Evidence map›Paper›PMID 23424587›Full record

ArticleAdvances in bioinformatics2013

Statistical Analysis of Terminal Extensions of Protein β-Strand Pairs.

Ning Zhang, Shan Gao, Lei Zhang, Jishou Ruan, Tao Zhang

Abstract read
In one paragraph

Article in Advances in bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ning ZhangDepartment of Biomedical Engineering, Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin 300072, China.
Shan Gao
Lei Zhang
Jishou Ruan
Tao Zhang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The long-range interactions, required to the accurate predictions of tertiary structures of β-sheet-containing proteins, are still difficult to simulate. To remedy this problem and to facilitate β-sheet structure predictions, many efforts have been made by computational methods. However, known efforts on β-sheets mainly focus on interresidue contacts or amino acid partners. In this study, to go one step further, we studied β-sheets on the strand level, in which a statistical analysis was made on the terminal extensions of paired β-strands. In most cases, the two paired β-strands have different lengths, and terminal extensions exist. The terminal extensions are the extended part of the paired strands besides the common paired part. However, we found that the best pairing required a terminal alignment, and β-strands tend to pair to make bigger common parts. As a result, 96.97%  of β-strand pairs have a ratio of 25% of the paired common part to the whole length. Also 94.26% and 95.98%  of β-strand pairs have a ratio of 40% of the paired common part to the length of the two β-strands, respectively. Interstrand register predictions by searching interacting β-strands from several alternative offsets should comply with this rule to reduce the computational searching space to improve the performances of algorithms.

Identifiers

PMID23424587
PMCPMC3569888

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.