Review

Cellular and Molecular Life Sciences CMLS

, Volume 60, Issue 12, pp 2637-2650

First online:

Automatic prediction of protein function

  • B. RostAffiliated withDepartment of Biochemistry and Molecular Biophysics, Columbia UniversityColumbia University Center for Computational Biology and Bioinformatics (C2B2)Northeast Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, Columbia University Email author 
  • , J. LiuAffiliated withDepartment of Biochemistry and Molecular Biophysics, Columbia UniversityNortheast Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular Biophysics, Columbia UniversityDepartment of Pharmacology, Columbia University
  • , R. NairAffiliated withDepartment of Biochemistry and Molecular Biophysics, Columbia UniversityDepartment of Physics, Columbia University
  • , K. O. WrzeszczynskiAffiliated withDepartment of Biochemistry and Molecular Biophysics, Columbia University
  • , Y. OfranAffiliated withDepartment of Biochemistry and Molecular Biophysics, Columbia UniversityDepartment of Biomedical Informatics, Columbia University

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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.

Genome analysis protein function prediction ab initio prediction neural networks multiple alignments sequence analysis subcellular localization post-translational modifications protein-protein interactions bioinformatics