Evidence map›Paper›PMID 25379372›Full record

ArticleOsong public health and research perspectives2014

A novel approach for predicting disordered regions in a protein sequence.

Meijing Li, Seong Beom Cho, Keun Ho Ryu

Abstract read
In one paragraph

Article in Osong public health and research perspectives, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Meijing LiDatabase/Bioinformatics Laboratory, Chungbuk National University, Cheongju, Korea.
Seong Beom ChoDivision of Bio-Medical Informatics, Center for Genome Science, Korea National Institute of Health, Cheongju, Korea.
Keun Ho RyuDatabase/Bioinformatics Laboratory, Chungbuk National University, Cheongju, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesA number of published predictors are based on various algorithms and disordered protein sequence properties. Although many predictors have been published, the study of protein disordered region prediction is ongoing because different prediction methods can find different disordered regions in a protein sequence.

methodsTherefore we have used a new approach to find the more varying disordered regions for more efficient and accurate prediction of protein structures. In this study, we propose a novel approach called "emerging subsequence (ES) mining" without using the characteristics of the disordered protein. We first adapted the approach to generate emerging protein subsequences on public protein sequence data. Second, the disordered and ordered regions in a protein sequence were predicted by searching the generated emerging protein subsequence with a sliding window, which tends to overlap. Third, the scores of the overlapping regions were calculated based on support and growthrate values in both classes. Finally, the score of predicted regions in the target class were compared with the score of the source class, and the class having a higher score was selected.

resultsIn this experiment, disordered sequence data and ordered sequence data was extracted from DisProt 6.02 and PDB respectively and used as training data. The test data come from CASP 9 and CASP 10 where disordered and ordered regions are known.

conclusionComparing with several published predictors, the results of the experiment show higher accuracy rates than with other existing methods.

Indexed as

amino acid sequencedisordered proteinemerging subsequenceprotein structure

Identifiers

PMID25379372
PMCPMC4215001

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