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Computational proteomics analysis of HIV-1 protease interactome
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
2007 (English)In: Proteins: Structure, Function, and Bioinformatics, ISSN 0887-3585, Vol. 68, no 1, 305-312 p.Article in journal (Refereed) Published
Abstract [en]

HIV-1 protease is a small homodimeric enzyme that ensures maturation of HIV virions by cleaving the viral precursor Gag and Gag-Pol polyproteins into structural and functional elements. The cleavage sites in the viral polyproteins share neither sequence homology nor binding motif and the specificity of the HIV-1 protease is therefore only partially understood. Using an extensive data set collected from 16 years of HIV proteome research we have here created a general and predictive rule-based model for HIV-1 protease specificity based on rough sets. We demonstrate that HIV-1 protease specificity is much more complex than previously anticipated, which cannot be defined based solely on the amino acids at the substrate's scissile bond or by any other single substrate amino acid position only. Our results show that the combination of at least three particular amino acids is needed in the substrate for a cleavage event to occur. Only by combining and analyzing massive amounts of HIV proteome data it was possible to discover these novel and general patterns of physico-chemical substrate cleavage determinants. Our study is an example how computational biology methods can advance the understanding of the viral interactomes.

Place, publisher, year, edition, pages
2007. Vol. 68, no 1, 305-312 p.
Keyword [en]
viral proteomics, bioinformatics, protein-peptide interactions, HIV-1 protease specificity, viral complexity
National Category
Pharmaceutical Sciences Biological Sciences Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-97332DOI: 10.1002/prot.21415ISI: 000246894800031PubMedID: 17427231OAI: oai:DiVA.org:uu-97332DiVA: diva2:172219
Available from: 2008-05-15 Created: 2008-05-15 Last updated: 2011-02-09Bibliographically 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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 444
Keyword
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
Identifiers
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
Opponent
Supervisors
Available from: 2008-05-15 Created: 2008-05-15Bibliographically approved

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