SeqNLS: nuclear localization signal prediction based on frequent pattern mining and linear motif scoring

PLoS One. 2013 Oct 29;8(10):e76864. doi: 10.1371/journal.pone.0076864. eCollection 2013.

Abstract

Nuclear localization signals (NLSs) are stretches of residues in proteins mediating their importing into the nucleus. NLSs are known to have diverse patterns, of which only a limited number are covered by currently known NLS motifs. Here we propose a sequential pattern mining algorithm SeqNLS to effectively identify potential NLS patterns without being constrained by the limitation of current knowledge of NLSs. The extracted frequent sequential patterns are used to predict NLS candidates which are then filtered by a linear motif-scoring scheme based on predicted sequence disorder and by the relatively local conservation (IRLC) based masking. The experiment results on the newly curated Yeast and Hybrid datasets show that SeqNLS is effective in detecting potential NLSs. The performance comparison between SeqNLS with and without the linear motif scoring shows that linear motif features are highly complementary to sequence features in discerning NLSs. For the two independent datasets, our SeqNLS not only can consistently find over 50% of NLSs with prediction precision of at least 0.7, but also outperforms other state-of-the-art NLS prediction methods in terms of F1 score or prediction precision with similar or higher recall rates. The web server of the SeqNLS algorithm is available at http://mleg.cse.sc.edu/seqNLS.

Publication types

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

MeSH terms

  • Algorithms*
  • Amino Acid Motifs*
  • Animals
  • Data Mining / methods*
  • Humans
  • Internet
  • Nuclear Localization Signals*
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Saccharomyces cerevisiae / genetics
  • Saccharomyces cerevisiae / metabolism

Substances

  • Nuclear Localization Signals

Grant support

This work was supported by the National Science Foundation Career Award (Grant BIO-DBI-0845381). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.