Prediction of Functional Sites Based on the Fuzzy Oil Drop Model

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Abstract
A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins. This paper describes a computational model that can be used to identify potential areas that are able to interact with other molecules (ligands, substrates, inhibitors, etc.). The model for active site recognition is based on the analysis of hydrophobicity distribution in protein molecules. It is shown, based on the analyses of proteins with known biological activity and of proteins of unknown function, that the region of significantly irregular hydrophobicity distribution in proteins appears to be function related. We present here a method of defining functional site recognition in proteins. The active site (enzymatic cavity or ligand-binding site) is localized on the basis of hydrophobicity deficiency, which is understood as the difference between empirical (dependent on amino acid positions) and idealized (3-D Gauss function, or Fuzzy Oil Drop model) distribution of hydrophobicity. It is assumed that the localization of amino acids representing a high difference of hydrophobic density reveals the functional site. The analysis of the structure of 33 proteins of known biological activity and of 33 proteins of unknown function (with comparable polypeptide chain lengths) seems to verify the applicability of the method to binding cavity localization. The comparative analysis with other methods oriented on biological function is also presented. The validation of predictability accuracy is shown with respect to the enzyme classes.