Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties.
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 structura...
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2010
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oai:doaj.org-article:9d419a84d9284c4ba304fe00b716ff452021-11-18T06:36:32ZPrediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties.1932-620310.1371/journal.pone.0011900https://doaj.org/article/9d419a84d9284c4ba304fe00b716ff452010-07-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20689580/?tool=EBIhttps://doaj.org/toc/1932-6203Non-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.Tao HuangPing WangZhi-Qiang YeHeng XuZhisong HeKai-Yan FengLele HuWeiren CuiKai WangXiao DongLu XieXiangyin KongYu-Dong CaiYixue LiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 7, p e11900 (2010) |
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Medicine R Science Q 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 Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Tao Huang |
| title |
Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| title_short |
Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| title_full |
Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| title_fullStr |
Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| title_full_unstemmed |
Prediction of deleterious non-synonymous SNPs based on protein interaction network and hybrid properties. |
| title_sort |
prediction of deleterious non-synonymous snps based on protein interaction network and hybrid properties. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2010 |
| url |
https://doaj.org/article/9d419a84d9284c4ba304fe00b716ff45 |
| work_keys_str_mv |
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