Literature DB >> 26073558

Comprehensive analysis of sequences of a protein switch.

Szu-Hua Chen1,2, Jaroslaw Meller3,4,5,6,7, Ron Elber2,8.   

Abstract

Switches form a special class of proteins that dramatically change their three-dimensional structures upon a small perturbation. One possible perturbation that we explore is that of a single point mutation. Building on the pioneering experimental work of Alexander et al. (Alexander et al. PNAS, 2007; 104,11963-11968) that determines switch sequences between α and α+β folds we conduct a comprehensive sequence sampling by a Markov Chain with multiple fitness criteria to identify new switches given the experimental folds. We screen for switch sequences using a combination of contact potential, secondary structure prediction, and finally molecular dynamics simulations. Statistical properties of switch sequences are discussed and illustrated to be most sensitive to mutation at the N- and C- termini of the switch protein. Based on this analysis, a particularly stable putative switch pair is identified and proposed for further experimental analysis.
© 2015 The Protein Society.

Keywords:  contact maps; molecular dynamics; mutations; protein folds; secondary structure prediction; structural flips

Mesh:

Substances:

Year:  2015        PMID: 26073558      PMCID: PMC4815306          DOI: 10.1002/pro.2723

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  24 in total

1.  Protein secondary structure prediction based on position-specific scoring matrices.

Authors:  D T Jones
Journal:  J Mol Biol       Date:  1999-09-17       Impact factor: 5.469

2.  Predicting conformational switches in proteins.

Authors:  M Young; K Kirshenbaum; K A Dill; S Highsmith
Journal:  Protein Sci       Date:  1999-09       Impact factor: 6.725

3.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

4.  Computational analysis of sequence selection mechanisms.

Authors:  Leonid Meyerguz; Catherine Grasso; Jon Kleinberg; Ron Elber
Journal:  Structure       Date:  2004-04       Impact factor: 5.006

5.  The network of sequence flow between protein structures.

Authors:  Leonid Meyerguz; Jon Kleinberg; Ron Elber
Journal:  Proc Natl Acad Sci U S A       Date:  2007-06-27       Impact factor: 11.205

6.  A minimal sequence code for switching protein structure and function.

Authors:  Patrick A Alexander; Yanan He; Yihong Chen; John Orban; Philip N Bryan
Journal:  Proc Natl Acad Sci U S A       Date:  2009-11-18       Impact factor: 11.205

7.  Protein design: a perspective from simple tractable models

Authors: 
Journal:  Fold Des       Date:  1998

8.  Natural selection and the concept of a protein space.

Authors:  J M Smith
Journal:  Nature       Date:  1970-02-07       Impact factor: 49.962

9.  Computational exploration of the network of sequence flow between protein structures.

Authors:  Baoqiang Cao; Ron Elber
Journal:  Proteins       Date:  2010-03

10.  TM-align: a protein structure alignment algorithm based on the TM-score.

Authors:  Yang Zhang; Jeffrey Skolnick
Journal:  Nucleic Acids Res       Date:  2005-04-22       Impact factor: 16.971

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  6 in total

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Journal:  Adv Drug Deliv Rev       Date:  2022-05-19       Impact factor: 17.873

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Authors:  Jordan Hoffmann; James O Wrabl; Vincent J Hilser
Journal:  Proteins       Date:  2016-02-13

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Authors:  David R Bell; Jeffrey K Weber; Wang Yin; Tien Huynh; Wei Duan; Ruhong Zhou
Journal:  Proc Natl Acad Sci U S A       Date:  2020-03-31       Impact factor: 11.205

4.  Theoretical Insights into the Biophysics of Protein Bi-stability and Evolutionary Switches.

Authors:  Tobias Sikosek; Heinrich Krobath; Hue Sun Chan
Journal:  PLoS Comput Biol       Date:  2016-06-02       Impact factor: 4.475

5.  Exploring the sequence fitness landscape of a bridge between protein folds.

Authors:  Pengfei Tian; Robert B Best
Journal:  PLoS Comput Biol       Date:  2020-10-13       Impact factor: 4.475

6.  Structure-based enzyme engineering improves donor-substrate recognition of Arabidopsis thaliana glycosyltransferases.

Authors:  Aishat Akere; Serena H Chen; Xiaohan Liu; Yanger Chen; Sarath Chandra Dantu; Alessandro Pandini; Debsindhu Bhowmik; Shozeb Haider
Journal:  Biochem J       Date:  2020-08-14       Impact factor: 3.857

  6 in total

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