The Protein Journal

, Volume 29, Issue 6, pp 427–431 | Cite as

Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix

Article
First Online:

Abstract

Lipid–protein interactions play a vital role in various biological processes, which are involved in cellular functions and can affect the stability, folding and the function of peptides and proteins. In this study, a sequence-based method by using support vector machine and position specific scoring matrix (PSSM) was proposed to predict lipid-binding sites. Considering the influence of surrounding residues of one amino acid, a sliding window was chosen to encode the PSSM profiles. By incorporating the evolutionary information and the local features of residues surrounding one lipid-binding site, the method yielded a high accuracy of 80.86% and the Matthew’s Correlation Coefficient of 0.58 by using fivefold cross validation test. The good result indicates the applicability of the method.

Keywords

Lipid–protein interactions Lipid-binding sites Position specific scoring matrix Support vector machine 

Abbreviations

SVM

Support vector machine

PSSM

Position-specific scoring matrices

RBF

Radial basis function

PSI-BLAST

Position specific iterated-basic local alignment search tool

ROC

Receiver operating characteristic

AUC

Area under curve

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Notes

Acknowledgments

The authors gratefully thank Yaping Fang for sharing the sliding window code. The authors would like to express their cordial thanks to the unknown reviewers for providing comments on the manuscript. This work was supported by the National Natural Science Foundation of China (Nos. 20905054, 20972103).

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.College of ChemistrySichuan UniversityChengduChina

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