST into sub-alignments according to their GO annotations. Conserved residues are identified for each GO term sub-alignment for which a position specific scoring matrix is generated. This combination of steps produces a set of feature (GO annotation) derived profiles from which protein function is predicted. Results: We assess the ability of ConFunc, BLAST and PSI-BLAST to predict protein function in the twilight zone of sequence similarity. ConFunc significantly outperforms BLAST & PSI-BLAST obtaining levels of recall and precision that are not obtained by either method and maximum precision 24% greater than BLAST. Further for a large test set of sequences with homologues of low sequence identity, at high levels of presicision, ConFunc obtains recall six times greater than BLAST. These results demonstrate the potential for ConFunc to form part of an automated genomics annotation pipeline. Availability: http://www.sbg.bio.ic.ac.uk/confunc Contact: m.sternberg@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.