Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Abstract

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

“…With the loose compound coloring identifiers generated by the neighborhood-specific graph coloring method, about 8865 compound pairs were detected, including both generic and specific compound pairs [ 25 ]. However, some cases were not solved perfectly by the loose compound coloring identifiers.…”

“…Our neighborhood-specific graph coloring method can derive atom identifiers for every atom in a specific compound with consideration of molecular symmetry, facilitating the construction of an atom-resolved metabolic network [ 25 ]. Furthermore, a unique compound coloring identifier can be generated based on the atom identifiers, which can be used for compound harmonization across metabolic databases.…”

Benchmark dataset for training machine learning models to predict the pathway involvement of metabolites

“…Beyond the ability to predict the pathway involvement of metabolites, there is also the need to determine which molecular substructures within the metabolites are most im-portant for this prediction. Rather than training black box models, Jin et al [16] presents an atom coloring method which generates molecular structure representations of com-pounds which when used as features for a machine learning tabular dataset, we can de-termine which molecular substructures are most associated with pathway involvement by measuring feature importance. In this work, we use the atom coloring method to gen-erate the benchmark dataset and train three different types of machine learning models i.e.…”

md_harmonize: a Python package for atom-level harmonization of public metabolic databases

“…Metabolic databases, like KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc, contain either atom transformation patterns between reactant-product pairs (Kotera, et al, 2004) or direct atom mappings for reactions (Latendresse, et al, 2012), which greatly contribute to the construction of an atom-re-solved metabolic network. However, the lack of a uniform identity, especially for the atom identifiers, is a big challenge in integrating metabolic databases (Jin, et al, 2020; Jin and Moseley, 2021; Poolman, et al, 2006; Powers, 2009).…”

“…A neighborhood-specific graph coloring method was developed to generate unique identifiers for every atom and the corresponding compound based on the structure (Jin, et al, 2020). This method requires only the molfile representation of the compound, which is available in most metabolic databases.…”