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Computational Peptidology

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Attention for Chapter 7: Peptide Toxicity Prediction
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Chapter title
Peptide Toxicity Prediction
Chapter number 7
Book title
Computational Peptidology
Published in
Methods in molecular biology, December 2014
DOI 10.1007/978-1-4939-2285-7_7
Pubmed ID
Book ISBNs
978-1-4939-2284-0, 978-1-4939-2285-7
Authors

Sudheer Gupta, Pallavi Kapoor, Kumardeep Chaudhary, Ankur Gautam, Rahul Kumar, Gajendra P. S. Raghava

Editors

Peng Zhou, Jian Huang

Abstract

Last decade has witnessed the revival of interest in peptides as potential therapeutics candidates. However, one of the bottlenecks in the success of therapeutic peptides in clinics is their toxicity towards eukaryotic cells. Therefore, considerable efforts have been made over the years both in wet and dry lab to overcome this limitation. With the advances in peptide synthesis, now it is possible to fine-tune the physicochemical properties of peptides by incorporating several chemical modifications and thus to optimize the peptide functionality in order to minimize the toxicity without compromising their therapeutic activity. Also various in silico tools for peptide toxicity prediction and peptide designing have been developed, which facilitates designing of therapeutic peptides with desired toxicity. In this chapter, we have discussed both wet lab and dry lab approaches used to optimize peptide toxicity. More emphasis has been given to describe the in silico method, ToxinPred, to predict the toxicity of peptide and about how to design a peptide or protein with desired toxicity by mutating minimum number of amino acids.

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
Colombia 1 <1%
Brazil 1 <1%
Unknown 115 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 22 19%
Researcher 13 11%
Student > Master 12 10%
Student > Bachelor 11 9%
Student > Doctoral Student 4 3%
Other 11 9%
Unknown 45 38%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 29 25%
Agricultural and Biological Sciences 14 12%
Immunology and Microbiology 5 4%
Medicine and Dentistry 5 4%
Pharmacology, Toxicology and Pharmaceutical Science 4 3%
Other 9 8%
Unknown 52 44%
Attention Score in Context

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 03 July 2016.
All research outputs
#13,420,341
of 22,778,347 outputs
Outputs from Methods in molecular biology
#3,609
of 13,093 outputs
Outputs of similar age
#177,992
of 361,208 outputs
Outputs of similar age from Methods in molecular biology
#233
of 996 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,093 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 70% of its peers.
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 361,208 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 996 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.