Literature DB >> 14988117

Protein secondary structure: entropy, correlations and prediction.

Gavin E Crooks1, Steven E Brenner.   

Abstract

MOTIVATION: Is protein secondary structure primarily determined by local interactions between residues closely spaced along the amino acid backbone or by non-local tertiary interactions? To answer this question, we measure the entropy densities of primary and secondary structure sequences, and the local inter-sequence mutual information density.
RESULTS: We find that the important inter-sequence interactions are short ranged, that correlations between neighboring amino acids are essentially uninformative and that only one-fourth of the total information needed to determine the secondary structure is available from local inter-sequence correlations. These observations support the view that the majority of most proteins fold via a cooperative process where secondary and tertiary structure form concurrently. Moreover, existing single-sequence secondary structure prediction algorithms are almost optimal, and we should not expect a dramatic improvement in prediction accuracy. AVAILABILITY: Both the data sets and analysis code are freely available from our Web site at http://compbio.berkeley.edu/

Mesh:

Substances:

Year:  2004        PMID: 14988117     DOI: 10.1093/bioinformatics/bth132

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  16 in total

1.  The effect of long-range interactions on the secondary structure formation of proteins.

Authors:  Daisuke Kihara
Journal:  Protein Sci       Date:  2005-06-29       Impact factor: 6.725

2.  Molecular dynamics simulation of the Escherichia coli NikR protein: equilibrium conformational fluctuations reveal interdomain allosteric communication pathways.

Authors:  Michael J Bradley; Peter T Chivers; Nathan A Baker
Journal:  J Mol Biol       Date:  2008-03-14       Impact factor: 5.469

3.  Globally, unrelated protein sequences appear random.

Authors:  Daniel T Lavelle; William R Pearson
Journal:  Bioinformatics       Date:  2009-11-30       Impact factor: 6.937

4.  A sequence-compatible amount of native burial information is sufficient for determining the structure of small globular proteins.

Authors:  Antonio F Pereira de Araujo; José N Onuchic
Journal:  Proc Natl Acad Sci U S A       Date:  2009-10-26       Impact factor: 11.205

Review 5.  From local structure to a global framework: recognition of protein folds.

Authors:  Agnel Praveen Joseph; Alexandre G de Brevern
Journal:  J R Soc Interface       Date:  2014-04-16       Impact factor: 4.118

6.  Analysis of an optimal hidden Markov model for secondary structure prediction.

Authors:  Juliette Martin; Jean-François Gibrat; François Rodolphe
Journal:  BMC Struct Biol       Date:  2006-12-13

7.  A dynamic Bayesian network approach to protein secondary structure prediction.

Authors:  Xin-Qiu Yao; Huaiqiu Zhu; Zhen-Su She
Journal:  BMC Bioinformatics       Date:  2008-01-25       Impact factor: 3.169

8.  Random amino acid mutations and protein misfolding lead to Shannon limit in sequence-structure communication.

Authors:  Andreas Martin Lisewski
Journal:  PLoS One       Date:  2008-09-01       Impact factor: 3.240

9.  Predicting peptide structures in native proteins from physical simulations of fragments.

Authors:  Vincent A Voelz; M Scott Shell; Ken A Dill
Journal:  PLoS Comput Biol       Date:  2009-02-06       Impact factor: 4.475

10.  Evolution based on domain combinations: the case of glutaredoxins.

Authors:  Rui Alves; Ester Vilaprinyo; Albert Sorribas; Enrique Herrero
Journal:  BMC Evol Biol       Date:  2009-03-25       Impact factor: 3.260

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