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Enzyme Function Prediction with Interpretable Models

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Computational Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 541))

Abstract

Enzymes play central roles in metabolic pathways, and the prediction of metabolic pathways in newly sequenced genomes usually starts with the assignment of genes to enzymatic reactions. However, genes with similar catalytic activity are not necessarily similar in sequence, and therefore the traditional sequence similarity-based approach often fails to identify the relevant enzymes, thus hindering efforts to map the metabolome of an organism.

Here we study the direct relationship between basic protein properties and their function. Our goal is to develop a new tool for functional prediction (e.g., prediction of Enzyme Commission number), which can be used to complement and support other techniques based on sequence or structure information. In order to define this mapping we collected a set of 453 features and properties that characterize proteins and are believed to be related to structural and functional aspects of proteins. We introduce a mixture model of stochastic decision trees to learn the set of potentially complex relationships between features and function. To study these correlations, trees are created and tested on the Pfam classification of proteins, which is based on sequence, and the EC classification, which is based on enzymatic function. The model is very effective in learning highly diverged protein families or families that are not defined on the basis of sequence. The resulting tree structures highlight the properties that are strongly correlated with structural and functional aspects of protein families, and can be used to suggest a concise definition of a protein family.

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Acknowledgments

This work is supported by the National Science Foundation under Grant No. 0133311 to Golan Yona.

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Authors and Affiliations

  1. Department of Computer Science, Princeton University, Princeton, NJ, USA

    Umar Syed

  2. Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY, USA

    Golan Yona

  3. Department of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel

    Golan Yona

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  1. Umar Syed
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    © 2009 Humana Press, a part of Springer Science+Business Media, LLC

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    Syed, U., Yona, G. (2009). Enzyme Function Prediction with Interpretable Models. In: Ireton, R., Montgomery, K., Bumgarner, R., Samudrala, R., McDermott, J. (eds) Computational Systems Biology. Methods in Molecular Biology, vol 541. Humana Press. https://doi.org/10.1007/978-1-59745-243-4_17

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    • DOI: https://doi.org/10.1007/978-1-59745-243-4_17

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    • Publisher Name: Humana Press

    • Print ISBN: 978-1-58829-905-5

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