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Mapping of ligand-binding cavities in proteins
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC), KBC)
Umeå University, Faculty of Science and Technology, Department of Chemistry. (Computational Life Science Cluster (CLiC), KBC)
2010 (English)In: Proteins: Structure, Function, and Genetics, ISSN 0887-3585, E-ISSN 1097-0134, Vol. 78, no 6, 1408-1422 p.Article in journal (Refereed) Published
Abstract [en]

The complex interactions between proteins and small organic molecules (ligands) are intensively studied because they play key roles in biological processes and drug activities. Here, we present a novel approach to characterize and map the ligand-binding cavities of proteins without direct geometric comparison of structures, based on Principal Component Analysis of cavity properties (related mainly to size, polarity, and charge). This approach can provide valuable information on the similarities and dissimilarities, of binding cavities due to mutations, between-species differences and flexibility upon ligand-binding. The presented results show that information on ligand-binding cavity variations can complement information on protein similarity obtained from sequence comparisons. The predictive aspect of the method is exemplified by successful predictions of serine proteases that were not included in the model construction. The presented strategy to compare ligand-binding cavities of related and unrelated proteins has many potential applications within protein and medicinal chemistry, for example in the characterization and mapping of "orphan structures", selection of protein structures for docking studies in structure-based design, and identification of proteins for selectivity screens in drug design programs.

Place, publisher, year, edition, pages
John Wiley & Sons, Inc , 2010. Vol. 78, no 6, 1408-1422 p.
Keyword [en]
protein cavity comparison, physicochemical properties, alignment independent, SCREEN, principal component analysis, binding sites, medicinal chemistry, drug design, PCA clustering tree, bioinformatics
National Category
Chemical Sciences
URN: urn:nbn:se:umu:diva-33671DOI: 10.1002/prot.22655ISI: 000276369700005PubMedID: 20034113OAI: diva2:317096
Available from: 2010-05-03 Created: 2010-05-03 Last updated: 2011-12-21Bibliographically approved
In thesis
1. Multivariate design of molecular docking experiments: An investigation of protein-ligand interactions
Open this publication in new window or tab >>Multivariate design of molecular docking experiments: An investigation of protein-ligand interactions
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To be able to make informed descicions regarding the research of new drug molecules (ligands), it is crucial to have access to information regarding the chemical interaction between the drug and its biological target (protein). Computer-based methods have a given role in drug research today and, by using methods such as molecular docking, it is possible to investigate the way in which ligands and proteins interact. Despite the acceleration in computer power experienced in the last decades many problems persist in modelling these complicated interactions. The main objective of this thesis was to investigate and improve molecular modelling methods aimed to estimate protein-ligand binding. In order to do so, we have utilised chemometric tools, e.g. design of experiments (DoE) and principal component analysis (PCA), in the field of molecular modelling. More specifically, molecular docking was investigated as a tool for reproduction of ligand poses in protein 3D structures and for virtual screening. Adjustable parameters in two docking software were varied using DoE and parameter settings were identified which lead to improved results. In an additional study, we explored the nature of ligand-binding cavities in proteins since they are important factors in protein-ligand interactions, especially in the prediction of the function of newly found proteins. We developed a strategy, comprising a new set of descriptors and PCA, to map proteins based on their cavity physicochemical properties. Finally, we applied our developed strategies to design a set of glycopeptides which were used to study autoimmune arthritis. A combination of docking and statistical molecular design, synthesis and biological evaluation led to new binders for two different class II MHC proteins and recognition by a panel of T-cell hybridomas. New and interesting SAR conclusions could be drawn and the results will serve as a basis for selection of peptides to include in in vivo studies.

Place, publisher, year, edition, pages
Umeå: Umeå universitet. Kemiska institutionen, 2010
Molecular docking, chemometrics, multivariate analysis, principal component analysis, PCA, design of experiments, DoE, partial least-square projections to latent structures, PLS, scoring functions, ligand-binding cavity, major histocompatibility complex, MHC, glycopeptide, T-cell.
National Category
Medicinal Chemistry
Research subject
urn:nbn:se:umu:diva-35736 (URN)978-91-7459-065-4 (ISBN)
Public defence
2010-10-01, Naturvetarhuset, N360, Umeå universitet, Umeå, 10:00 (Swedish)
Available from: 2010-09-09 Created: 2010-09-01 Last updated: 2011-03-24Bibliographically approved

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Andersson, David C.Linusson Jonsson, Anna
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