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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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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.

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Mendeley readers

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 > Ph. D. Student 2 15%
Student > Bachelor 2 15%
Student > Master 1 8%
Unknown 1 8%
Readers by discipline Count As %
Agricultural and Biological Sciences 5 38%
Biochemistry, Genetics and Molecular Biology 4 31%
Computer Science 2 15%
Chemistry 1 8%
Unknown 1 8%
Attention Score in Context

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 04 April 2016.
All research outputs
#15,635,122
of 23,245,494 outputs
Outputs from BMC Bioinformatics
#5,453
of 7,361 outputs
Outputs of similar age
#181,292
of 301,121 outputs
Outputs of similar age from BMC Bioinformatics
#88
of 117 outputs
Altmetric has tracked 23,245,494 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,361 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 301,121 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.