AI Article Synopsis
- The ability to predict how new molecules can be synthesized through biochemical transformations is essential for metabolic engineering, but current methods often require detailed knowledge of reaction mechanisms, which can be difficult to gather.
- A new method using subgraph mining allows for the mapping and prediction of reactions solely based on the chemical structures of reactants and products, enabling the identification of reaction pathways even for unknown molecules.
- This approach has demonstrated the capability to accurately predict natural biosynthetic pathways and is accessible via a Java implementation at the provided GitHub link.
Article Abstract
Motivation: 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 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 reactant mapping, which are often hard to obtain accurately.
Results: 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 demonstrate this ability by predicting pathways to molecules not present in the KEGG database. 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.
Availability And Implementation: A Java-based implementation of our algorithms is available at https://github.com/RamanLab/ReactionMiner.
Contact: sayanranu@cse.iitd.ac.in or kraman@iitm.ac.in.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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