Literature DB >> 19751685

Understanding protein structure from a percolation perspective.

Dhruba Deb1, Saraswathi Vishveshwara, Smitha Vishveshwara.   

Abstract

Underlying the unique structures and diverse functions of proteins are a vast range of amino-acid sequences and a highly limited number of folds taken up by the polypeptide backbone. By investigating the role of noncovalent connections at the backbone level and at the detailed side-chain level, we show that these unique structures emerge from interplay between random and selected features. Primarily, the protein structure network formed by these connections shows simple (bond) and higher order (clique) percolation behavior distinctly reminiscent of random network models. However, the clique percolation specific to the side-chain interaction network bears signatures unique to proteins characterized by a larger degree of connectivity than in random networks. These studies reflect some salient features of the manner in which amino acid sequences select the unique structure of proteins from the pool of a limited number of available folds.

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Year:  2009        PMID: 19751685      PMCID: PMC2749797          DOI: 10.1016/j.bpj.2009.07.016

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  54 in total

1.  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

2.  Clusters in alpha/beta barrel proteins: implications for protein structure, function, and folding: a graph theoretical approach.

Authors:  N Kannan; S Selvaraj; M M Gromiha; S Vishveshwara
Journal:  Proteins       Date:  2001-05-01

3.  Protein folds: laws of form revisited.

Authors:  M Denton; C Marshall
Journal:  Nature       Date:  2001-03-22       Impact factor: 49.962

4.  Backbone cluster identification in proteins by a graph theoretical method.

Authors:  S M Patra; S Vishveshwara
Journal:  Biophys Chem       Date:  2000-02-14       Impact factor: 2.352

5.  Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid.

Authors:  J D WATSON; F H CRICK
Journal:  Nature       Date:  1953-04-25       Impact factor: 49.962

6.  Geometry and symmetry presculpt the free-energy landscape of proteins.

Authors:  Trinh Xuan Hoang; Antonio Trovato; Flavio Seno; Jayanth R Banavar; Amos Maritan
Journal:  Proc Natl Acad Sci U S A       Date:  2004-05-17       Impact factor: 11.205

7.  A network representation of protein structures: implications for protein stability.

Authors:  K V Brinda; Saraswathi Vishveshwara
Journal:  Biophys J       Date:  2005-09-08       Impact factor: 4.033

8.  Interpreting correlated motions using normal mode analysis.

Authors:  Adam W Van Wynsberghe; Qiang Cui
Journal:  Structure       Date:  2006-11       Impact factor: 5.006

9.  Collective motions in HIV-1 reverse transcriptase: examination of flexibility and enzyme function.

Authors:  I Bahar; B Erman; R L Jernigan; A R Atilgan; D G Covell
Journal:  J Mol Biol       Date:  1999-01-22       Impact factor: 5.469

Review 10.  Protein folding: the endgame.

Authors:  M Levitt; M Gerstein; E Huang; S Subbiah; J Tsai
Journal:  Annu Rev Biochem       Date:  1997       Impact factor: 23.643

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  11 in total

1.  Interaction energy based protein structure networks.

Authors:  M S Vijayabaskar; Saraswathi Vishveshwara
Journal:  Biophys J       Date:  2010-12-01       Impact factor: 4.033

2.  An automated approach to network features of protein structure ensembles.

Authors:  Moitrayee Bhattacharyya; Chanda R Bhat; Saraswathi Vishveshwara
Journal:  Protein Sci       Date:  2013-10       Impact factor: 6.725

3.  NAPS update: network analysis of molecular dynamics data and protein-nucleic acid complexes.

Authors:  Broto Chakrabarty; Varun Naganathan; Kanak Garg; Yash Agarwal; Nita Parekh
Journal:  Nucleic Acids Res       Date:  2019-07-02       Impact factor: 16.971

4.  Percolation-like phase transitions in network models of protein dynamics.

Authors:  Jeffrey K Weber; Vijay S Pande
Journal:  J Chem Phys       Date:  2015-06-07       Impact factor: 3.488

5.  Self-assembly of polymer-grafted nanoparticles in solvent-free conditions.

Authors:  Alexandros Chremos; Jack F Douglas
Journal:  Soft Matter       Date:  2016-11-28       Impact factor: 3.679

6.  NAPS: Network Analysis of Protein Structures.

Authors:  Broto Chakrabarty; Nita Parekh
Journal:  Nucleic Acids Res       Date:  2016-05-05       Impact factor: 16.971

7.  Exploration of the conformational landscape in pregnane X receptor reveals a new binding pocket.

Authors:  Aneesh Chandran; Saraswathi Vishveshwara
Journal:  Protein Sci       Date:  2016-08-23       Impact factor: 6.725

8.  Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein's structural organization.

Authors:  Dhriti Sengupta; Sudip Kundu
Journal:  BMC Bioinformatics       Date:  2012-06-21       Impact factor: 3.169

9.  Ranking the quality of protein structure models using sidechain based network properties.

Authors:  Soma Ghosh; Saraswathi Vishveshwara
Journal:  F1000Res       Date:  2014-01-21

10.  Interaction signatures stabilizing the NAD(P)-binding Rossmann fold: a structure network approach.

Authors:  Moitrayee Bhattacharyya; Roopali Upadhyay; Saraswathi Vishveshwara
Journal:  PLoS One       Date:  2012-12-17       Impact factor: 3.240

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