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Towards proteome-wide interaction models using the proteochemometrics approach
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 Science and Technology, Biology, Department of Cell and Molecular Biology, Structural Molecular Biology.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2010 (English)In: Molecular Informatics, ISSN 1868-1743, Vol. 29, no 6-7, 499-508 p.Article in journal (Refereed) Published
Abstract [en]

A proteochemometrics model was induced from all interaction data in the BindingDB database, comprizing in all 7078 protein-ligand complexes with representatives from all major drug target categories. Proteins were represented by alignment-independent sequence descriptors holding information on properties such as hydrophobicity, charge, and secondary structure. Ligands were represented by commonly used QSAR descriptors. The inhibition constant (pK(i)) values of protein-ligand complexes were discretized into "high" and "low" interaction activity. Different machine-learning techniques were used to induce models relating protein and ligand properties to the interaction activity. The best was decision trees, which gave an accuracy of 80% and an area under the ROC curve of 0.81. The tree pointed to the protein and ligand properties, which are relevant for the interaction. As the approach does neither require alignments nor knowledge of protein 3D structures virtually all available protein-ligand interaction data could be utilized, thus opening a way to completely general interaction models that may span entire proteomes.

Place, publisher, year, edition, pages
2010. Vol. 29, no 6-7, 499-508 p.
Keyword [en]
Bioinformatics, Chemogenomics, Drug design, Protein-Ligand interactions, Proteochemometrics
National Category
Pharmaceutical Sciences Biological Sciences
Identifiers
URN: urn:nbn:se:uu:diva-89298DOI: 10.1002/minf.201000052ISI: 000280908200004OAI: oai:DiVA.org:uu-89298DiVA: diva2:159947
Available from: 2009-02-10 Created: 2009-02-10 Last updated: 2013-04-12Bibliographically approved
In thesis
1. Chemogenomics: Models of Protein-Ligand Interaction Space
Open this publication in new window or tab >>Chemogenomics: Models of Protein-Ligand Interaction Space
2009 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. This has stimulated the development of chemogenomics and proteochemometrics (PCM) that take target information as well as ligand information into account to study the genomic effect of potential drugs.

This thesis is concerned with modeling protein-ligand recognition, and the aim is to develop models that generalize to the entire protein-ligand space. To this end, protein-ligand interaction data has been extracted and manually curated from public databases, protein and ligand descriptors have been computed, and predictive models have been induced with machine-learning methods.

An introduction to chemogenomics, machine learning, and PCM modeling is given in the thesis summary, which is followed by five research papers. Paper I shows that it is possible to induce interpretable models with a non-linear rule-based method, and paper II demonstrates that local descriptors of protein structure may be used to induce PCM models that cover proteins differing in sequence and fold. In paper III, such local descriptors are used to induce a PCM model on a large dataset that includes all major enzyme classes. This demonstrates that the local descriptors may be used to induce generalized models that span the entire known structural enzyme-ligand space. Paper IV describes a step towards proteome-wide PCM models, and shows that it is possible to predict high- and low-affinity complexes using a set of protein and ligand descriptors that do not require knowledge of 3D structure. Finally, paper V presents a method to visualize and compare protein-ligand chemogenomic subspaces, which may be used to predict unwanted cross-interactions of drugs with other proteins in the proteome.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2009. 54 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 608
Identifiers
urn:nbn:se:uu:diva-89299 (URN)978-91-554-7430-0 (ISBN)
Public defence
2009-03-27, C8:305, Biomedical Centre, Uppsala, 13:00 (English)
Opponent
Supervisors
Available from: 2009-03-05 Created: 2009-02-10 Last updated: 2009-06-02Bibliographically approved

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Strömbergsson, HelenaLapins, MarisKleywegt, Gerard J.Wikberg, Jarl E. S.

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