IGERS: Inferring Gibbs Energy Changes of Biochemical Reactions from Reaction Similarities

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (Delta G(0)) of reactions and the concentration ranges of their reactants. Currently, experimental Delta G(0) values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown Delta G(0) value of a reaction from the known Delta G(0) value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined Delta G(0) values, we found that a minimum reaction similarity of T = 0.6 is required to infer Delta G(0) values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign Delta G(0) values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of Delta G(0) measurements required for a full coverage of a given reaction network with reliable Delta G(0) values.