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Prediction and assignment of function for a divergent N-succinyl amino acid racemase.
Song, Ling; Kalyanaraman, Chakrapani; Fedorov, Alexander A; Fedorov, Elena V; Glasner, Margaret E; Brown, Shoshana; Imker, Heidi J; Babbitt, Patricia C; Almo, Steven C; Jacobson, Matthew P; Gerlt, John A.
Afiliação
  • Song L; Department of Biochemistry, University of Illinois at Urbana-Champaign, 600 South Mathews Avenue, Urbana, Illinois 61801, USA.
Nat Chem Biol ; 3(8): 486-91, 2007 Aug.
Article em En | MEDLINE | ID: mdl-17603539
ABSTRACT
The protein databases contain many proteins with unknown function. A computational approach for predicting ligand specificity that requires only the sequence of the unknown protein would be valuable for directing experiment-based assignment of function. We focused on a family of unknown proteins in the mechanistically diverse enolase superfamily and used two approaches to assign function (i) enzymatic assays using libraries of potential substrates, and (ii) in silico docking of the same libraries using a homology model based on the most similar (35% sequence identity) characterized protein. The results matched closely; an experimentally determined structure confirmed the predicted structure of the substrate-liganded complex. We assigned the N-succinyl arginine/lysine racemase function to the family, correcting the annotation (L-Ala-D/L-Glu epimerase) based on the function of the most similar characterized homolog. These studies establish that ligand docking to a homology model can facilitate functional assignment of unknown proteins by restricting the identities of the possible substrates that must be experimentally tested.
Assuntos
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Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Isomerases de Aminoácido Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2007 Tipo de documento: Article
Buscar no Google
Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Isomerases de Aminoácido Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2007 Tipo de documento: Article