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

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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.

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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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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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Authors and Affiliations

  1. College of Chemistry, Sichuan University, 610064, Chengdu, China

    Wenjia Xiong, Yanzhi Guo & Menglong Li

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  1. Wenjia Xiong
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  2. Yanzhi Guo
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  3. Menglong Li
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Correspondence to Menglong Li.

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Xiong, W., Guo, Y. & Li, M. Prediction of Lipid-Binding Sites Based on Support Vector Machine and Position Specific Scoring Matrix. Protein J 29, 427–431 (2010). https://doi.org/10.1007/s10930-010-9269-x

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