Cellular and Molecular Life Sciences CMLS

, Volume 60, Issue 12, pp 2637–2650

Automatic prediction of protein function

Authors

    • Department of Biochemistry and Molecular BiophysicsColumbia University
    • Columbia University Center for Computational Biology and Bioinformatics (C2B2)
    • Northeast Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular BiophysicsColumbia University
  • J. Liu
    • Department of Biochemistry and Molecular BiophysicsColumbia University
    • Northeast Structural Genomics Consortium (NESG), Department of Biochemistry and Molecular BiophysicsColumbia University
    • Department of PharmacologyColumbia University
  • R. Nair
    • Department of Biochemistry and Molecular BiophysicsColumbia University
    • Department of PhysicsColumbia University
  • K. O. Wrzeszczynski
    • Department of Biochemistry and Molecular BiophysicsColumbia University
  • Y. Ofran
    • Department of Biochemistry and Molecular BiophysicsColumbia University
    • Department of Biomedical InformaticsColumbia University
Review

DOI: 10.1007/s00018-003-3114-8

Cite this article as:
Rost, B., Liu, J., Nair, R. et al. CMLS, Cell. Mol. Life Sci. (2003) 60: 2637. doi:10.1007/s00018-003-3114-8

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 analysisprotein function predictionab initio predictionneural networksmultiple alignmentssequence analysissubcellular localizationpost-translational modificationsprotein-protein interactionsbioinformatics

Copyright information

© Birkhäuser-Verlag Basel 2003