ArticleScientific reports2015
An in silico platform for predicting, screening and designing of antihypertensive peptides.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers.
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Who cites it
64 citing papers in PubMed.
- A Length-Aware C-Terminal Rule for Prioritizing Short ACE-Inhibitory Peptides from Food Protein Hydrolysates.Foods (Basel, Switzerland) · 2026Article
- Substrate Concentration Modulates Peptide Profile and Bioactivity in Alcalase Hydrolysis of Bovine Plasma Proteins at Fixed Enzyme-to-Substrate Ratio.Molecules (Basel, Switzerland) · 2026Article
- Food-Derived Multi-Target Antihypertensive Peptides: Sources, Mechanisms and AI-Driven Strategies.Foods (Basel, Switzerland) · 2026Review
- PepPharmaHub: a cloud-based platform integrating multimodel language architectures with curated data resources for therapeutic peptide discovery.BMC biology · 2026Article
- Goat milk-derived bioactive peptides as a health promoter: an insight based on recent in silico studies.Food science of animal resources · 2026Review
- CKAN-ATHP: a predictor for antihypertensive peptides based on sample augmentation and loss improvement strategies using the convolutional Kolmogorov-Arnold network.Journal of cheminformatics · 2026Article
- Sustainability-oriented recovery of antioxidant and ACE-inhibitory low-molecular-weight peptide fractions fromFrontiers in nutrition · 2026Article
- Investigation of Angiotensin I-Converting Enzyme Inhibitory Peptides Derived from Germinated Lamtoro Gung UsingPreventive nutrition and food science · 2025Article
- Novel multitarget ACE inhibitory peptides from grass carp swim bladder: Identification and potential antihypertensive mechanism.Food chemistry: X · 2025Article
- Antihypertensive and antioxidant peptide identification and storage stability assessment of novel probiotic fermented sheep milk: A comparative study with bovine milk.Food chemistry: X · 2025Article
- Food-derived bioactive peptides: health benefits, structure‒activity relationships, and translational prospects.Journal of Zhejiang University. Science. B · 2025Review
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- MicroalgaFoods (Basel, Switzerland) · 2025Article
- Sequence-Activity Relationship of Angiotensin-Converting Enzyme Inhibitory Peptides Derived from Food Proteins, Based on a New Deep Learning Model.Foods (Basel, Switzerland) · 2024Article
- A Novel Workflow for In Silico Prediction of Bioactive Peptides: An Exploration ofBiomolecules · 2024Article
- The first bioactive (angiotensin-converting enzyme-inhibitory) peptide isolated from pearl matrix protein.Heliyon · 2024Article
- Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools.International journal of molecular sciences · 2024Review
- Prospection of Peptide Inhibitors of Thrombin from Diverse Origins Using a Machine Learning Pipeline.Bioengineering (Basel, Switzerland) · 2023Article
- Discovery of ACE Inhibitory Peptides Derived from Green Coffee Using In Silico and In Vitro Methods.Foods (Basel, Switzerland) · 2023Article
- A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides.Molecules (Basel, Switzerland) · 2023Article
4 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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