title: An in silico platform for predicting, screening and designing of antihypertensive peptides.
abstract: 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.
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Ravi
authors.initials: R
authors.lastname: Kumar
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Kumardeep
authors.initials: K
authors.lastname: Chaudhary
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Jagat
authors.initials: J
authors.lastname: Singh Chauhan
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Gandharva
authors.initials: G
authors.lastname: Nagpal
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Rahul
authors.initials: R
authors.lastname: Kumar
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Minakshi
authors.initials: M
authors.lastname: Sharma
authors.affiliation: Bioinformatics Centre, CSIR-Institute of Microbial Technology, Chandigarh-160036, India.
authors.forename: Gajendra P S
authors.initials: GP
authors.lastname: Raghava
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000959:Antihypertensive Agents / Q000008:administration & dosage / Q000737:chemistry*; D015195:Drug Design; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D010455:Peptides / Q000008:administration & dosage / Q000737:chemistry*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D060388:Support Vector Machine
chemical_list: D000959:Antihypertensive Agents; D010455:Peptides