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|Paper IPM / Biological / 14430||
The approaches taken to represent and describe structural features of the macromolecules are of major importance when developing computational methods for studying and predicting their structures and interactions. This study attempts to explore the significance of Delaunay tessellation for the definition of atomic interactions by evaluating its impact on the performance of scoring proteinâprotein docking prediction. Two sets of knowledge-based scoring potentials are extracted from a training dataset of native proteinâprotein complexes. The potential of the first set is derived using atomic interactions extracted from Delaunay tessellated structures. The potential of the second set is calculated conventionally, that is, using atom pairs whose interactions were determined by their separation distances. The scoring potentials were tested against two different docking decoy sets and their performances were compared. The results show that, if properly optimized, the Delaunay-based scoring potentials can achieve higher success rate than the usual scoring potentials. These results and the results of a previous study on the use of Delaunay-based potentials in protein fold recognition, all point to the fact that Delaunay tessellation of protein structure can provide a more realistic definition of atomic interaction, and therefore, if appropriately utilized, may be able to improve the accuracy of pair potentials.
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