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EMBnet
Volume 14 Nr. 3
Abstract 57 - GIBBS FREE ENERGY CHANGES OF BIOCHEMICAL REACTIONS INFERRED FROM REACTION SIMILARITIES Rother Kristian*[1], Hofmann Sabrina[2], Bulik Sascha[2], Hoppe Andreas[2], Holzhuetter Herrmann-Georg[2] - [1]Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology ~ Warsaw ~ Poland - [2]Computational Biophysics Group, Institute of Biochemistry, Charite Universit채tsmedizin ~ Berlin ~ Germany # 1E) System Biology
Motivation: An indispensable prerequisite for the thermodynamic and kinetic modeling of biochemical reaction networks is to assign a reliable value for the standard Gibbs free energy change (DeltaG0) to each reaction and transporter. However, for genome-wide metabolic networks experimental DeltaG0 values are scarce. Here we propose a novel computational method to infer the unknown DeltaG0 value of a reaction from known DeltaG0 values of chemically similar reactions. Methods: To quantify the chemical similarity of biochemical reactions we have established a detailed classification procedure that assigns 3304 different chemical attributes to atomic groups occurring in presently characterized biochemical metabolites. Changes in these attributes between the substrate and product molecules are tracked on a per-atom basis and similarities between these reactionspecific attribute changes are assessed by the Tanimoto coefficient (T) assuming values between 0 (complete dissimilarity of reactions compared) and 1 (identity of reactions compared). Results: Testing our method across a set of 1546 biochemical reactions 216 of which being covered by experimentally determined DeltaG0 values - the root-meansquare distance (RMSD) between predicted and measured DeltaG0 values amounted to 8.0 kJ/mol, if a minimum similarity of T>0.6 to reactions with known DeltaG0 values is assumed. This value is significantly smaller than the RMSD of 10.5 kJ/mol achieved with the commonly used group contribution method. However, for less similar reactions, the group contribution method produces a more accurate predictions and a combination of both approaches is proposed. Clustering all reactions of a given metabolic network according to chemical similarity allows to identify minimal sets of reactions for which DeltaG0 values yet have to be experimentally determined in order to make reliable predictions of DeltaG0 values for the remaining reactions.