Welcome to the official repository for AHTPDB, a comprehensive and manually curated database of experimentally validated antihypertensive peptides (AHTPs). This resource is designed to support researchers in peptide therapeutics, hypertension biology, and computational drug discovery.
Web Server: http://crdd.osdd.net/raghava/ahtpdb/
ZENODO : https://doi.org/10.5281/zenodo.20065627
Kumar, R., Chaudhary, K., Sharma, M., Nagpal, G., Chauhan, J. S., Singh, S., Gautam, A., & Raghava, G. P. S. (2015). AHTPDB: a comprehensive platform for analysis and presentation of antihypertensive peptides. Nucleic Acids Research, 43(Database issue), D956–D962. https://doi.org/10.1093/nar/gku1141
AHTPDB is a dedicated and comprehensive platform for systematic collection, storage, and presentation of antihypertensive peptides. It consolidates scattered experimental findings from independent research studies into a centralized platform, enabling systematic exploration of peptide sequences, structures, sources, and biological activities.
The database integrates information from:
- Literature (PubMed, ~350 research articles)
- Public repositories (ACEpepDB, BIOPEP, EROP-Moscow)
Massive Dataset
- 5978 total entries
- 1694 unique peptides
- 3364 entries with IC50 values
Extensive Coverage
- 35 major peptide sources (milk, egg, fish, pork, chicken, soybean, etc.)
- Peptides from natural food, fungi, algae, microorganisms, insects, and snake venom
Rich Annotations
Each entry includes:
- Sequence, length, molecular mass, isoelectric point
- Source of peptide (natural or synthetic)
- Inhibitory concentration (IC50) and log value (pIC50)
- Toxicity/bitterness value
- Purification technique and assay method
- In vivo animal model data and decrease in systolic blood pressure (SBP)
- PubMed ID and year of publication
Structural Data
- Predicted tertiary structures via PEPstr algorithm
- Secondary structure assignment using DSSP
- SMILES representations for molecular analysis
- Structures for peptides of length 2–30 residues
AHTPDB provides experimentally validated data along with:
- ACE-inhibiting activity profiles
- Physicochemical and structural properties
- Source-specific peptide coverage
- Toxicity and bitterness annotations
- In vivo efficacy data (SBP reduction in rat models)
- Cross-references (PubMed, ACEpepDB, BIOPEP, EROP-Moscow)
Structures were predicted using:
- PEPstr — de novo tertiary structure prediction for peptides (length 5–30 residues), with extended molecular dynamics simulation of 1 ns using AMBER 11
- Custom approach — phi/psi torsion angle restraints at 180° for di-, tri-, and tetrapeptides, followed by AMBER simulation
- DSSP — secondary structure assignment from predicted tertiary structures
- Open Babel — conversion of tertiary structures to SMILES notation
Search
- Basic search — query any database field
- Advanced search — multi-field queries
- Peptide search — containing or exact match
- SMILES search — molecular structure-based search
Explore / Browse
- By source (35 major categories)
- By peptide length
- By IC50 range and units
- By physicochemical properties
Analysis Tools
- Smith-Waterman similarity search
- Sequence alignment
- Mapping (super-search and sub-search)
- Amino acid composition and frequency analysis
- Physicochemical property analysis
- Secondary and tertiary structure tools
- Structure of 36 peptides containing pyroglutamine (non-natural residue) could not be predicted due to unavailability of special force fields
- One peptide of length 81 residues was excluded from structure prediction
- Bitterness/toxicity values available for only 156 entries (limited by literature availability)
- SBP decrease values were approximated from graphs where exact values were not provided
- Antihypertensive peptide design and discovery
- Machine learning model training for AHTP prediction
- Structure-function analysis
- Food additive safety and efficacy research
- In silico screening and drug discovery pipelines
Prof. Gajendra P. S. Raghava Email - raghava@iiitd.ac.in IIIT Delhi
This database is distributed under the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0)
We acknowledge all researchers whose published work contributed to this dataset.