An in silico platform for predicting, screening and designing of antihypertensive peptides

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 antihypertensiv...

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Bibliographic Details
Published in:Scientific reports Vol. 5; no. 1; p. 12512
Main Authors: Kumar, Ravi, Chaudhary, Kumardeep, Singh Chauhan, Jagat, Nagpal, Gandharva, Kumar, Rahul, Sharma, Minakshi, Raghava, Gajendra P.S.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 27.07.2015
Nature Publishing Group
Subjects:
631/114
631/114/2398
631/154/1435/2418
Accuracy
Algorithms
Amino acid composition
Amino Acid Sequence
Amino acids
Antihypertensive Agents - administration & dosage
Antihypertensive Agents - chemistry
Antihypertensives
Blood pressure
Drug Design
Drug Evaluation, Preclinical - methods
Humanities and Social Sciences
Hypertension
Molecular Sequence Data
multidisciplinary
Pattern Recognition, Automated - methods
Peptides
Peptides - administration & dosage
Peptides - chemistry
Regression analysis
Science
Sequence Alignment - methods
Sequence Analysis, Protein - methods
Software
Support Vector Machine
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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These authors contributed equally to this work.
ISSN:2045-2322
2045-2322
DOI:10.1038/srep12512