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Predicting Novel Metabolic Pathways through Subgraph Mining

Aravind Sankar, Sayan Ranu, Karthik Raman
doi: https://doi.org/10.1101/123877
Aravind Sankar
1Dept. of CSE, IIT Madras, Chennai, India 600036, ,
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  • For correspondence: aravindsankar28@gmail.com sayanranu@cse.iitd.ac.in
Sayan Ranu
1Dept. of CSE, IIT Madras, Chennai, India 600036, ,
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  • For correspondence: aravindsankar28@gmail.com sayanranu@cse.iitd.ac.in
Karthik Raman
2Dept. of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras Chennai, India 600036,
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  • For correspondence: kraman@iitm.ac.in
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Abstract

The ability to predict pathways for biosynthesis of metabolites is very important in metabolic engineering. It is possible to mine the repertoire of biochemical transformations from reaction databases, and apply the knowledge to predict reactions to synthesize new molecules. However, this usually involves a careful understanding of the mechanism and the knowledge of the exact bonds being created and broken. There is clearly a need for a method to rapidly predict reactions for synthesizing new molecules, which relies only on the structures of the molecules, without demanding additional information such as thermodynamics or hand-curated information such as atom-atom mapping, which are often hard to obtain accurately.

We here describe a robust method based on subgraph mining, to predict a series of biochemical transformations, which can convert between two (even previously unseen) molecules. We first describe a reliable method based on subgraph edit distance to map reactants and products, using only their chemical structures. Having mapped reactants and products, we identify the reaction centre and its neighbourhood, the reaction signature, and store this in a reaction rule network. This novel representation enables us to rapidly predict pathways, even between previously unseen molecules. We also propose a heuristic that predominantly recovers natural biosynthetic pathways from amongst hundreds of possible alternatives, through a directed search of the reaction rule network, enabling us to provide a reliable ranking of the different pathways. Our approach scales well, even to databases with > 100,000 reactions. A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner

CCS CONCEPTS •Information systems →Data mining; •Applied computing →Bioinformatics;

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Posted April 04, 2017.
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Predicting Novel Metabolic Pathways through Subgraph Mining
Aravind Sankar, Sayan Ranu, Karthik Raman
bioRxiv 123877; doi: https://doi.org/10.1101/123877
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Predicting Novel Metabolic Pathways through Subgraph Mining
Aravind Sankar, Sayan Ranu, Karthik Raman
bioRxiv 123877; doi: https://doi.org/10.1101/123877

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