Literature DB >> 17340633

Predicting DNA-binding amino acid residues from electrostatic stabilization upon mutation to Asp/Glu and evolutionary conservation.

Yao Chi Chen1, Chih Yuan Wu, Carmay Lim.   

Abstract

Binding of polyanionic DNA depends on the cluster of electropositive atoms in the binding site of a DNA-binding protein. Such a cluster of electropositive protein atoms would be electrostatically unfavorable without stabilizing interactions from the respective electronegative DNA atoms and would likely be evolutionary conserved due to its critical biological role. Consequently, our strategy for predicting DNA-binding residues is based on detecting a cluster of evolutionary conserved surface residues that are electrostatically stabilized upon mutation to negatively charged Asp/Glu residues. The method requires as input the protein structure and sufficient sequence homologs to define each residue's relative conservation, and it yields as output experimentally testable residues that are predicted to bind DNA. By incorporating characteristic DNA-binding site features (i.e., electrostatic strain and amino acid conservation), the new method yields a prediction accuracy of 83%, which is much higher than methods based on only electrostatic strain (57%) or conservation alone (50%). It is also less sensitive to protein conformational changes upon DNA binding than methods that mainly depend on the 3D protein structure. 2007 Wiley-Liss, Inc.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 17340633     DOI: 10.1002/prot.21366

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


  12 in total

1.  A structural-alphabet-based strategy for finding structural motifs across protein families.

Authors:  Chih Yuan Wu; Yao Chi Chen; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2010-06-04       Impact factor: 16.971

2.  In silico cloning and characterization of p8 homolog cDNA from common urchin (Paracentrotus lividus).

Authors:  Jia-Qing Wang; Jin-Cheng Han; Dai-Zong Li; Lin-Chun Li
Journal:  Mol Biol Rep       Date:  2009-02-27       Impact factor: 2.316

3.  Analysis and classification of DNA-binding sites in single-stranded and double-stranded DNA-binding proteins using protein information.

Authors:  Wei Wang; Juan Liu; Yi Xiong; Lida Zhu; Xionghui Zhou
Journal:  IET Syst Biol       Date:  2014-08       Impact factor: 1.615

4.  DR_bind: a web server for predicting DNA-binding residues from the protein structure based on electrostatics, evolution and geometry.

Authors:  Yao Chi Chen; Jon D Wright; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2012-05-31       Impact factor: 16.971

5.  From face to interface recognition: a differential geometric approach to distinguish DNA from RNA binding surfaces.

Authors:  Shula Shazman; Gershon Elber; Yael Mandel-Gutfreund
Journal:  Nucleic Acids Res       Date:  2011-06-21       Impact factor: 16.971

6.  Exploiting a reduced set of weighted average features to improve prediction of DNA-binding residues from 3D structures.

Authors:  Yi Xiong; Junfeng Xia; Wen Zhang; Juan Liu
Journal:  PLoS One       Date:  2011-12-08       Impact factor: 3.240

7.  DNA-binding residues and binding mode prediction with binding-mechanism concerned models.

Authors:  Yu-Feng Huang; Chun-Chin Huang; Yu-Cheng Liu; Yen-Jen Oyang; Chien-Kang Huang
Journal:  BMC Genomics       Date:  2009-12-03       Impact factor: 3.969

8.  Common physical basis of macromolecule-binding sites in proteins.

Authors:  Yao Chi Chen; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2008-11-06       Impact factor: 16.971

9.  Predicting RNA-binding sites from the protein structure based on electrostatics, evolution and geometry.

Authors:  Yao Chi Chen; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2008-02-14       Impact factor: 16.971

10.  Identifying RNA-binding residues based on evolutionary conserved structural and energetic features.

Authors:  Yao Chi Chen; Karen Sargsyan; Jon D Wright; Yi-Shuian Huang; Carmay Lim
Journal:  Nucleic Acids Res       Date:  2013-12-16       Impact factor: 16.971

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.