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Computing double-pushout graph transformation rules and atom-to-atom maps from KEGG RCLASS data
Nora Beier1, Thomas Gatter1, Jakob L Andersen2
1Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16-18, D-04107, Leipzig, Germany.
Algorithms for Molecular Biology : AMB
|January 30, 2026
Summary
The new laveau software generates explicit DPO rules and atom-to-atom maps from KEGG RCLASS data, enabling detailed atom-level models of metabolic networks.
Area of Science:
- Bioinformatics
- Computational Chemistry
- Systems Biology
Background:
- Atom-to-atom maps are crucial for many applications but difficult to obtain.
- KEGG reaction database uses RCLASSes, not direct atom-to-atom maps, hindering rule construction.
- DPO graph transformation rules offer an efficient representation for atom-level mapping.
Purpose of the Study:
- To develop a method for converting KEGG RCLASS data into DPO rules.
- To enable the generation of explicit atom-to-atom maps from existing KEGG reaction data.
Main Methods:
- Developed 'laveau', a tool to compute DPO rules from KEGG reactions and RCLASS data.
- Algorithm translates RDM codes to RDM pattern graphs, merges them based on embeddings, and forms reactant/product subgraphs.
- Atom-to-atom maps are derived from RDM codes to define DPO transformation rules.
Main Results:
- laveau successfully generated 1232 DPO rules and 1594 atom-to-atom maps from 3195 RCLASSes.
- The generated DPO rules applied to reactants yield complete atom-to-atom maps.
- The tool effectively reconstructs atom-level details from RCLASS data.
Conclusions:
- laveau software extracts local atom-to-atom maps from KEGG RCLASSes for enzyme-catalyzed reactions.
- Provides DPO rules for atom-level metabolic network models, addressing a data gap.
- Facilitates detailed analysis of biochemical transformations at the atomic level.
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