Active site prediction for comparative model structures with thematics.

Ihsan A Shehadi; Alexej Abyzov; Alper Uzun; Ying Wei; Leonel F Murga; Valentin Ilyin; Mary Jo Ondrechen

doi:10.1142/s0219720005000916

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Active site prediction for comparative model structures with thematics.

Ihsan A Shehadi·Alexej Abyzov·Alper Uzun·Ying Wei·Leonel F Murga·Valentin Ilyin·Mary Jo Ondrechen

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Abstract

THEMATICS (Theoretical Microscopic Titration Curves) is a simple, reliable computational predictor of the active sites of enzymes from structure. Our method, based on well-established Finite Difference Poisson-Boltzmann techniques, identifies the ionisable residues with anomalous predicted titration behavior. A cluster of two or more such perturbed residues is a very reliable predictor of the active site. The protein does not have to bear any resemblance in sequence or structure to any previously characterized protein, but the method does require the three-dimensional structure. We now present evidence that THEMATICS can also locate the active site in structures built by comparative modeling from similar structures. Results are given for a total of 21 sets of proteins, including 21 templates and 83 comparative model structures. Detailed results are presented for three sets of orthologous proteins (Triosephosphate isomerase, 6-Hydroxymethyl-7,8-dihydropterin pyrophosphokinase, and Aspartate aminotransferase) and for one set of human homologues of Aldose reductase with different functions. THEMATICS correctly locates the active site in the model structures. This suggests that the method can be applicable to a much larger set of proteins for which an experimentally determined structure is unavailable. With a few exceptions, the predicted active sites in the comparative model structures are similar to that of the corresponding template structure.

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Algorithms Binding Sites Enzyme Activation Enzymes Models, Chemical Models, Molecular Protein Binding Sequence Analysis, Protein Software Structure-Activity Relationship Titrimetry Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.Validation Study

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Ihsan A Shehadi, Alexej Abyzov, Alper Uzun, Ying Wei, Leonel F Murga, Valentin Ilyin, Mary Jo Ondrechen. 2005. Active site prediction for comparative model structures with thematics.. https://doi.org/10.1142/s0219720005000916

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