Literature DB >> 11170200

Prediction of protein surface accessibility with information theory.

H Naderi-Manesh1, M Sadeghi, S Arab, A A Moosavi Movahedi.   

Abstract

A new, simple method based on information theory is introduced to predict the solvent accessibility of amino acid residues in various states defined by their different thresholds. Prediction is achieved by the application of information obtained from a single amino acid position or pair-information for a window of seventeen amino acids around the desired residue. Results obtained by pairwise information values are better than results from single amino acids. This reinforces the effect of the local environment on the accessibility of amino acid residues. The prediction accuracy of this method in a jackknife test system for two and three states is better than 70 and 60 %, respectively. A comparison of the results with those reported by others involving the same data set also testifies to a better prediction accuracy in our case. Copyright 2001 Wiley-Liss, Inc.

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Year:  2001        PMID: 11170200     DOI: 10.1002/1097-0134(20010301)42:4<452::aid-prot40>3.0.co;2-q

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


  31 in total

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4.  Accessible surface area from NMR chemical shifts.

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Journal:  BMC Bioinformatics       Date:  2008-12-12       Impact factor: 3.169

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Journal:  Protein Sci       Date:  2003-07       Impact factor: 6.725

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Journal:  BMC Struct Biol       Date:  2011-09-18

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Journal:  BioData Min       Date:  2011-11-14       Impact factor: 2.522

9.  A structure filter for the Eukaryotic Linear Motif Resource.

Authors:  Allegra Via; Cathryn M Gould; Christine Gemünd; Toby J Gibson; Manuela Helmer-Citterich
Journal:  BMC Bioinformatics       Date:  2009-10-24       Impact factor: 3.169

10.  Evidence for water-tuned structural differences in proteins: an approach emphasizing variations in local hydrophilicity.

Authors:  Yasar Akdogan; Jörg Reichenwallner; Dariush Hinderberger
Journal:  PLoS One       Date:  2012-09-25       Impact factor: 3.240

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