Mardia, KV, Nyirongo, VB, Fallaize, CJ et al. (2 more authors) (2011) Hierarchical bayesian modeling of pharmacophores in bioinformatics. Biometrics, 67 (2). 611 - 619. ISSN 0006-341X
Abstract
One of the key ingredients in drug discovery is the derivation of conceptual templates called pharmacophores. A pharmacophore model characterizes the physicochemical properties common to all active molecules, called ligands, bound to a particular protein receptor, together with their relative spatial arrangement. Motivated by this important application, we develop a Bayesian hierarchical model for the derivation of pharmacophore templates from multiple configurations of point sets, partially labeled by the atom type of each point. The model is implemented through a multistage template hunting algorithm that produces a series of templates that capture the geometrical relationship of atoms matched across multiple configurations. Chemical information is incorporated by distinguishing between atoms of different elements, whereby different elements are less likely to be matched than atoms of the same element. We illustrate our method through examples of deriving templates from sets of ligands that all bind structurally related protein active sites and show that the model is able to retrieve the key pharmacophore features in two test cases.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2011, Wiley. This is an author produced version of a paper published in Biometrics. Uploaded in accordance with the publisher's self-archiving policy This is the accepted version of the following article: Mardia, KV, Nyirongo, VB, Fallaize, CJ, Barber, S and Jackson, RM (2011) Hierarchical bayesian modeling of pharmacophores in bioinformatics. Biometrics, 67 (2). 611 - 619. ISSN 0006-341X, which has been published in final form at http://dx.doi.org/10.1111/j.1541-0420.2010.01460.x. |
Keywords: | Algorithms; Bayes Theorem; Biometry; Catalytic domain; Computational biology; Drug design; Drug discovery; Proteins; Structure-activity relationship; Chemoinformatics; Ligands; Markov chain Monte Carlo; Multiple alignment; Pharmacophore; Shape analysis; Spatial matching; Template |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 16 Apr 2014 09:56 |
Last Modified: | 23 Jun 2023 21:39 |
Published Version: | http://dx.doi.org/10.1111/j.1541-0420.2010.01460.x |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1111/j.1541-0420.2010.01460.x |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:78449 |