Literature DB >> 12360524

Optimal potentials for predicting inter-helical packing in transmembrane proteins.

H Dobbs1, E Orlandini, R Bonaccini, F Seno.   

Abstract

A set of pairwise contact potentials between amino acid residues in transmembrane helices was determined from the known native structure of the transmembrane protein (TMP) bacteriorhodopsin by the method of perceptron learning, using Monte Carlo dynamics to generate suitable "decoy" structures. The procedure of finding these decoys is simpler than for globular proteins, since it is reasonable to assume that helices behave as independent, stable objects and, therefore, the search in the conformational space is greatly reduced. With the learnt potentials, the association of the helices in bacteriorhodopsin was successfully simulated. The folding of a second TMP (the helix-dimer glycophorin A) was then accomplished with only a refinement of the potentials from a small number of decoys. Copyright 2002 Wiley-Liss, Inc.

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Year:  2002        PMID: 12360524     DOI: 10.1002/prot.10229

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  7 in total

1.  Optimal bundling of transmembrane helices using sparse distance constraints.

Authors:  Ken Sale; Jean-Loup Faulon; Genetha A Gray; Joseph S Schoeniger; Malin M Young
Journal:  Protein Sci       Date:  2004-08-31       Impact factor: 6.725

2.  Replica exchange Monte-Carlo simulations of helix bundle membrane proteins: rotational parameters of helices.

Authors:  H-H Wu; C-C Chen; C-M Chen
Journal:  J Comput Aided Mol Des       Date:  2012-03-31       Impact factor: 3.686

3.  Multipass membrane protein structure prediction using Rosetta.

Authors:  Vladimir Yarov-Yarovoy; Jack Schonbrun; David Baker
Journal:  Proteins       Date:  2006-03-01

4.  Computational prediction of atomic structures of helical membrane proteins aided by EM maps.

Authors:  Julio A Kovacs; Mark Yeager; Ruben Abagyan
Journal:  Biophys J       Date:  2007-05-11       Impact factor: 4.033

Review 5.  Computational studies of membrane proteins: models and predictions for biological understanding.

Authors:  Jie Liang; Hammad Naveed; David Jimenez-Morales; Larisa Adamian; Meishan Lin
Journal:  Biochim Biophys Acta       Date:  2011-10-12

6.  Statistical analyses and computational prediction of helical kinks in membrane proteins.

Authors:  Y-H Huang; C-M Chen
Journal:  J Comput Aided Mol Des       Date:  2012-09-21       Impact factor: 3.686

7.  Scoring predictive models using a reduced representation of proteins: model and energy definition.

Authors:  Federico Fogolari; Lidia Pieri; Agostino Dovier; Luca Bortolussi; Gilberto Giugliarelli; Alessandra Corazza; Gennaro Esposito; Paolo Viglino
Journal:  BMC Struct Biol       Date:  2007-03-23
  7 in total

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