Improvement of HIV-1 coreceptor tropism prediction by employing selected nucleotide positions of the env gene in a Bayesian network classifier

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Abstract

Objectives

This study aimed to develop a genotypic method to predict HIV-1 coreceptor usage by employing the nucleotide sequence of the env gene in a tree-augmented naive Bayes (TAN) classifier, and to evaluate its accuracy in prediction compared with other available tools.

Methods

A wrapper data-mining strategy interleaved with a TAN algorithm was employed to evaluate the predictor value of every single-nucleotide position throughout the HIV-1 env gene. Based on these results, different nucleotide positions were selected to develop a TAN classifier, which was employed to predict the coreceptor tropism of all the full-length env gene sequences with information on coreceptor tropism currently available at the Los Alamos HIV Sequence Database.

Results

Employing 26 nucleotide positions in the TAN classifier, an accuracy of 95.6%, a specificity (identification of CCR5-tropic viruses) of 99.4% and a sensitivity (identification of CXCR4/dual-tropic viruses) of 80.5% were achieved for the in silico cross-validation. Compared with the phenotypic determination of coreceptor usage, the TAN algorithm achieved more accurate predictions than WebPSSM and Geno2pheno [coreceptor] (P < 0.05).

Conclusions

The use of the methodology presented in this work constitutes a robust strategy to identify genetic patterns throughout the HIV-1 env gene differently present in CCR5-tropic and CXCR4/dual-tropic viruses. Moreover, the TAN classifier can be used as a genotypic tool to predict the coreceptor usage of HIV-1 isolates reaching more accurate predictions than with other widely used genotypic tools. The use of this algorithm could improve the correct prescribing of CCR5 antagonist drugs to HIV-1-infected patients.

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