| A Bayesian molecular interaction library. | |
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MedLine Citation:
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PMID: 14677639 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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We describe a library of molecular fragments designed to model and predict non-bonded interactions between atoms. We apply the Bayesian approach, whereby prior knowledge and uncertainty of the mathematical model are incorporated into the estimated model and its parameters. The molecular interaction data are strengthened by narrowing the atom classification to 14 atom types, focusing on independent molecular contacts that lie within a short cutoff distance, and symmetrizing the interaction data for the molecular fragments. Furthermore, the location of atoms in contact with a molecular fragment are modeled by Gaussian mixture densities whose maximum a posteriori estimates are obtained by applying a version of the expectation-maximization algorithm that incorporates hyperparameters for the components of the Gaussian mixtures. A routine is introduced providing the hyperparameters and the initial values of the parameters of the Gaussian mixture densities. A model selection criterion, based on the concept of a 'minimum message length' is used to automatically select the optimal complexity of a mixture model and the most suitable orientation of a reference frame for a fragment in a coordinate system. The type of atom interacting with a molecular fragment is predicted by values of the posterior probability function and the accuracy of these predictions is evaluated by comparing the predicted atom type with the actual atom type seen in crystal structures. The fact that an atom will simultaneously interact with several molecular fragments forming a cohesive network of interactions is exploited by introducing two strategies that combine the predictions of atom types given by multiple fragments. The accuracy of these combined predictions is compared with those based on an individual fragment. Exhaustive validation analyses and qualitative examples (e.g., the ligand-binding domain of glutamate receptors) demonstrate that these improvements lead to effective modeling and prediction of molecular interactions. |
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Authors:
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Ville-Veikko Rantanen; Mats Gyllenberg; Timo Koski; Mark S Johnson |
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Publication Detail:
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Type: Journal Article; Research Support, Non-U.S. Gov't |
Journal Detail:
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Title: Journal of computer-aided molecular design Volume: 17 ISSN: 0920-654X ISO Abbreviation: J. Comput. Aided Mol. Des. Publication Date: 2003 Jul |
Date Detail:
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Created Date: 2003-12-17 Completed Date: 2004-08-13 Revised Date: 2006-11-15 |
Medline Journal Info:
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Nlm Unique ID: 8710425 Medline TA: J Comput Aided Mol Des Country: Netherlands |
Other Details:
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Languages: eng Pagination: 435-61 Citation Subset: IM |
Affiliation:
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Department of Mathematics, University of Turku, FIN-20014 Turku, Finland. vira@utu.fi |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Algorithms Automation Bayes Theorem* Drug Design Ligands Models, Molecular Normal Distribution Peptide Library* Probability |
| Chemical | |
Reg. No./Substance:
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0/Ligands; 0/Peptide Library |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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