Literature DB >> 21905701

Design of native-like proteins through an exposure-dependent environment potential.

Samuel DeLuca1, Brent Dorr, Jens Meiler.   

Abstract

We hypothesize that the degree of surface exposure of amino acid side chains within a globular, soluble protein has been optimized in evolution, not only to minimize the solvation free energy of the monomeric protein but also to prevent protein aggregation. This effect needs to be taken into account when engineering proteins de novo. We test this hypothesis through addition of a knowledge-based, exposure-dependent energy term to the RosettaDesign solvation potential [Lazaridis, T., and Karplus, M. (1999) Proteins 35, 133-152]. Correlation between amino acid type and surface exposure is determined from a representative set of experimental protein structures. The amino acid solvent accessible surface area (SASA) is estimated with a neighbor vector measure that increases in accuracy compared to the neighbor count measure while remaining pairwise decomposable [Durham, E., et al. (2009) J. Mol. Model. 15, 1093-1108]. Benchmarking of this potential in protein design displays a 3.2% improvement in the overall sequence recovery and an 8.5% improvement in recovery of amino acid types tolerated in evolution.

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Year:  2011        PMID: 21905701      PMCID: PMC3752783          DOI: 10.1021/bi200664b

Source DB:  PubMed          Journal:  Biochemistry        ISSN: 0006-2960            Impact factor:   3.162


  12 in total

1.  Effective energy function for proteins in solution.

Authors:  T Lazaridis; M Karplus
Journal:  Proteins       Date:  1999-05-01

2.  Native protein sequences are close to optimal for their structures.

Authors:  B Kuhlman; D Baker
Journal:  Proc Natl Acad Sci U S A       Date:  2000-09-12       Impact factor: 11.205

3.  An amino acid has two sides: a new 2D measure provides a different view of solvent exposure.

Authors:  Thomas Hamelryck
Journal:  Proteins       Date:  2005-04-01

Review 4.  Interfaces and the driving force of hydrophobic assembly.

Authors:  David Chandler
Journal:  Nature       Date:  2005-09-29       Impact factor: 49.962

5.  SODOCK: swarm optimization for highly flexible protein-ligand docking.

Authors:  Hung-Ming Chen; Bo-Fu Liu; Hui-Ling Huang; Shiow-Fen Hwang; Shinn-Ying Ho
Journal:  J Comput Chem       Date:  2007-01-30       Impact factor: 3.376

6.  A large scale test of computational protein design: folding and stability of nine completely redesigned globular proteins.

Authors:  Gautam Dantas; Brian Kuhlman; David Callender; Michelle Wong; David Baker
Journal:  J Mol Biol       Date:  2003-09-12       Impact factor: 5.469

7.  Triathlon for energy functions: who is the winner for design of protein-protein interactions?

Authors:  Oz Sharabi; Ayelet Dekel; Julia M Shifman
Journal:  Proteins       Date:  2011-03-01

8.  An orientation-dependent hydrogen bonding potential improves prediction of specificity and structure for proteins and protein-protein complexes.

Authors:  Tanja Kortemme; Alexandre V Morozov; David Baker
Journal:  J Mol Biol       Date:  2003-02-28       Impact factor: 5.469

9.  Solvent accessible surface area approximations for rapid and accurate protein structure prediction.

Authors:  Elizabeth Durham; Brent Dorr; Nils Woetzel; René Staritzbichler; Jens Meiler
Journal:  J Mol Model       Date:  2009-02-21       Impact factor: 1.810

10.  The relation between the divergence of sequence and structure in proteins.

Authors:  C Chothia; A M Lesk
Journal:  EMBO J       Date:  1986-04       Impact factor: 11.598

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  6 in total

1.  Computational design of membrane proteins using RosettaMembrane.

Authors:  Amanda M Duran; Jens Meiler
Journal:  Protein Sci       Date:  2017-11-15       Impact factor: 6.725

Review 2.  Energy functions in de novo protein design: current challenges and future prospects.

Authors:  Zhixiu Li; Yuedong Yang; Jian Zhan; Liang Dai; Yaoqi Zhou
Journal:  Annu Rev Biophys       Date:  2013-02-28       Impact factor: 12.981

3.  Computational design of protein-small molecule interfaces.

Authors:  Brittany Allison; Steven Combs; Sam DeLuca; Gordon Lemmon; Laura Mizoue; Jens Meiler
Journal:  J Struct Biol       Date:  2013-08-17       Impact factor: 2.867

Review 4.  Finding the needle in the haystack: towards solving the protein-folding problem computationally.

Authors:  Bian Li; Michaela Fooksa; Sten Heinze; Jens Meiler
Journal:  Crit Rev Biochem Mol Biol       Date:  2017-10-04       Impact factor: 8.250

5.  Octarellin VI: using rosetta to design a putative artificial (β/α)8 protein.

Authors:  Maximiliano Figueroa; Nicolas Oliveira; Annabelle Lejeune; Kristian W Kaufmann; Brent M Dorr; André Matagne; Joseph A Martial; Jens Meiler; Cécile Van de Weerdt
Journal:  PLoS One       Date:  2013-08-19       Impact factor: 3.240

6.  New computational protein design methods for de novo small molecule binding sites.

Authors:  James E Lucas; Tanja Kortemme
Journal:  PLoS Comput Biol       Date:  2020-10-05       Impact factor: 4.475

  6 in total

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