PlantPhos: Using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

67Citations
Citations of this article
40Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Protein phosphorylation catalyzed by kinases plays crucial regulatory roles in intracellular signal transduction. Due to the difficulty in performing high-throughput mass spectrometry-based experiment, there is a desire to predict phosphorylation sites using computational methods. However, previous studies regarding in silico prediction of plant phosphorylation sites lack the consideration of kinase-specific phosphorylation data. Thus, we are motivated to propose a new method that investigates different substrate specificities in plant phosphorylation sites.Results: Experimentally verified phosphorylation data were extracted from TAIR9-a protein database containing 3006 phosphorylation data from the plant species Arabidopsis thaliana. In an attempt to investigate the various substrate motifs in plant phosphorylation, maximal dependence decomposition (MDD) is employed to cluster a large set of phosphorylation data into subgroups containing significantly conserved motifs. Profile hidden Markov model (HMM) is then applied to learn a predictive model for each subgroup. Cross-validation evaluation on the MDD-clustered HMMs yields an average accuracy of 82.4% for serine, 78.6% for threonine, and 89.0% for tyrosine models. Moreover, independent test results using Arabidopsis thaliana phosphorylation data from UniProtKB/Swiss-Prot show that the proposed models are able to correctly predict 81.4% phosphoserine, 77.1% phosphothreonine, and 83.7% phosphotyrosine sites. Interestingly, several MDD-clustered subgroups are observed to have similar amino acid conservation with the substrate motifs of well-known kinases from Phospho.ELM-a database containing kinase-specific phosphorylation data from multiple organisms.Conclusions: This work presents a novel method for identifying plant phosphorylation sites with various substrate motifs. Based on cross-validation and independent testing, results show that the MDD-clustered models outperform models trained without using MDD. The proposed method has been implemented as a web-based plant phosphorylation prediction tool, PlantPhos http://csb.cse.yzu.edu.tw/PlantPhos/. Additionally, two case studies have been demonstrated to further evaluate the effectiveness of PlantPhos. © 2011 Lee et al; licensee BioMed Central Ltd.

References Powered by Scopus

WebLogo: A sequence logo generator

9724Citations
N/AReaders
Get full text

Mass spectrometry-based proteomics

5953Citations
N/AReaders
Get full text

Profile hidden Markov models

4396Citations
N/AReaders
Get full text

Cited by Powered by Scopus

DbPTM in 2019: Exploring disease association and cross-Talk of post-Translational modifications

180Citations
N/AReaders
Get full text

Quantitative phosphoproteomics of the ataxia telangiectasia-mutated (ATM) and ataxia telangiectasia-mutated and Rad3-related (ATR) dependent DNA damage response in arabidopsis thaliana

172Citations
N/AReaders
Get full text

DbPTM 3.0: An informative resource for investigating substrate site specificity and functional association of protein post-translational modifications

168Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Lee, T. Y., Bretaña, N. A., & Lu, C. T. (2011). PlantPhos: Using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity. BMC Bioinformatics, 12. https://doi.org/10.1186/1471-2105-12-261

Readers' Seniority

Tooltip

Researcher 16

47%

PhD / Post grad / Masters / Doc 13

38%

Professor / Associate Prof. 5

15%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 21

64%

Biochemistry, Genetics and Molecular Bi... 6

18%

Computer Science 4

12%

Engineering 2

6%

Save time finding and organizing research with Mendeley

Sign up for free