title: Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure.
abstract: BACKGROUND
  Drug resistance has become a severe challenge for treatment of HIV infections. Mutations accumulate in the HIV genome and make certain drugs ineffective. Prediction of resistance from genotype data is a valuable guide in choice of drugs for effective therapy.
  
  
  RESULTS
  In order to improve the computational prediction of resistance from genotype data we have developed a unified encoding of the protein sequence and three-dimensional protein structure of the drug target for classification and regression analysis. The method was tested on genotype-resistance data for mutants of HIV protease and reverse transcriptase. Our graph based sequence-structure approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers. Cross-validated regression analysis with the sparse dictionary gave excellent correlation between predicted and observed resistance.
  
  
  CONCLUSIONS
  The approach of encoding the protein structure and sequence as a 210-dimensional vector, based on Delaunay triangulation, has promise as an accurate method for predicting resistance from sequence for drugs inhibiting HIV protease and reverse transcriptase.
authors: Xiaxia
authors: X
authors: Yu
authors: Irene T
authors: IT
authors: Weber
authors: Robert W
authors: RW
authors: Harrison
mesh_terms: D019380:Anti-HIV Agents / Q000494:pharmacology; D024882:Drug Resistance, Viral / Q000235:genetics*; D005838:Genotype; D016333:HIV Protease / Q000235:genetics; D015497:HIV-1 / Q000187:drug effects* / Q000235:genetics*; D008958:Models, Molecular; D009154:Mutation; D016571:Neural Networks, Computer; D017434:Protein Structure, Tertiary; D060388:Support Vector Machine
chemical_list: D019380:Anti-HIV Agents; D016333:HIV Protease