TY  - JOUR
N1  - Copyright of this article belongs to NPG.

ID  - open1826
UR  - http://dx.doi.org/10.1038/srep12512
A1  - Kumar, Ravi
A1  - Chaudhary, Kumardeep
A1  - Singh Chauhan, Jagat
A1  - Nagpal, Gandharva
A1  - Kumar, Rahul
A1  - Sharma, Minakshi
A1  - Raghava, G.P.S.
Y1  - 2015///
N2  - High blood pressure or hypertension is an affliction that threatens millions of lives worldwide. Peptides from natural origin have been shown recently to be highly effective in lowering blood pressure. In the present study, we have framed a platform for predicting and designing novel antihypertensive peptides. Due to a large variation found in the length of antihypertensive peptides, we divided these peptides into four categories (i) Tiny peptides, (ii) small peptides, (iii) medium peptides and (iv) large peptides. First, we developed SVM based regression models for tiny peptides using chemical descriptors and achieved maximum correlation of 0.701 and 0.543 for dipeptides and tripeptides, respectively. Second, classification models were developed for small peptides and achieved maximum accuracy of 76.67%, 72.04% and 77.39% for tetrapeptide, pentapeptide and hexapeptides, respectively. Third, we have developed a model for medium peptides using amino acid composition and achieved maximum accuracy of 82.61%. Finally, we have developed a model for large peptides using amino acid composition and achieved maximum accuracy of 84.21%. Based on the above study, a web-based platform has been developed for locating antihypertensive peptides in a protein, screening of peptides and designing of antihypertensive peptides.
PB  - NPG
JF  - Scientific Reports
VL  - 5
SN  - 2045-2322
TI  - An in silico platform for predicting, screening and designing of antihypertensive peptides
ER  -