Is EC class predictable from reaction mechanism?

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...

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Veröffentlicht in:BMC bioinformatics Jg. 13; H. 1; S. 60
Hauptverfasser: Nath, Neetika, Mitchell, John BO
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London BioMed Central 24.04.2012
BioMed Central Ltd
Springer Nature B.V
BMC
Schlagworte:
Algorithms
Analysis
Artificial Intelligence
Bioinformatics
Biomedical and Life Sciences
Chemical reactions
Chemistry
Classification
Cluster Analysis
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Data mining
Databases, Protein
Enzymes
Enzymes - chemistry
Enzymes - classification
Enzymes - metabolism
Life Sciences
Machine learning
Microarrays
Models, Chemical
Oxidases
Prediction models
Research Article
Studies
Support Vector Machine
Terminology as Topic
United Kingdom
Support Vector Machine
Descriptor Space
Radial Basis Function
Random Forest
Enzyme Commission
ISSN:1471-2105, 1471-2105
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Zusammenfassung: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/1471-2105-13-60