Analysis of HIV resistance mutations

DFG Project within the DFG priority program

Informatikmethoden zur Analyse und Interpretation großer genomischer Datenmengen

Subject

Bioinformatics analysis of the relations between mutations in the HIV genome and phenotypic drug resistance for antiviral therapy optimization

Persons and Institutes

Niko Beerenwinkel Max Planck Institute for Informatics,
Saarbrücken
Rolf Kaiser,
Martin Däumer 		Institute of Virology,
University of Cologne
Daniel Hoffmann Center of Advanced European Studies and Research,
Bonn
Joachim Selbig Max Planck Institute of Molecular Plant Physiology,
Golm

Time scale

Beginning of the project: April 1, 2000

Description

The Human Immunodeficiency Virus (HIV) is widely believed to cause the Acquired Immunodeficiency syndrome (AIDS). Currently, for treating HIV infected patients there are two possibilities to interfere with the replication cycle of the virus: Inhibitors of the two viral enzymes protease (PR) and reverse transcriptase (RT) are available.

Since HIV shows a very high genomic variability, even under the usual combination therapy (HAART - highly active antiretroviral therapy) consisting of several drugs, mutations occur, that confer resistance to the prescribed drugs and even to drugs not yet prescribed (cross resistance). Therefore the treating physician is faced with the problem of finding a new therapy rather frequently.

Clinical trials have shown that therapy changes based on a genotypic resistance test (i. e. sequencing of PR and RT) result in a significantly better therapy success. However, the relations between observed mutations, phenotypic resistance and therapy success are poorly understood so far.

The goal of the project is to develop bioinformatics methods that help to understand these connections and that contribute directly to therapy optimization.

In a database, set up in collaboration with university hospitals and virological institutes, clinical data, sequence data and phenotypic resistance data are collected. Machine learning methods are then used to learn and predict properties like therapy success or drug resistance.

As a first result we have established a web-based service for the prediction of phenotypic drug resistance from genotypes (geno2pheno).

In a second phase of the project the mechanisms of drug resistance are studied at the molecular level. To this end we will carry out force field based calculations on enzyme-inhibitor complexes.