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Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Author

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  • Tao Huang
  • Ping Wang
  • Zhi-Qiang Ye
  • Heng Xu
  • Zhisong He
  • Kai-Yan Feng
  • LeLe Hu
  • WeiRen Cui
  • Kai Wang
  • Xiao Dong
  • Lu Xie
  • Xiangyin Kong
  • Yu-Dong Cai
  • Yixue Li

Abstract

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies.

Suggested Citation

  • Tao Huang & Ping Wang & Zhi-Qiang Ye & Heng Xu & Zhisong He & Kai-Yan Feng & LeLe Hu & WeiRen Cui & Kai Wang & Xiao Dong & Lu Xie & Xiangyin Kong & Yu-Dong Cai & Yixue Li, 2010. "Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-7, July.
  • Handle: RePEc:plo:pone00:0011900
    DOI: 10.1371/journal.pone.0011900
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    References listed on IDEAS

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    1. Tao Huang & WeiRen Cui & LeLe Hu & KaiYan Feng & Yi-Xue Li & Yu-Dong Cai, 2009. "Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
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    Cited by:

    1. Yu-Dong Cai & Tao Huang & Kai-Yan Feng & Lele Hu & Lu Xie, 2010. "A Unified 35-Gene Signature for both Subtype Classification and Survival Prediction in Diffuse Large B-Cell Lymphomas," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-8, September.
    2. Tao Huang & Lei Chen & Yu-Dong Cai & Kuo-Chen Chou, 2011. "Classification and Analysis of Regulatory Pathways Using Graph Property, Biochemical and Physicochemical Property, and Functional Property," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.

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