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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Vydáno v:	Scientific reports Ročník 5; číslo 1; s. 12512
Hlavní autoři:	Kumar, Ravi, Chaudhary, Kumardeep, Singh Chauhan, Jagat, Nagpal, Gandharva, Kumar, Rahul, Sharma, Minakshi, Raghava, Gajendra P.S.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 27.07.2015 Nature Publishing Group
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 These authors contributed equally to this work.
ISSN:	2045-2322 2045-2322
DOI:	10.1038/srep12512