Automatic prediction of protein function |
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| Authors: | B.?Rost mailto:rost@columbia.edu" title=" rost@columbia.edu" itemprop=" email" data-track=" click" data-track-action=" Email author" data-track-label=" " >Email author,J.?Liu,R.?Nair,K.?O.?Wrzeszczynski,Y.?Ofran |
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| Affiliation: | (1) Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, 10032 New York, New York, USA;(2) Columbia University Center for Computational Biology and Bioinformatics (C2B2), 1150 St. Nicholas Avenue, 10032 New York, New York, USA;(3) Northeast Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, Columbia University, 650 West 168th Street BB217, 10032 New York, New York, USA;(4) Department of Pharmacology, Columbia University, 630 West 168th Street, 10032 New York, New York, USA;(5) Department of Physics, Columbia University, 538 West 120th Street, 10027 New York, New York, USA;(6) Department of Biomedical Informatics, Columbia University, 630 West 168th Street, 10032 New York, New York, USA |
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| Abstract: | ![]()
Most methods annotating protein function utilise sequence homology to proteins of experimentally known function. Such a homology-based annotation transfer is problematic and limited in scope. Therefore, computational biologists have begun to develop ab initio methods that predict aspects of function, including subcellular localization, post-translational modifications, functional type and protein-protein interactions. For the first two cases, the most accurate approaches rely on identifying short signalling motifs, while the most general methods utilise tools of artificial intelligence. An outstanding new method predicts classes of cellular function directly from sequence. Similarly, promising methods have been developed predicting protein-protein interaction partners at acceptable levels of accuracy for some pairs in entire proteomes. No matter how difficult the task, successes over the last few years have clearly paved the way for ab initio prediction of protein function.Received 26 March 2003; received after revision 15 May 2003; accepted 12 June 2003 |
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| Keywords: | Genome analysis protein function prediction ab initio prediction neural networks multiple alignments sequence analysis subcellular localization post-translational modifications protein-protein interactions bioinformatics |
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