Literature DB >> 15093838

Development of novel statistical potentials for protein fold recognition.

N-V Buchete1, J E Straub, D Thirumalai.   

Abstract

The need to perform large-scale studies of protein fold recognition, structure prediction and protein-protein interactions has led to novel developments of residue-level minimal models of proteins. A minimum requirement for useful protein force-fields is that they be successful in the recognition of native conformations. The balance between the level of detail in describing the specific interactions within proteins and the accuracy obtained using minimal protein models is the focus of many current protein studies. Recent results suggest that the introduction of explicit orientation dependence in a coarse-grained, residue-level model improves the ability of inter-residue potentials to recognize the native state. New statistical and optimization computational algorithms can be used to obtain accurate residue-dependent potentials for use in protein fold recognition and, more importantly, structure prediction.

Mesh:

Substances:

Year:  2004        PMID: 15093838     DOI: 10.1016/j.sbi.2004.03.002

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


  42 in total

1.  GOAP: a generalized orientation-dependent, all-atom statistical potential for protein structure prediction.

Authors:  Hongyi Zhou; Jeffrey Skolnick
Journal:  Biophys J       Date:  2011-10-19       Impact factor: 4.033

2.  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

3.  Sequence composition and environment effects on residue fluctuations in protein structures.

Authors:  Anatoly M Ruvinsky; Ilya A Vakser
Journal:  J Chem Phys       Date:  2010-10-21       Impact factor: 3.488

4.  Lessons from the design of a novel atomic potential for protein folding.

Authors:  William W Chen; Eugene I Shakhnovich
Journal:  Protein Sci       Date:  2005-07       Impact factor: 6.725

5.  Statistical potential for assessment and prediction of protein structures.

Authors:  Min-Yi Shen; Andrej Sali
Journal:  Protein Sci       Date:  2006-11       Impact factor: 6.725

6.  A free-rotating and self-avoiding chain model for deriving statistical potentials based on protein structures.

Authors:  Ji Cheng; Jianfeng Pei; Luhua Lai
Journal:  Biophys J       Date:  2007-03-09       Impact factor: 4.033

7.  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

8.  A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins.

Authors:  Peter Májek; Ron Elber
Journal:  Proteins       Date:  2009-09

Review 9.  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

10.  Modeling CAPRI targets 110-120 by template-based and free docking using contact potential and combined scoring function.

Authors:  Petras J Kundrotas; Ivan Anishchenko; Varsha D Badal; Madhurima Das; Taras Dauzhenka; Ilya A Vakser
Journal:  Proteins       Date:  2017-09-28
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.