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Exploring interaction space of retroviral proteases reveals general patterns for resistance-improved HIV retardants
Uppsala University, Teknisk-naturvetenskapliga vetenskapsområdet, Faculty of Science and Technology, Biology, The Linnaeus Centre for Bioinformatics.
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Article in journal (Refereed) Submitted
URN: urn:nbn:se:uu:diva-97334OAI: oai:DiVA.org:uu-97334DiVA: diva2:172221
Available from: 2008-05-15 Created: 2008-05-15Bibliographically approved
In thesis
1. Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach: Applications for Drug Discovery and Development
Open this publication in new window or tab >>Modeling the Interaction Space of Biological Macromolecules: A Proteochemometric Approach: Applications for Drug Discovery and Development
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Molecular interactions lie at the heart of myriad biological processes. Knowledge of molecular recognition processes and the ability to model and predict interactions of any biological molecule to any chemical compound are the key for better understanding of cell functions and discovery of more efficacious medicines.

This thesis presents contributions to the development of a novel chemo-bioinformatics approach called proteochemometrics; a general method for interaction space analysis of biological macromolecules and their ligands. In this work we explore proteochemometrics-based interaction models over broad groups of protein families, evaluate their validity and scope, and compare proteochemometrics to traditional modeling approaches.

Through the proteochemometric analysis of large interaction data sets of multiple retroviral proteases from various viral species we investigate complex mechanisms of drug resistance in HIV-1 and discover general physicochemical determinants of substrate cleavage efficiency and binding in retroviral proteases. We further demonstrate how global proteochemometric models can be used for design of protease inhibitors with broad activity on drug-resistant viral mutants, for monitoring drug resistance mechanisms in the physicochemical sense and prediction of potential HIV-1 evolution trajectories. We provide novel insights into the complexity of HIV-1 protease specificity by constructing a generalized IF-THEN rule model based on bioinformatics analysis of the largest set of HIV-1 protease substrates and non-substrates.

We discuss how proteochemometrics can be used to map recognition sites of entire protein families in great detail and demonstrate how it can incorporate target variability into drug discovery process. Finally, we assess the utility of the proteochemometric approach in evaluation of ADMET properties of drug candidates with a special focus on inhibition of cytochrome P450 enzymes and investigate application of the approach in the pharmacogenomics field.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 77 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 444
Bioinformatics, proteochemometrics, bioinformatics, chemoinformatics, chemical space, QSAR, retroviral proteases, HIV-1, drug resistance, pharmacogenomics, cytochrome P450, GPCRs, melanocortin receptors, interactome, machine-learning, rough sets, Bioinformatik
urn:nbn:se:uu:diva-8916 (URN)978-91-554-7229-0 (ISBN)
Public defence
2008-06-04, C10:305, BMC, Husargatan 3, Uppsala, 09:00
Available from: 2008-05-15 Created: 2008-05-15Bibliographically approved

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