Literature DB >> 15751116

Active site prediction for comparative model structures with thematics.

Ihsan A Shehadi1, Alexej Abyzov, Alper Uzun, Ying Wei, Leonel F Murga, Valentin Ilyin, Mary Jo Ondrechen.   

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.

Entities:  

Mesh:

Substances:

Year:  2005        PMID: 15751116     DOI: 10.1142/s0219720005000916

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  4 in total

1.  Robustness can evolve gradually in complex regulatory gene networks with varying topology.

Authors:  Stefano Ciliberti; Olivier C Martin; Andreas Wagner
Journal:  PLoS Comput Biol       Date:  2007-02-02       Impact factor: 4.475

2.  Machine learning differentiates enzymatic and non-enzymatic metals in proteins.

Authors:  Ryan Feehan; Meghan W Franklin; Joanna S G Slusky
Journal:  Nat Commun       Date:  2021-06-17       Impact factor: 14.919

3.  Prediction of functional sites based on the fuzzy oil drop model.

Authors:  Michał Bryliński; Katarzyna Prymula; Wiktor Jurkowski; Marek Kochańczyk; Ewa Stawowczyk; Leszek Konieczny; Irena Roterman
Journal:  PLoS Comput Biol       Date:  2007-04-12       Impact factor: 4.475

4.  Selective prediction of interaction sites in protein structures with THEMATICS.

Authors:  Ying Wei; Jaeju Ko; Leonel F Murga; Mary Jo Ondrechen
Journal:  BMC Bioinformatics       Date:  2007-04-09       Impact factor: 3.169

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.