Comprehensive bioinformatic analysis of HIV-1 protease specificity

Poster number: 2

Liwen You (1,2), Daniel Garwicz (3,4), Thorsteinn Rögnvaldsson (1)

  1. School of Information Science, Computer and Electrical Engineering, Halmstad University, Halmstad, Sweden
  2. Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden
  3. Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Biomedical Center, Lund University, Lund, Sweden
  4. Division of Molecular Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

Inhibitors of the protease of human immunodeficiency virus type 1 (HIV-1) are today an important part of highly active antiretroviral therapy (HAART) for HIV-infected individuals and AIDS patients. However, rapidly developing viral resistance to antiretroviral therapy is an increasing problem worldwide and accurate models for predicting protease cleavage specificity are needed for a rational design of more effective protease inhibitors. We have previously analyzed the specificity of HIV-1 protease using bioinformatic machine learning methods [1]. In the present work, we have extended these studies and used a new, extensive 746 peptide dataset for analysis of the specificity of HIV-1 protease [2]. We show that the best predictor is a nonlinear predictor using two physicochemical peptide residue parameters (hydrophobicity and size), indicating that these properties are key features recognized by the HIV-1 protease. Our cleavage prediction model provides new, important insights into the function of HIV-1 protease.

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