A study of statistical methods for function prediction of protein motifs.
Automatic discovery of new protein motifs (i.e. amino acid patterns) is one of the major challenges in bioinformatics. Several algorithms have been proposed that can extract statistically significant motif patterns from any set of protein sequences. With these methods, one can generate a large set of candidate motifs that may be biologically meaningful. This article examines methods to predict the functions of these candidate motifs. We use several statistical methods: a popularity method, a mutual information method and probabilistic translation models. These methods capture, from different perspectives, the correlations between the matched motifs of a protein and its assigned Gene Ontology terms that characterise the function of the protein. We evaluate these different methods using the known motifs in the InterPro database. Each method is used to rank candidate terms for each motif. We then use the expected mean reciprocal rank to evaluate the performance. The results show that, in general, all these methods perform well, suggesting that they can all be useful for predicting the function of an unknown motif. Among the methods tested, a probabilistic translation model with a popularity prior performs the best.
Tao
Tao
T
Department of Computer Science, University of Illinois at Urbana-Champaign, 506 S. Matthews Avenue, Urbana, IL 61801, USA. taotao@cs.uiuc.edu
Zhai
Cheng Xiang
CX
Lu
Xinghua
X
Fang
Hui
H
D000465:Algorithms; D020816:Amino Acid Motifs / Q000502:physiology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D030562:Databases, Protein; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship
D011506:Proteins