Literature DB >> 15523668

Predicting absolute contact numbers of native protein structure from amino acid sequence.

Akira R Kinjo1, Katsuhisa Horimoto, Ken Nishikawa.   

Abstract

The contact number of an amino acid residue in a protein structure is defined by the number of C(beta) atoms around the C(beta) atom of the given residue, a quantity similar to, but different from, solvent accessible surface area. We present a method to predict the contact numbers of a protein from its amino acid sequence. The method is based on a simple linear regression scheme and predicts the absolute values of contact numbers. When single sequences are used for both parameter estimation and cross-validation, the present method predicts the contact numbers with a correlation coefficient of 0.555 on average. When multiple sequence alignments are used, the correlation increases to 0.627, which is a significant improvement over previous methods. In terms of discrete states prediction, the accuracies for 2-, 3-, and 10-state predictions are, respectively, 71.4%, 54.1%, and 18.9% with residue type-dependent unbiased thresholds, and 76.3%, 59.2%, and 21.8% with residue type-independent unbiased thresholds. The difference between accessible surface area and contact number from a prediction viewpoint and the application of contact number prediction to three-dimensional structure prediction are discussed. (c) 2004 Wiley-Liss, Inc.

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Year:  2005        PMID: 15523668     DOI: 10.1002/prot.20300

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


  21 in total

1.  Improving prediction of helix-helix packing in membrane proteins using predicted contact numbers as restraints.

Authors:  Bian Li; Jeffrey Mendenhall; Elizabeth Dong Nguyen; Brian E Weiner; Axel W Fischer; Jens Meiler
Journal:  Proteins       Date:  2017-04-01

2.  svmPRAT: SVM-based protein residue annotation toolkit.

Authors:  Huzefa Rangwala; Christopher Kauffman; George Karypis
Journal:  BMC Bioinformatics       Date:  2009-12-22       Impact factor: 3.169

3.  High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABER-TOOTH.

Authors:  Florian Teichert; Jonas Minning; Ugo Bastolla; Markus Porto
Journal:  BMC Bioinformatics       Date:  2010-05-14       Impact factor: 3.169

4.  Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins.

Authors:  Bian Li; Jeffrey Mendenhall; Elizabeth Dong Nguyen; Brian E Weiner; Axel W Fischer; Jens Meiler
Journal:  J Chem Inf Model       Date:  2016-02-05       Impact factor: 4.956

5.  Better prediction of protein contact number using a support vector regression analysis of amino acid sequence.

Authors:  Zheng Yuan
Journal:  BMC Bioinformatics       Date:  2005-10-13       Impact factor: 3.169

6.  CRNPRED: highly accurate prediction of one-dimensional protein structures by large-scale critical random networks.

Authors:  Akira R Kinjo; Ken Nishikawa
Journal:  BMC Bioinformatics       Date:  2006-09-05       Impact factor: 3.169

7.  Prodepth: predict residue depth by support vector regression approach from protein sequences only.

Authors:  Jiangning Song; Hao Tan; Khalid Mahmood; Ruby H P Law; Ashley M Buckle; Geoffrey I Webb; Tatsuya Akutsu; James C Whisstock
Journal:  PLoS One       Date:  2009-09-17       Impact factor: 3.240

8.  Characterization of non-trivial neighborhood fold constraints from protein sequences using generalized topohydrophobicity.

Authors:  Guillaume Fourty; Isabelle Callebaut; Jean-Paul Mornon
Journal:  Bioinform Biol Insights       Date:  2008-01-31

9.  Protein molecular surface mapped at different geometrical resolutions.

Authors:  Dan V Nicolau; Ewa Paszek; Florin Fulga; Dan V Nicolau
Journal:  PLoS One       Date:  2013-03-14       Impact factor: 3.240

10.  Automated alphabet reduction for protein datasets.

Authors:  Jaume Bacardit; Michael Stout; Jonathan D Hirst; Alfonso Valencia; Robert E Smith; Natalio Krasnogor
Journal:  BMC Bioinformatics       Date:  2009-01-06       Impact factor: 3.169

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