Improvement in the determination of HIV-1 tropism using the V3 gene sequence and a combination of bioinformatic tools.

Assessment of HIV tropism using bioinformatic tools based on V3 sequences correlates poorly with results provided by phenotypic tropism assays, particularly for recognizing X4 viruses. This may represent an obstacle for the use of CCR5 antagonists. An algorithm combining several bioinformatic tools might improve the correlation with phenotypic tropism results. A total of 200 V3 sequences from HIV-1 subtype B, available in several databases with known phenotypic tropism results, were used to evaluate the sensitivity and specificity of seven different bioinformatic tools (PSSM, SVM, C4.5 decision tree generator and C4.5, PART, Charge Rule, and Geno2pheno). The best predictive bioinformatic tools were identified, and a model combining several of these was built. Using the 200 reference sequences, SVM and geno2-pheno showed the highest sensitivity for detecting X4 viruses (98.8% and 93.7%, respectively); however, their specificity was relatively low (62.5% and 86.6%, respectively). For R5 viruses, PSSM and C4.5 gave the same results and outperformed other bioinformatic tools (95.7% sensitivity, 82% specificity). When results from three out of these four tools were concordant, the sensitivity and specificity, taking as reference the results from phenotypic tropism assays, were over 90% in predicting either R5 or X4 viruses (AUC: 0.9701; 95% CI: 0.9358-0.9889). An algorithm combining four distinct bioinformatic tools (SVM, geno2pheno, PSSM and C4.5), improves the genotypic prediction of HIV tropism, and merits further evaluation, as it might prove useful as a screening strategy in clinical practice.