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CONFOLD2: improved contact-driven ab initio protein structure modeling

Overview of attention for article published in BMC Bioinformatics, January 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (61st percentile)

Mentioned by

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6 tweeters

Citations

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55 Dimensions

Readers on

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51 Mendeley
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Title
CONFOLD2: improved contact-driven ab initio protein structure modeling
Published in
BMC Bioinformatics, January 2018
DOI 10.1186/s12859-018-2032-6
Pubmed ID
Authors

Badri Adhikari, Jianlin Cheng

Abstract

Contact-guided protein structure prediction methods are becoming more and more successful because of the latest advances in residue-residue contact prediction. To support contact-driven structure prediction, effective tools that can quickly build tertiary structural models of good quality from predicted contacts need to be developed. We develop an improved contact-driven protein modelling method, CONFOLD2, and study how it may be effectively used for ab initio protein structure prediction with predicted contacts as input. It builds models using various subsets of input contacts to explore the fold space under the guidance of a soft square energy function, and then clusters the models to obtain the top five models. CONFOLD2 obtains an average reconstruction accuracy of 0.57 TM-score for the 150 proteins in the PSICOV contact prediction dataset. When benchmarked on the CASP11 contacts predicted using CONSIP2 and CASP12 contacts predicted using Raptor-X, CONFOLD2 achieves a mean TM-score of 0.41 on both datasets. CONFOLD2 allows to quickly generate top five structural models for a protein sequence when its secondary structures and contacts predictions at hand. The source code of CONFOLD2 is publicly available at https://github.com/multicom-toolbox/CONFOLD2/ .

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 22%
Researcher 9 18%
Student > Master 9 18%
Student > Doctoral Student 4 8%
Student > Bachelor 3 6%
Other 4 8%
Unknown 11 22%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 29%
Computer Science 9 18%
Agricultural and Biological Sciences 7 14%
Chemistry 2 4%
Mathematics 2 4%
Other 1 2%
Unknown 15 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 08 October 2018.
All research outputs
#7,357,427
of 13,595,754 outputs
Outputs from BMC Bioinformatics
#2,753
of 5,067 outputs
Outputs of similar age
#151,434
of 350,877 outputs
Outputs of similar age from BMC Bioinformatics
#7
of 18 outputs
Altmetric has tracked 13,595,754 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,067 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 350,877 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 18 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 61% of its contemporaries.