Literature DB >> 10388574

Side-chain and backbone flexibility in protein core design.

J R Desjarlais1, T M Handel.   

Abstract

We have developed a computational approach for the design and prediction of hydrophobic cores that includes explicit backbone flexibility. The program consists of a two-stage combination of a genetic algorithm and monte carlo sampling using a torsional model of the protein. Backbone structures are evaluated either by a canonical force-field or a constraining potential that emphasizes the preservation of local geometry. The utility of the method for protein design and engineering is explored by designing three novel hydrophobic core variants of the protein 434 cro. We use the new method to evaluate these and previously designed 434 cro variants, as well as a series of phage T4 lysozyme variants. In order to properly evaluate the influence of backbone flexibility, we have also analyzed the effects of varying amounts of side-chain flexibility on the performance of fixed backbone methods. Comparison of results using a fixed versus flexible backbone reveals that, surprisingly, the two methods are almost equivalent in their abilities to predict relative experimental stabilities, but only when full side-chain flexibility is allowed. The prediction of core side-chain structure can vary dramatically between methods. In some, but not all, cases the flexible backbone method is a better predictor of structure. The development of a flexible backbone approach to core design is particularly important for attempts at de novo protein design, where there is no prior knowledge of a precise backbone structure. Copyright 1999 Academic Press.

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Year:  1999        PMID: 10388574     DOI: 10.1006/jmbi.1999.2866

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  38 in total

1.  Designed protein G core variants fold to native-like structures: sequence selection by ORBIT tolerates variation in backbone specification.

Authors:  S A Ross; C A Sarisky; A Su; S L Mayo
Journal:  Protein Sci       Date:  2001-02       Impact factor: 6.725

2.  Prediction of amino acid sequence from structure.

Authors:  K Raha; A M Wollacott; M J Italia; J R Desjarlais
Journal:  Protein Sci       Date:  2000-06       Impact factor: 6.725

3.  Implicit solvation in the self-consistent mean field theory method: sidechain modelling and prediction of folding free energies of protein mutants.

Authors:  J Mendes; A M Baptista; M A Carrondo; C M Soares
Journal:  J Comput Aided Mol Des       Date:  2001-08       Impact factor: 3.686

4.  Thoroughly sampling sequence space: large-scale protein design of structural ensembles.

Authors:  Stefan M Larson; Jeremy L England; John R Desjarlais; Vijay S Pande
Journal:  Protein Sci       Date:  2002-12       Impact factor: 6.725

5.  Folding free energy function selects native-like protein sequences in the core but not on the surface.

Authors:  Alfonso Jaramillo; Lorenz Wernisch; Stéphanie Héry; Shoshana J Wodak
Journal:  Proc Natl Acad Sci U S A       Date:  2002-10-04       Impact factor: 11.205

6.  Computational protein design is a challenge for implicit solvation models.

Authors:  Alfonso Jaramillo; Shoshana J Wodak
Journal:  Biophys J       Date:  2004-09-17       Impact factor: 4.033

7.  Improved side-chain prediction accuracy using an ab initio potential energy function and a very large rotamer library.

Authors:  Ronald W Peterson; P Leslie Dutton; A Joshua Wand
Journal:  Protein Sci       Date:  2004-03       Impact factor: 6.725

8.  Protein backbone ensemble generation explores the local structural space of unseen natural homologs.

Authors:  Christian D Schenkelberg; Christopher Bystroff
Journal:  Bioinformatics       Date:  2016-01-18       Impact factor: 6.937

9.  Improved side-chain modeling for protein-protein docking.

Authors:  Chu Wang; Ora Schueler-Furman; David Baker
Journal:  Protein Sci       Date:  2005-03-31       Impact factor: 6.725

10.  Toward full-sequence de novo protein design with flexible templates for human beta-defensin-2.

Authors:  Ho Ki Fung; Christodoulos A Floudas; Martin S Taylor; Li Zhang; Dimitrios Morikis
Journal:  Biophys J       Date:  2007-09-07       Impact factor: 4.033

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