Pattern recognition techniques can be applied very profitably to proteomics because of the strong linkage of the protein’s molecule morphology and proteins functionalities. In fact, geometric and topological congruence (concavity and convexity correspondences) can be often considered certainly not sufficient but in many cases necessary conditions. In this connection, considering that the “active sites” are always located in one of the biggest concavities (in one of the largest “pockets”) and that the ligand must match this concavity, its effective part must be mainly convex. For this reason, the matching potential can be evaluated through an Extended Gaussian Image (EGI) shape representation. The original EGI, and a few extensions (namely Complex EGI and Enriched Complex EGI) representations and their correspondent concrete data-structures are here discussed. This data structure is then exploited for the implementation and evaluation of the matching stance between the small ligand molecule and a pocket of a protein macromolecule.
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