title: Predicting Bevirimat resistance of HIV-1 from genotype.
abstract: BACKGROUND
  Maturation inhibitors are a new class of antiretroviral drugs. Bevirimat (BVM) was the first substance in this class of inhibitors entering clinical trials. While the inhibitory function of BVM is well established, the molecular mechanisms of action and resistance are not well understood. It is known that mutations in the regions CS p24/p2 and p2 can cause phenotypic resistance to BVM. We have investigated a set of p24/p2 sequences of HIV-1 of known phenotypic resistance to BVM to test whether BVM resistance can be predicted from sequence, and to identify possible molecular mechanisms of BVM resistance in HIV-1.
  
  
  RESULTS
  We used artificial neural networks and random forests with different descriptors for the prediction of BVM resistance. Random forests with hydrophobicity as descriptor performed best and classified the sequences with an area under the Receiver Operating Characteristics (ROC) curve of 0.93 +/- 0.001. For the collected data we find that p2 sequence positions 369 to 376 have the highest impact on resistance, with positions 370 and 372 being particularly important. These findings are in partial agreement with other recent studies. Apart from the complex machine learning models we derived a number of simple rules that predict BVM resistance from sequence with surprising accuracy. According to computational predictions based on the data set used, cleavage sites are usually not shifted by resistance mutations. However, we found that resistance mutations could shorten and weaken the alpha-helix in p2, which hints at a possible resistance mechanism.
  
  
  CONCLUSIONS
  We found that BVM resistance of HIV-1 can be predicted well from the sequence of the p2 peptide, which may prove useful for personalized therapy if maturation inhibitors reach clinical practice. Results of secondary structure analysis are compatible with a possible route to BVM resistance in which mutations weaken a six-helix bundle discovered in recent experiments, and thus ease Gag cleavage by the retroviral protease.
authors.affiliation: Department of Bioinformatics, Center of Medical Biotechnology, University of Duisburg-Essen, Universitaetsstr. 2, 45117 Essen, Germany. dominik.heider@uni-due.de
authors.forename: Dominik
authors.initials: D
authors.lastname: Heider
authors.forename: Jens
authors.initials: J
authors.lastname: Verheyen
authors.forename: Daniel
authors.initials: D
authors.lastname: Hoffmann
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology*; D019295:Computational Biology / Q000379:methods*; D024882:Drug Resistance, Viral / Q000235:genetics*; D005838:Genotype; D015497:HIV-1 / Q000187:drug effects / Q000235:genetics*; D009154:Mutation; D016571:Neural Networks, Computer; D013386:Succinates / Q000494:pharmacology*; D014315:Triterpenes / Q000494:pharmacology*; D014779:Virus Replication / Q000235:genetics; D053978:Pentacyclic Triterpenes
chemical_list: D019380:Anti-HIV Agents; D013386:Succinates; D014315:Triterpenes; C479935:bevirimat; D053978:Pentacyclic Triterpenes