A Large-Scale Evaluation of Computational Protein Function Prediction

Nat Methods. 2013 Mar;10(3):221-7. doi: 10.1038/nmeth.2340. Epub 2013 Jan 27.

Abstract

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Animals
  • Computational Biology / methods*
  • Databases, Protein
  • Exoribonucleases / classification
  • Exoribonucleases / genetics
  • Exoribonucleases / physiology
  • Forecasting
  • Humans
  • Molecular Biology / methods*
  • Molecular Sequence Annotation*
  • Proteins / chemistry
  • Proteins / classification
  • Proteins / genetics
  • Proteins / physiology*
  • Species Specificity

Substances

  • Proteins
  • Exoribonucleases
  • PNPT1 protein, human