Title |
In Silico Approach for Predicting Toxicity of Peptides and Proteins
|
---|---|
Published in |
PLOS ONE, September 2013
|
DOI | 10.1371/journal.pone.0073957 |
Pubmed ID | |
Authors |
Sudheer Gupta, Pallavi Kapoor, Kumardeep Chaudhary, Ankur Gautam, Rahul Kumar, Gajendra P. S. Raghava |
Abstract |
Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins. |
X Demographics
The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 29% |
India | 1 | 14% |
Germany | 1 | 14% |
Unknown | 3 | 43% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Scientists | 3 | 43% |
Mendeley readers
The data shown below were compiled from readership statistics for 1,054 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 2 | <1% |
United States | 1 | <1% |
Colombia | 1 | <1% |
France | 1 | <1% |
Unknown | 1049 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 128 | 12% |
Student > Ph. D. Student | 121 | 11% |
Student > Master | 110 | 10% |
Researcher | 100 | 9% |
Student > Doctoral Student | 45 | 4% |
Other | 124 | 12% |
Unknown | 426 | 40% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 227 | 22% |
Agricultural and Biological Sciences | 133 | 13% |
Immunology and Microbiology | 37 | 4% |
Chemistry | 35 | 3% |
Engineering | 24 | 2% |
Other | 125 | 12% |
Unknown | 473 | 45% |