A new machine learning approach for protein phosphorylation site prediction in plants

Jianjiong Gao, Ganesh Kumar Agrawal, Jay J. Thelen, Zoran Obradovic, A. Keith Dunker, Dong Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

17 Scopus citations

Abstract

Protein phosphorylation is a crucial regulatory mechanism in various organisms. With recent improvements in mass spectrometry, phosphorylationsite data are rapidly accumulating. Despite this wealth of data, computational prediction of phosphorylation sites remains a challenging task. This is particularly true in plants, due to the limited information on substrate specificities of protein kinases in plants and the fact that current phosphorylation prediction tools are trained with kinase-specific phosphorylation data from non-plant organisms. In this paper, we proposed a new machine learning approach for phosphorylation site prediction. We incorporate protein sequence information and protein disordered regions, and integrate machine learning techniques of knearest neighbor and support vector machine for redicting phosphorylation sites. Test results on the PhosPhAt dataset of phosphoserines in Arabidopsis and the TAIR7 non-redundant protein database show good performance of our proposed phosphorylation site prediction method.

Original languageEnglish (US)
Title of host publicationBioinformatics and Computational Biology - First International Conference, BICoB 2009, Proceedings
Pages18-29
Number of pages12
DOIs
StatePublished - 2009
Event1st International Conference on Bioinformatics and Computational Biology, BICoB 2009 - New Orleans, LA, United States
Duration: Apr 8 2009Apr 10 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5462 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other1st International Conference on Bioinformatics and Computational Biology, BICoB 2009
CountryUnited States
CityNew Orleans, LA
Period4/8/094/10/09

Keywords

  • Arabidopsis
  • KNN
  • Phosphoproteomics
  • Protein Disorder
  • Protein phosphorylation
  • SVM

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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