Identifying protein short linear motifs by position-specific scoring matrix

Chun Fang*, Tamotsu Noguchi, Hayato Yamana, Fuzhen Sun

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Short linear motifs (SLiMs) play a central role in several biological functions, such as cell regulation, scaffolding, cell signaling, post-translational modification, and cleavage. Identifying SLiMs is an important step for understanding their functions and mechanism. Due to their short length and particular properties, discovery of SLiMs in proteins is a challenge both experimentally and computationally. So far, many existing computational methods adopted many predicted sequence or structures features as input for prediction, there is no report about using position-specific scoring matrix (PSSM) profiles of proteins directly for SLiMs prediction. In this study, we describe a simple method, named as PSSMpred, which only use the evolutionary information generated in form of PSSM profiles of protein sequences for SLiMs prediction. When comparing with other methods tested on the same datasets, PSSMpred achieves the best performances: (1) achieving 0.03–0.1 higher AUC than other methods when tested on HumanTest151; (2) achieving 0.03– 0.05 and 0.03–0.06 higher AUC than other methods when tested on ANCHOR-short and ANCHOR-long respectively.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages206-214
Number of pages9
DOIs
Publication statusPublished - 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9713 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Prediction
  • Protein
  • Short linear motifs

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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