Predicting DNA-binding specificities of eukaryotic transcription factors

PLoS One. 2010 Nov 30;5(11):e13876. doi: 10.1371/journal.pone.0013876.

Abstract

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Amino Acid Motifs / genetics
  • Amino Acid Sequence
  • Animals
  • Binding Sites / genetics
  • Binding, Competitive
  • Computational Biology / methods
  • DNA / metabolism*
  • DNA-Binding Proteins / genetics
  • DNA-Binding Proteins / metabolism*
  • Humans
  • Mice
  • Molecular Sequence Data
  • Protein Binding
  • Rats
  • Reproducibility of Results
  • Transcription Factors / genetics
  • Transcription Factors / metabolism*

Substances

  • DNA-Binding Proteins
  • Transcription Factors
  • DNA