Literature DB >> 16843652

Knowledge-based potentials in protein design.

Alan M Poole1, Rama Ranganathan.   

Abstract

Knowledge-based potentials are statistical parameters derived from databases of known protein properties that empirically capture aspects of the physical chemistry of protein structure and function. These potentials play a key role in protein design by improving the accuracy of physics-based models of interatomic interactions and enhancing the computational efficiency of the design process by limiting the complexity of searching sequence space. Recently, knowledge-based potentials (in isolation or in combination with physics-based potentials) have been applied to the modification of existing protein function, the redesign of natural protein folds and the complete design of a non-natural protein fold. In addition, knowledge-based potentials appear to be providing important information about the global topology of amino acid interactions in natural proteins. A detailed study of the methods and products of these protein design efforts promises to greatly expand our understanding of proteins and the evolutionary process that created them.

Mesh:

Substances:

Year:  2006        PMID: 16843652     DOI: 10.1016/j.sbi.2006.06.013

Source DB:  PubMed          Journal:  Curr Opin Struct Biol        ISSN: 0959-440X            Impact factor:   6.809


  34 in total

Review 1.  Overview of regulatory strategies and molecular elements in metabolic engineering of bacteria.

Authors:  Tianwen Wang; Xingyuan Ma; Guocheng Du; Jian Chen
Journal:  Mol Biotechnol       Date:  2012-11       Impact factor: 2.695

2.  Evolution of substrate specificity within a diverse family of beta/alpha-barrel-fold basic amino acid decarboxylases: X-ray structure determination of enzymes with specificity for L-arginine and carboxynorspermidine.

Authors:  Xiaoyi Deng; Jeongmi Lee; Anthony J Michael; Diana R Tomchick; Elizabeth J Goldsmith; Margaret A Phillips
Journal:  J Biol Chem       Date:  2010-06-08       Impact factor: 5.157

3.  Recovering physical potentials from a model protein databank.

Authors:  J W Mullinax; W G Noid
Journal:  Proc Natl Acad Sci U S A       Date:  2010-11-01       Impact factor: 11.205

Review 4.  Computational tools for epitope vaccine design and evaluation.

Authors:  Linling He; Jiang Zhu
Journal:  Curr Opin Virol       Date:  2015-03-31       Impact factor: 7.090

5.  OPUS-Ca: a knowledge-based potential function requiring only Calpha positions.

Authors:  Yinghao Wu; Mingyang Lu; Mingzhi Chen; Jialin Li; Jianpeng Ma
Journal:  Protein Sci       Date:  2007-07       Impact factor: 6.725

Review 6.  Progress in computational protein design.

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

7.  OPUS-PSP: an orientation-dependent statistical all-atom potential derived from side-chain packing.

Authors:  Mingyang Lu; Athanasios D Dousis; Jianpeng Ma
Journal:  J Mol Biol       Date:  2007-11-19       Impact factor: 5.469

8.  A nonadaptive origin of a beneficial trait: in silico selection for free energy of folding leads to the neutral emergence of mutational robustness in single domain proteins.

Authors:  Rafael F Pagan; Steven E Massey
Journal:  J Mol Evol       Date:  2013-12-21       Impact factor: 2.395

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

Review 10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.