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Improved approach for proteochemometrics modeling: application to organic compound--amine G protein-coupled receptor interactions
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences, Pharmaceutical Pharmacology. (Proteochemometric group)
2005 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 21, no 23, 4289-4296 p.Article in journal (Refereed) Published
Abstract [en]

MOTIVATION: Proteochemometrics is a novel technology for the analysis of interactions of series of proteins with series of ligands. We have here customized it for analysis of large datasets and evaluated it for the modeling of the interaction of psychoactive organic amines with all the five known families of amine G protein-coupled receptors (GPCRs). RESULTS: The model exploited data for the binding of 22 compounds to 31 amine GPCRs, correlating chemical descriptions and cross-descriptions of compounds and receptors to binding affinity using a novel strategy. A highly valid model (q2 = 0.76) was obtained which was further validated by external predictions using data for 10 other entirely independent compounds, yielding the high q2ext = 0.67. Interpretation of the model reveals molecular interactions that govern psychoactive organic amines overall affinity for amine GPCRs, as well as their selectivity for particular amine GPCRs. The new modeling procedure allows us to obtain fully interpretable proteochemometrics models using essentially unlimited number of ligand and protein descriptors.

Place, publisher, year, edition, pages
2005. Vol. 21, no 23, 4289-4296 p.
Keyword [en]
Amines/chemistry, Binding Sites, Chemistry; Organic/*methods, Cluster Analysis, Databases; Factual, Drug Interactions, Hydrogen-Ion Concentration, Least-Squares Analysis, Ligands, Models; Biological, Models; Chemical, Models; Molecular, Models; Statistical, Models; Theoretical, Mutagenesis, Pharmacology/methods, Protein Binding, Proteomics/*methods, Receptors; G-Protein-Coupled/*chemistry, Research Support; Non-U.S. Gov't
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-104274DOI: 10.1093/bioinformatics/bti703PubMedID: 16204343OAI: oai:DiVA.org:uu-104274DiVA: diva2:219586
Available from: 2009-05-28 Created: 2009-05-28 Last updated: 2017-12-13Bibliographically approved

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