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A chemogenomics view on protein-ligand spaces
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 Science and Technology, Biology, Department of Cell and Molecular Biology.
2009 (English)In: BMC Bioinformatics, ISSN 1471-2105, Vol. 10, no Suppl.6, S13- p.Article in journal (Refereed) Published
Abstract [en]

BACKGROUND: Chemogenomics is an emerging inter-disciplinary approach to drug discovery that combines traditional ligand-based approaches with biological information on drug targets and lies at the interface of chemistry, biology and informatics. The ultimate goal in chemogenomics is to understand molecular recognition between all possible ligands and all possible drug targets. Protein and ligand space have previously been studied as separate entities, but chemogenomics studies deal with large datasets that cover parts of the joint protein-ligand space. Since drug discovery has traditionally focused on ligand optimization, the chemical space has been studied extensively. The protein space has been studied to some extent, typically for the purpose of classification of proteins into functional and structural classes. Since chemogenomics deals not only with ligands but also with the macromolecules the ligands interact with, it is of interest to find means to explore, compare and visualize protein-ligand subspaces. RESULTS: Two chemogenomics protein-ligand interaction datasets were prepared for this study. The first dataset covers the known structural protein-ligand space, and includes all non-redundant protein-ligand interactions found in the worldwide Protein Data Bank (PDB). The second dataset contains all approved drugs and drug targets stored in the DrugBank database, and represents the approved drug-drug target space. To capture biological and physicochemical features of the chemogenomics datasets, sequence-based descriptors were computed for the proteins, and 0, 1 and 2 dimensional descriptors for the ligands. Principal component analysis (PCA) was used to analyze the multidimensional data and to create global models of protein-ligand space. The nearest neighbour method, computed using the principal components, was used to obtain a measure of overlap between the datasets. CONCLUSION: In this study, we present an approach to visualize protein-ligand spaces from a chemogenomics perspective, where both ligand and protein features are taken into account. The method can be applied to any protein-ligand interaction dataset. Here, the approach is applied to analyze the structural protein-ligand space and the protein-ligand space of all approved drugs and their targets. We show that this approach can be used to visualize and compare chemogenomics datasets, and possibly to identify cross-interaction complexes in protein-ligand space.

Place, publisher, year, edition, pages
2009. Vol. 10, no Suppl.6, S13- p.
National Category
Natural Sciences
URN: urn:nbn:se:uu:diva-89297DOI: 10.1186/1471-2105-10-S6-S13ISI: 000267522200013PubMedID: 19534738OAI: oai:DiVA.org:uu-89297DiVA: diva2:159946
Available from: 2009-02-10 Created: 2009-02-10 Last updated: 2011-03-12Bibliographically 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.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 608
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)
Available from: 2009-03-05 Created: 2009-02-10 Last updated: 2009-06-02Bibliographically approved

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