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| Research article summary (published 29 Sep 2009): |
Prediction of protein-glucose binding sites using support vector machines.
Full Abstract
Glucose is a simple sugar that plays an essential role in many basic metabolic and signaling pathways. Many proteins have binding sites that are highly specific to glucose. The exponential increase of genomic data has revealed the identity of many proteins that seem to be central to biological processes, but whose exact functions are unknown. Many of these proteins seem to be associated with disease processes. Being able to predict glucose-specific binding sites in these proteins will greatly enhance our ability to annotate protein function and may significantly contribute to drug design. We hereby present the first glucose-binding site classifier algorithm. We consider the sugar-binding pocket as a spherical spatio-chemical environment and represent it as a vector of geometric and chemical features. We then perform Random Forests feature selection to identify key features and analyze them using support vector machines classification. Our work shows that glucose binding sites can be modeled effectively using a limited number of basic chemical and residue features. Using a leave-one-out cross-validation method, our classifier achieves a 8.11% error, a 89.66% sensitivity and a 93.33% specificity over our dataset. From a biochemical perspective, our results support the relevance of ordered water molecules and ions in determining glucose specificity. They also reveal the importance of carboxylate residues in glucose binding and the high concentration of negatively charged atoms in direct contact with the bound glucose molecule.
Author information
Author/s: Nassif, Houssam (H); Al-Ali, Hassan (H); Khuri, Sawsan (S); Keirouz, Walid (W);
Affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Journal and publication information
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal: Proteins (Proteins), published in United States. (Language: eng)
Reference: 2009-Oct; vol 77 (issue 1) : pp 121-32
Dates: Created 2009/08/24; Completed 2009/10/29;
PMID: 19415755, status: MEDLINE (last retrieval date: 10/29/2009, IMS Date: )
Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.
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