Efficacy of different protein descriptors in predicting protein functional families

BMC Bioinformatics. 2007 Aug 17;8:300. doi: 10.1186/1471-2105-8-300.


Background: Sequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families.

Results: The performance of these descriptor-sets were ranked by Matthews correlation coefficient (MCC), and categorized into two groups based on their performance. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets.

Conclusion: Our study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors.

MeSH terms

  • Algorithms*
  • Amino Acid Sequence
  • Molecular Sequence Data
  • Proteins / chemistry*
  • Proteins / classification*
  • Proteins / metabolism
  • Sequence Alignment / methods*
  • Sequence Analysis, Protein / methods*
  • Structure-Activity Relationship


  • Proteins