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Download fileCLCA: Maximum Common Molecular Substructure Queries within the MetRxn Database
journal contribution
posted on 2014-12-22, 00:00 authored by Akhil Kumar, Costas D. MaranasThe challenge of automatically identifying
the preserved molecular
moieties in a chemical reaction is referred to as the atom mapping
problem. Reaction atom maps provide the ability to locate the fate
of individual atoms across an entire metabolic network. Atom maps
are used to track atoms in isotope labeling experiments for metabolic
flux elucidation, trace novel biosynthetic routes to a target compound,
and contrast entire pathways for structural homology. However, rapid
computation of the reaction atom mappings remains elusive despite
significant research. We present a novel substructure search algorithm,
canonical labeling for clique approximation (CLCA), with polynomial
run-time complexity to quickly generate atom maps for all the reactions
present in MetRxn. CLCA uses number theory (i.e., prime factorization)
to generate canonical labels or unique IDs and identify a bijection
between the vertices (atoms) of two distinct molecular graphs. CLCA
utilizes molecular graphs generated by combining atomistic information
on reactions and metabolites from 112 metabolic models and 8 metabolic
databases. CLCA offers improvements in run time, accuracy, and memory
utilization over existing heuristic and combinatorial maximum common
substructure (MCS) search algorithms. We provide detailed examples
on the various advantages as well as failure modes of CLCA over existing
algorithms.
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Keywords
track atomsclique approximationatom mapsnovel substructure search algorithmmemory utilizationflux elucidationCLCAcanonical labelssearch algorithmsatom mapping problemchemical reactiontrace novel biosynthetic routesreaction atom mappingsnumber theoryfailure modesReaction atom mapsatomistic informationtarget compoundMaximum Common Molecular Substructure QueriesAtom mapsMetRxn DatabaseThe challengeMCS