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Analysis of local molecular interaction networks underlying HIV-1 resistance to reverse transcriptase inhibitors.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. (Jan Komorowski's)
Institute of Computer Science, Polish Academy of Sciences.
Institute of Computer Science, Polish Academy of Sciences.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
(English)Manuscript (preprint) (Refereed)
Abstract [en]

Rapid emergence of drug resistant HIV-1 mutants is the ma jor cause of many treatment failures. A number of individual drug resistance mutations is known but the way they interact to create resistance often remains an open question. So far this question could be answered in an experimental way only. Here we apply a novel Monte Carlo feature selection-based approach to uncover molecular interaction networks that form HIV-1 reverse transcriptase (RT) resistome. By considering mutation-induced changes in the physicochemical properties of mutating amino acids, we were able to elucidate interaction networks leading to resistance to six anti-viral drugs. We selected significant properties (p − value <= 0.05) and analyzed the networks of the 20% strongest interdependencies between them. The topology of each network was validated by mapping it onto the 3D structure of RT and by relating the findings to the existing knowledge. The method can be easily applied to a wide range of similar problems in the domain of proteomics.

Keyword [en]
HIV-1 resistance, interaction networks, resistome, MCFS-ID, feature selection, interdependency discovery
National Category
Bioinformatics and Systems Biology
Research subject
Biopharmaceutics; Biology, with specialization in structural biology
Identifiers
URN: urn:nbn:se:uu:diva-109835OAI: oai:DiVA.org:uu-109835DiVA: diva2:274119
Available from: 2009-10-29 Created: 2009-10-27 Last updated: 2010-01-14Bibliographically approved
In thesis
1. From Physicochemical Features to Interdependency Networks: A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications
Open this publication in new window or tab >>From Physicochemical Features to Interdependency Networks: A Monte Carlo Approach to Modeling HIV-1 Resistome and Post-translational Modifications
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The availability of new technologies supplied life scientists with large amounts of experimental data. The data sets are large not only in terms of the number of observations, but also in terms of the number of recorded features. One of the aims of modeling is to explain a given phenomenon in possibly the simplest way, hence the need for selection of suitable features.

We extended a Monte Carlo-based approach to selecting statistically significant features with discovery of feature interdependencies and used it in modeling sequence-function relationships in proteins. Our approach led to compact and easy-to-interpret predictive models.

First, we represented protein sequences in terms of their physicochemical properties. This was followed by our feature selection and discovery of feature interdependencies. Finally, predictive models based on e.g., decision trees or rough sets were constructed.

We applied the method to model two important biological problems: 1) HIV-1 resistance to reverse transcriptase-targeted drugs and 2) post-translational modifications of proteins.

In the case of HIV resistance, we were not only able to predict whether the mutated protein is resistant to a drug or not, but we also suggested some new, previously neglected, mutations that possibly contribute to drug resistance. For all these mutations we proposed probable molecular mechanisms of action using literature and 3D structure studies.

In the case of predicting PTMs, we built high accuracy models of modifications. In comparison to other methods, we were able to resolve whether the closest neighborhood of a residue (the nanomer) is sufficient to determine its modification status. Importantly, the application of our method yields networks of interdependent physicochemical properties of amino acids that show how these properties collaborate in establishing a given modification.

We believe that the presented methods will help researchers to analyze a large class of important biological problems and will guide them in their research.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2009. 89 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 688
Keyword
bioinformatics, HIV-1, resistome analysis, drug resistance, predicting PTMs, molecular interdependency networks, MCFS-ID, feature selection, interactome, machine-learning, rough sets
National Category
Bioinformatics and Systems Biology
Research subject
Computer Science; Biology, with specialization in structural biology
Identifiers
urn:nbn:se:uu:diva-109873 (URN)978-91-554-7650-2 (ISBN)
Public defence
2009-12-15, C8:305, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2009-11-18 Created: 2009-10-28 Last updated: 2009-11-18Bibliographically approved

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