Literature DB >> 23658418

PconsC: combination of direct information methods and alignments improves contact prediction.

Marcin J Skwark1, Abbi Abdel-Rehim, Arne Elofsson.   

Abstract

SUMMARY: Recently, several new contact prediction methods have been published. They use (i) large sets of multiple aligned sequences and (ii) assume that correlations between columns in these alignments can be the results of indirect interaction. These methods are clearly superior to earlier methods when it comes to predicting contacts in proteins. Here, we demonstrate that combining predictions from two prediction methods, PSICOV and plmDCA, and two alignment methods, HHblits and jackhmmer at four different e-value cut-offs, provides a relative improvement of 20% in comparison with the best single method, exceeding 70% correct predictions for one contact prediction per residue. AVAILABILITY: The source code for PconsC along with supplementary data is freely available at http://c.pcons.net/ CONTACT: arne@bioinfo.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Substances:

Year:  2013        PMID: 23658418     DOI: 10.1093/bioinformatics/btt259

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


  33 in total

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Journal:  Bioinformatics       Date:  2015-08-14       Impact factor: 6.937

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Journal:  Proteins       Date:  2015-11-17

6.  Identifying functionally informative evolutionary sequence profiles.

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Journal:  Bioinformatics       Date:  2018-04-15       Impact factor: 6.937

Review 7.  Applications of sequence coevolution in membrane protein biochemistry.

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9.  Forecasting residue-residue contact prediction accuracy.

Authors:  P P Wozniak; B M Konopka; J Xu; G Vriend; M Kotulska
Journal:  Bioinformatics       Date:  2017-11-01       Impact factor: 6.937

10.  Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolution.

Authors:  Wenzhi Mao; Cihan Kaya; Anindita Dutta; Amnon Horovitz; Ivet Bahar
Journal:  Bioinformatics       Date:  2015-02-19       Impact factor: 6.937

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