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Bioinformatics-assisted anti-HIV therapy


Highly active antiretroviral therapy (HAART), in which three or more drugs are given in combination, has substantially improved the clinical management of HIV-1 infection. Still, the emergence of drug-resistant variants eventually leads to therapy failure in most patients. In such a scenario, the high diversity of resistance-associated mutational patterns complicates the choice of an optimal follow-up regimen. To support physicians in this task, a range of bioinformatics tools for predicting drug resistance or response to combination therapy from the viral genotype have been developed. With several free and commercial software services available, computational advice is rapidly gaining acceptance as an important element of rational decision-making in the treatment of HIV infection.

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Figure 1: Location of resistance mutations in the HIV genome.
Figure 2: Dose-response curve for zidovudine, determined using an in vitro recombinant assay.
Figure 3: The geno2pheno tool.
Figure 4: Mixtures of mutagenetic trees for the development of resistance against zidovudine.

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We are indebted to M. Zazzi, R. Harrigan, and three anonymous referees for their detailed and helpful comments. Thanks to A. Altmann and M. Däumer for help on preparing Figure 4. We thank R.W. Shafer, B. Larder, A. Altmann and N. Beerenwinkel for allowing us to discuss their submitted manuscripts. Our work is supported by the European Union, as part of the EuResist project.

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Correspondence to Thomas Lengauer.

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Related links

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Entrez Genome






FDA approved drugs used in the treatment of HIV


HIV Resistance Response Database Initiative

PIRSpred — protein inhibitor resistance/susceptibility prediction

Stanford University HIV Drug Resistance Database

The Forum for Collaborative HIV Research




Classification problem

A classification problem aims to categorize an input x into one of a finite set of categories. Often only two categories are used. For example, the categories could be resistant or susceptible (or resistant, intermediate or susceptible) if the input is a viral genotype together with a drug, based on suitable resistance-factor cutoffs.


A method for assessing the prediction accuracy of a statistical model. The training data set is divided into, for example, ten equal-sized parts. The statistical model is derived ('learned') on nine parts and is then tested on the tenth part. In ten trials, each part is used in turn for testing.

Drug activity

A value that is reciprocal to viral resistance. Drug activity can be normalized, thereby allowing comparison between different drugs (for example, drug activity ranging from 0 (not active) to 1 (maximally active)).

Homology-based modelling method

A method that allows the three-dimensional structure of a 'query' protein to be modelled on that of a structurally known 'template' protein.


A resensitization effect in which a virus encoding resistance mutations becomes more susceptible to a drug than the reference virus.

Molecular docking method

A procedure by which the conformation of a ligand that interacts with the binding site of a structurally known protein is computed.

Molecular dynamics simulation

A time-consuming computational method that simulates molecular movements by computing the forces that act on the molecules.

Regression problem

A regression problem aims to approximate a real-valued output y = f(x) given the input x, and based on a set of training data (xi, yi). Here, the input is a viral genotype together with a drug, and the output is a quantitative measure of the resistance of the virus against the drug, such as the resistance factor of the virus.


An effect by which a mutation that confers resistance to one drug can increase the susceptibility of the virus to another drug.

Resistance-factor cutoff

A number that is used to assign a resistance category to a virus, based on its resistance factor. For example, to categorize viruses into susceptible, intermediate or resistant, two resistance-factor cutoffs (c1, c2) are chosen. The virus is said to be susceptible if its resistance factor is below c1, and is resistant if the resistance factor is above c2. Otherwise, the virus has intermediate resistance.

Rule-based algorithm

An algorithmic decision procedure that is based on identified resistance mutations and coded in the form of sets of propositional rules. Although rule sets can be learned using statistical learning methods, it is current practice in the HIV community to assemble them using panels of human experts.

Statistical learning methods

Algorithms for deriving computational models that can predict unseen output from available input. Such models are derived from sets of 'training data'. The training data comprise inputs together with their associated known outputs.

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Lengauer, T., Sing, T. Bioinformatics-assisted anti-HIV therapy. Nat Rev Microbiol 4, 790–797 (2006).

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