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PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies

Overview of attention for article published in BMC Bioinformatics, April 2016
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  • Above-average Attention Score compared to outputs of the same age (62nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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5 tweeters

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13 Mendeley
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Title
PAT: predictor for structured units and its application for the optimization of target molecules for the generation of synthetic antibodies
Published in
BMC Bioinformatics, April 2016
DOI 10.1186/s12859-016-1001-1
Pubmed ID
Authors

Jouhyun Jeon, Roland Arnold, Fateh Singh, Joan Teyra, Tatjana Braun, Philip M. Kim

Abstract

The identification of structured units in a protein sequence is an important first step for most biochemical studies. Importantly for this study, the identification of stable structured region is a crucial first step to generate novel synthetic antibodies. While many approaches to find domains or predict structured regions exist, important limitations remain, such as the optimization of domain boundaries and the lack of identification of non-domain structured units. Moreover, no integrated tool exists to find and optimize structural domains within protein sequences. Here, we describe a new tool, PAT ( http://www.kimlab.org/software/pat ) that can efficiently identify both domains (with optimized boundaries) and non-domain putative structured units. PAT automatically analyzes various structural properties, evaluates the folding stability, and reports possible structural domains in a given protein sequence. For reliability evaluation of PAT, we applied PAT to identify antibody target molecules based on the notion that soluble and well-defined protein secondary and tertiary structures are appropriate target molecules for synthetic antibodies. PAT is an efficient and sensitive tool to identify structured units. A performance analysis shows that PAT can characterize structurally well-defined regions in a given sequence and outperforms other efforts to define reliable boundaries of domains. Specially, PAT successfully identifies experimentally confirmed target molecules for antibody generation. PAT also offers the pre-calculated results of 20,210 human proteins to accelerate common queries. PAT can therefore help to investigate large-scale structured domains and improve the success rate for synthetic antibody generation.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 8%
Unknown 12 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 54%
Student > Bachelor 2 15%
Student > Ph. D. Student 2 15%
Student > Master 1 8%
Unspecified 1 8%
Other 0 0%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Biochemistry, Genetics and Molecular Biology 5 38%
Computer Science 1 8%
Unspecified 1 8%
Chemistry 1 8%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 13 April 2016.
All research outputs
#2,948,790
of 7,546,003 outputs
Outputs from BMC Bioinformatics
#1,612
of 3,399 outputs
Outputs of similar age
#98,710
of 272,934 outputs
Outputs of similar age from BMC Bioinformatics
#72
of 121 outputs
Altmetric has tracked 7,546,003 research outputs across all sources so far. This one has received more attention than most of these and is in the 60th percentile.
So far Altmetric has tracked 3,399 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 49th percentile – i.e., 49% 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 272,934 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 62% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.