Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism
Heinonen Markus, Lappalainen Sampsa, Taneli Mielikäinen and Juho Rousu
Journal of Computational Biology
The ability to trace the fate of individual atoms through the metabolic
pathways is needed in many applications of systems biology and drug
discovery. However, this information is not immediately available from the
most common metabolome studies and needs to be separately acquired.
Automatic discovery of correspondence of atoms in biochemical reactions
is called the atom mapping problem.
We suggest an effcient approach for solving the atom mapping problem
exactly finding mappings of minimum edge edit distance. The algorithm
is based on A* search equipped with sophisticated heuristics for pruning
the search space. This approach has clear advantages over the commonly
used heuristic approach of iterative maximum common subgraph (MCS)
algorithm: we explicitly minimize an objective function, we produce so-
lutions that typically require less manual curation. The two methods are
similar in computational resource demands.
We compare the performance of the proposed algorithm against sev-
eral alternatives on data obtained from the KEGG LIGAND and RPAIR
databases: greedy search, bi-partite graph matching and the MCS ap-
proach. Our experiments show that alternative approaches often fail in
nding mappings with minimum edit distance.
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Juho Rousu|
|Deposited On:||08 March 2010|