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Please use this identifier to cite or link to this item: http://hdl.handle.net/10023/2883
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Title: Is EC class predictable from reaction mechanism?
Authors: Nath, Neetika
Mitchell, John B. O.
Keywords: QD Chemistry
Issue Date: 24-Apr-2012
Citation: Nath , N & Mitchell , J B O 2012 , ' Is EC class predictable from reaction mechanism? ' BMC Bioinformatics , vol 13 , pp. Article no 60 - .
Abstract: Background: We investigate the relationships between the EC (Enzyme Commission) class, the associated chemical reaction, and the reaction mechanism by building predictive models using Support Vector Machine (SVM), Random Forest (RF) and k-Nearest Neighbours (kNN). We consider two ways of encoding the reaction mechanism in descriptors, and also three approaches that encode only the overall chemical reaction. Both cross-validation and also an external test set are used. Results: The three descriptor sets encoding overall chemical transformation perform better than the two descriptions of mechanism. SVM and RF models perform comparably well; kNN is less successful. Oxidoreductases and hydrolases are relatively well predicted by all types of descriptor; isomerases are well predicted by overall reaction descriptors but not by mechanistic ones. Conclusions: Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. Oxidoreductases, hydrolases, and to some extent isomerases and ligases, have clear chemical signatures, making them easier to predict than transferases and lyases. We find evidence that isomerases as a class are notably mechanistically diverse and that their one shared property, of substrate and product being isomers, can arise in various unrelated ways. The performance of the different machine learning algorithms is in line with many cheminformatics applications, with SVM and RF being roughly equally effective. kNN is less successful, given the role that non-local information plays in successful classification. We note also that, despite a lack of clarity in the literature, EC number prediction is not a single problem; the challenge of predicting protein function from available sequence data is quite different from assigning an EC classification from a cheminformatics representation of a reaction.
Version: Publisher PDF
Status: Peer reviewed
URI: http://hdl.handle.net/10023/2883
http://www.biomedcentral.com/1471-2105/13/60/
DOI: http://dx.doi.org/10.1186/1471-2105-13-60
ISSN: 1471-2105
Type: Journal article
Rights: © 2012 Nath and Mitchell; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Appears in Collections:University of St Andrews Research
Chemistry Research
Biomedical Sciences Research Complex (BSRC) Research



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