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Leveraging family-specific signatures for AMP discovery and high-throughput annotation

Overview of attention for article published in Scientific Reports, April 2016
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1 tweeter

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35 Mendeley
Title
Leveraging family-specific signatures for AMP discovery and high-throughput annotation
Published in
Scientific Reports, April 2016
DOI 10.1038/srep24684
Pubmed ID
Authors

Faiza Hanif Waghu, Ram Shankar Barai, Susan Idicula-Thomas

Abstract

Antimicrobial peptides (AMPs) are diverse, biologically active, essential components of the innate immune system. As compared to conventional antibiotics, AMPs exhibit broad spectrum antimicrobial activity, reduced toxicity and reduced microbial resistance. They are widely researched for their therapeutic potential, especially against multi-drug resistant pathogens. AMPs are known to have family-specific sequence composition, which can be mined for their discovery and rational design. Here, we present a detailed family-based study on AMP families. The study involved the use of sequence signatures represented by patterns and hidden Markov models (HMMs) present in experimentally studied AMPs to identify novel AMPs. Along with AMPs, peptides hitherto lacking antimicrobial annotation were also retrieved and wet-lab studies on randomly selected sequences proved their antimicrobial activity against Escherichia coli. CAMPSign, a webserver has been created for researchers to effortlessly exploit the use of AMP family signatures for identification of AMPs. The webserver is available online at www.campsign.bicnirrh.res.in. In this work, we demonstrate an optimised and experimentally validated protocol along with a freely available webserver that uses family-based sequence signatures for accelerated discovery of novel AMPs.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 9 26%
Student > Master 5 14%
Student > Doctoral Student 2 6%
Student > Bachelor 2 6%
Other 1 3%
Unknown 6 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 14 40%
Biochemistry, Genetics and Molecular Biology 7 20%
Chemical Engineering 2 6%
Computer Science 2 6%
Immunology and Microbiology 1 3%
Other 3 9%
Unknown 6 17%

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 20 April 2016.
All research outputs
#6,562,101
of 7,578,098 outputs
Outputs from Scientific Reports
#25,109
of 29,311 outputs
Outputs of similar age
#224,129
of 267,247 outputs
Outputs of similar age from Scientific Reports
#2,281
of 2,703 outputs
Altmetric has tracked 7,578,098 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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