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.

Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review

“…In their QSAR approach developed with ACE inhibitory peptides, Kumar, et al 49 have also included negative datasets to develop classication models in order to assign unknown peptides to a category of active or inactive peptides. These models have been incorporated in a freely available web resource (http:// crdd.osdd.net/raghava/ahtpin).…”

“…Several QSAR approaches have suggested that specic scales allowed obtention of QSAR models with higher correlation and cross-validation coefficients (R 2 and Q 2 , respectively), which has generally been the case with the more recently developed amino acid descriptor scales. 37,39,49,83,84 Some QSAR studies incorporating relatively large numbers of BAPs in the dataset have been conducted with bioactivity data obtained using different experimental conditions (enzyme : substrate ratio, source of enzymes, substrate, temperature, etc. ), which makes the validity of the models developed questionable.…”

“…A recent QSAR study on ACE inhibitory activity of peptides has been applied to the largest peptide (>1400 peptides) dataset employed to date. 49 The originality of this study also lies in the fact that it took into account peptides with a broad size range (from 2 to >12 amino acid residues). The peptides were classied according to their amino acid length as "tiny" (#3 amino acids), "small" (4-6 amino acids), "medium" (7-12 amino acids) and "large" (>12 amino acids).…”

Insight into subtilisin E-S7 cleavage pattern based on crystal structure and hydrolysates peptide analysis

“…The Enzyme Predictor tool was used to help to analyze peptide sequences[21]. The QSAR platform AHT pin was used to predict antihypertensive Capacity in the variable length mode and amino acid composition model (SVM threshold was set to 0.9)[22]. …”

Enabling full‐length evolutionary profiles based deep convolutional neural network for predicting DNA‐binding proteins from sequence

“…Due to this reason, the proteins or their evolutionary profiles first have to be reduced to a definite set of features to enable their usage in a computational predictive model . Main summary features traditionally used for function prediction have been (a) amino acid composition of the sequence (see eg, ), (b) dinucleotide or higher k‐mer frequencies, (c) column averages of a position‐specific substitution matrices, representing evolutionary profile or submatrices from a PSSM collected by pooling together rows where the same amino acid residue has been observed . Recently, we extended this feature set by defining sequence features in terms of predicted scores of binding with other ligands' binding sites for each residue in a protein .…”