title: Improvement of HIV-1 coreceptor tropism prediction by employing selected nucleotide positions of the env gene in a Bayesian network classifier.
abstract: OBJECTIVE
  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.
authors.affiliation: HIV Biology and Variability Unit, Centro Nacional de Microbiología, Instituto de Salud Carlos III, Majadahonda, Madrid, Spain. frandifu@gmail.com
authors.forename: Francisco
authors.initials: F
authors.lastname: Díez-Fuertes
authors.forename: Elena
authors.initials: E
authors.lastname: Delgado
authors.forename: Yolanda
authors.initials: Y
authors.lastname: Vega
authors.forename: Aurora
authors.initials: A
authors.lastname: Fernández-García
authors.forename: María Teresa
authors.initials: MT
authors.lastname: Cuevas
authors.forename: Milagros
authors.initials: M
authors.lastname: Pinilla
authors.forename: Valentina
authors.initials: V
authors.lastname: García
authors.forename: Lucía
authors.initials: L
authors.lastname: Pérez-Álvarez
authors.forename: Michael M
authors.initials: MM
authors.lastname: Thomson
mesh_terms: D019295:Computational Biology / Q000379:methods; D005838:Genotype; D015497:HIV-1 / Q000235:genetics* / Q000502:physiology; D006801:Humans; D025202:Molecular Diagnostic Techniques / Q000379:methods*; D015728:Receptors, HIV / Q000378:metabolism*; D056189:Viral Tropism; D014773:Virology / Q000379:methods*; D054299:env Gene Products, Human Immunodeficiency Virus / Q000235:genetics*
chemical_list: D015728:Receptors, HIV; D054299:env Gene Products, Human Immunodeficiency Virus
keywords: Bayesian classifier; maraviroc; wrapper