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A computational model for predicting transmembrane regions of retroviruses.

作者信息

Liu Ze, Lv Hongqiang, Han Jiuqiang, Liu Ruiling

机构信息

1 School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China.

出版信息

J Bioinform Comput Biol. 2017 Jun;15(3):1750010. doi: 10.1142/S021972001750010X. Epub 2017 Mar 21.

DOI:10.1142/S021972001750010X
PMID:28403667
Abstract

Transmembrane region (TR) is a conserved region of transmembrane (TM) subunit in envelope (env) glycoprotein of retrovirus. Evidences have shown that TR is responsible for anchoring the env glycoprotein on the lipid bilayer and substitution of the TR for a covalently linked lipid anchor abrogates fusion. However, universal software could not achieve sufficient accuracy as TM in env also has several motifs such as signal peptide, fusion peptide and immunosuppressive domain composed largely of hydrophobic residues. In this paper, a support vector machine-based (SVM) model is proposed to identify TRs in retroviruses. Firstly, physicochemical and evolutionary information properties were extracted as original features. And then, the feature importance was analyzed by minimum Redundancy Maximum Relevance (mRMR) feature selection criterion. Our model achieved an Sn of 0.955, Sp of 0.998, ACC of 0.995, MCC of 0.954 using 10-fold cross-validation on the training dataset. These results suggest that the proposed model can be used to predict TRs in non-annotation retroviruses and 11917, 3344, 2, 289 and 6 new putative TRs were found in HERV, HIV, HTLV, SIV, MLV, respectively.

摘要

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