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Identification of residue pairing in interacting β-strands from a predicted residue contact map

Overview of attention for article published in BMC Bioinformatics, April 2018
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  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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Title
Identification of residue pairing in interacting β-strands from a predicted residue contact map
Published in
BMC Bioinformatics, April 2018
DOI 10.1186/s12859-018-2150-1
Pubmed ID
Authors

Wenzhi Mao, Tong Wang, Wenxuan Zhang, Haipeng Gong

Abstract

Despite the rapid progress of protein residue contact prediction, predicted residue contact maps frequently contain many errors. However, information of residue pairing in β strands could be extracted from a noisy contact map, due to the presence of characteristic contact patterns in β-β interactions. This information may benefit the tertiary structure prediction of mainly β proteins. In this work, we propose a novel ridge-detection-based β-β contact predictor to identify residue pairing in β strands from any predicted residue contact map. Our algorithm RDb2C adopts ridge detection, a well-developed technique in computer image processing, to capture consecutive residue contacts, and then utilizes a novel multi-stage random forest framework to integrate the ridge information and additional features for prediction. Starting from the predicted contact map of CCMpred, RDb2C remarkably outperforms all state-of-the-art methods on two conventional test sets of β proteins (BetaSheet916 and BetaSheet1452), and achieves F1-scores of ~ 62% and ~ 76% at the residue level and strand level, respectively. Taking the prediction of the more advanced RaptorX-Contact as input, RDb2C achieves impressively higher performance, with F1-scores reaching ~ 76% and ~ 86% at the residue level and strand level, respectively. In a test of structural modeling using the top 1 L predicted contacts as constraints, for 61 mainly β proteins, the average TM-score achieves 0.442 when using the raw RaptorX-Contact prediction, but increases to 0.506 when using the improved prediction by RDb2C. Our method can significantly improve the prediction of β-β contacts from any predicted residue contact maps. Prediction results of our algorithm could be directly applied to effectively facilitate the practical structure prediction of mainly β proteins. All source data and codes are available at http://166.111.152.91/Downloads.html or the GitHub address of https://github.com/wzmao/RDb2C .

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Researcher 2 33%
Professor > Associate Professor 1 17%
Student > Postgraduate 1 17%
Readers by discipline Count As %
Computer Science 1 17%
Agricultural and Biological Sciences 1 17%
Chemistry 1 17%
Unknown 3 50%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 February 2019.
All research outputs
#11,425,696
of 14,411,276 outputs
Outputs from BMC Bioinformatics
#4,454
of 5,399 outputs
Outputs of similar age
#206,700
of 275,599 outputs
Outputs of similar age from BMC Bioinformatics
#10
of 25 outputs
Altmetric has tracked 14,411,276 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,399 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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We're also able to compare this research output to 25 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.