Literature DB >> 16317667

Protein design simulations suggest that side-chain conformational entropy is not a strong determinant of amino acid environmental preferences.

Xiaozhen Hu1, Brian Kuhlman.   

Abstract

Loss of side-chain conformational entropy is an important force opposing protein folding and the relative preferences of the amino acids for being buried or solvent exposed may be partially determined by which amino acids lose more side-chain entropy when placed in the core of a protein. To investigate these preferences, we have incorporated explicit modeling of side-chain entropy into the protein design algorithm, RosettaDesign. In the standard version of the program, the energy of a particular sequence for a fixed backbone depends only on the lowest energy side-chain conformations that can be identified for that sequence. In the new model, the free energy of a single amino acid sequence is calculated by evaluating the average energy and entropy of an ensemble of structures generated by Monte Carlo sampling of amino acid side-chain conformations. To evaluate the impact of including explicit side-chain entropy, sequences were designed for 110 native protein backbones with and without the entropy model. In general, the differences between the two sets of sequences are modest, with the largest changes being observed for the longer amino acids: methionine and arginine. Overall, the identity between the designed sequences and the native sequences does not increase with the addition of entropy, unlike what is observed when other key terms are added to the model (hydrogen bonding, Lennard-Jones energies, and solvation energies). These results suggest that side-chain conformational entropy has a relatively small role in determining the preferred amino acid at each residue position in a protein. (c) 2005 Wiley-Liss, Inc.

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Year:  2006        PMID: 16317667     DOI: 10.1002/prot.20786

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


  15 in total

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Authors:  Pawel Sledz; Heping Zheng; Krzysztof Murzyn; Maksymilian Chruszcz; Matthew D Zimmerman; Mahendra D Chordia; Andrzej Joachimiak; Wladek Minor
Journal:  Protein Sci       Date:  2010-07       Impact factor: 6.725

2.  Salt bridges: geometrically specific, designable interactions.

Authors:  Jason E Donald; Daniel W Kulp; William F DeGrado
Journal:  Proteins       Date:  2011-01-05

Review 3.  Progress in computational protein design.

Authors:  Shaun M Lippow; Bruce Tidor
Journal:  Curr Opin Biotechnol       Date:  2007-07-20       Impact factor: 9.740

4.  Modeling mutations in protein structures.

Authors:  Eric Feyfant; Andrej Sali; András Fiser
Journal:  Protein Sci       Date:  2007-09       Impact factor: 6.725

Review 5.  Challenges in the computational design of proteins.

Authors:  María Suárez; Alfonso Jaramillo
Journal:  J R Soc Interface       Date:  2009-03-11       Impact factor: 4.118

6.  Flex ddG: Rosetta Ensemble-Based Estimation of Changes in Protein-Protein Binding Affinity upon Mutation.

Authors:  Kyle A Barlow; Shane Ó Conchúir; Samuel Thompson; Pooja Suresh; James E Lucas; Markus Heinonen; Tanja Kortemme
Journal:  J Phys Chem B       Date:  2018-02-15       Impact factor: 2.991

7.  Computational analysis of residue contributions to coiled-coil topology.

Authors:  Jorge Ramos; Themis Lazaridis
Journal:  Protein Sci       Date:  2011-09-20       Impact factor: 6.725

8.  Loss of conformational entropy in protein folding calculated using realistic ensembles and its implications for NMR-based calculations.

Authors:  Michael C Baxa; Esmael J Haddadian; John M Jumper; Karl F Freed; Tobin R Sosnick
Journal:  Proc Natl Acad Sci U S A       Date:  2014-10-13       Impact factor: 11.205

9.  OSPREY: protein design with ensembles, flexibility, and provable algorithms.

Authors:  Pablo Gainza; Kyle E Roberts; Ivelin Georgiev; Ryan H Lilien; Daniel A Keedy; Cheng-Yu Chen; Faisal Reza; Amy C Anderson; David C Richardson; Jane S Richardson; Bruce R Donald
Journal:  Methods Enzymol       Date:  2013       Impact factor: 1.600

10.  Algorithm for backrub motions in protein design.

Authors:  Ivelin Georgiev; Daniel Keedy; Jane S Richardson; David C Richardson; Bruce R Donald
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

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