Research Article Open Access

Analysis of Resistance to Human Immunodeficiency Virus Protease Inhibitors Using Molecular Mechanics and Machine Learning Strategies

A. Harishchander1, D. Alex Anand1 and Sanjib Senapati2
  • 1 Sathyabama University, India
  • 2 Indian Institute of Technology Madras, India

Abstract

Problem statement: Drug resistance is the most important factor, which influences the failure of current HIV therapies. So, the ability to predict the drug resistance of HIV-1 protease mutants will be useful in developing more effective and longer lasting treatment regimens. Approach: Drug resistance of HIV-1 protease is predicted with two current protease inhibitors (Indinavir and Saquinavir). The problem was approached from two perspectives. First, structural features of the HIV protease with inhibitor complex were constructed. Next, a classifier was constructed based on the patterns of various drug resistant mutants. In first stage SPDB viewer (for making mutations) and INSIGHT II (for analyzing binding energies and hydrogen bond contact with the inhibitor and the binding site) software's were used for structural property analysis. In the second stage a supervised learning linear Classifier (SVM-LIB) in DTREG tool has been used to analyze the Resistant and susceptible patterns. Finally Genetic Algorithm in Matlab tool has been used for Optimization. Results: Structural data mining performed linear SVM model gives "93% accuracy" in initial screening of pattern sets HIV1 protease (wild type and mutants) of sub type B against the inhibitors Indinavir and Saquinavir. Genetic algorithm gives "80% Accuracy" for Indinavir and "60% Accuracy" for Saquinavir. Conclusion: Geno2pheno software uses machine learning analysis for subtypes of HIV with proper inhibitory values. If Molecular Mechanics is followed by Machine Learning with appropriate Inhibitory or effective concentration analysis, the validation of Genotyping will be more accurate than initial Geno2Pheno analysis. In future even the dynamics of the molecule will be analyzed with molecular mechanics and machine learning principles for various mutations of all FDA approved protease Inhibitors within the individual complex with the protease.

Current Research in Medicine
Volume 1 No. 2, 2010, 126-132

DOI: https://doi.org/10.3844/amjsp.2010.126.132

Submitted On: 5 January 2010 Published On: 31 October 2010

How to Cite: Harishchander, A., Anand, D. A. & Senapati, S. (2010). Analysis of Resistance to Human Immunodeficiency Virus Protease Inhibitors Using Molecular Mechanics and Machine Learning Strategies. Current Research in Medicine, 1(2), 126-132. https://doi.org/10.3844/amjsp.2010.126.132

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Keywords

  • Binding energies
  • protease
  • hydrogen bonding
  • linear SVM model
  • Matlab tool and genetic algorithms