DBAASP V4: condition-resolved bioactivity and chemical structures for data-driven peptide and peptidomimetic discovery
- 1Laboratory of Bioinformatics, Ivane Beritashvili Center of Experimental Biomedicine , Tbilisi 0160 ,
- 2Laboratory of Chromatin Biology, Institute of Cellular and Molecular Biology, Agricultural University of Georgia , Tbilisi 0159 ,
- 3Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health , Bethesda, MD 20892 ,
Abstract
The current peptide renaissance—driven by metagenomic and proteomic mining, molecular de-extinction, protein language models, and generative or evolutionary design—is producing millions of candidates, but useful machine learning depends on experimentally anchored, context-rich labels. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is a manually curated resource linking peptides and peptidomimetics to quantitative antimicrobial, antibiofilm, anticancer, hemolytic, and mammalian-cell cytotoxicity measurements and their experimental conditions. DBAASP v4 contains >25 000 entries and extends the resource from a sequence-centered database to a structure-aware repository. Major additions include systematic SMILES representations, enhanced annotation of intrachain bonds and ring architectures, dedicated antibiofilm, and synergy records, PubChem links, >9200 molecular-dynamics trajectories, and new prediction services. The condition-resolved data support supervised learning, strain-, and target-specific models, model calibration, and surrogate fitness functions for genetic algorithms. SMILES representations further enable modeling of noncanonical peptides and peptidomimetics that cannot be adequately described by sequence alone. DBAASP therefore complements large peptide catalogues by providing the experimentally curated activity and structure layers, needed to convert sequence information into testable predictions, design, and optimization of biologically active compounds. DBAASP v4 is freely available at https://dbaasp.org.
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BibTeX
@article{pirtskhalava2026dbaasp,
title = {{DBAASP V4: condition-resolved bioactivity and chemical structures for data-driven peptide and peptidomimetic discovery}},
author = {Malak Pirtskhalava and Evgenia Alimbarashvili and Maia Grigolava and Mindia Chubinidze and Boris Vishnepolsky and Giorgi Zaalishvili and Romeo Karaputadze and Natia Samsonidze and Alex Rosenthal and Darrell E Hurt and Michael Tartakovsky and Andrei Gabrielian},
journal = {Nucleic Acids Research},
year = {2026},
month = oct,
doi = {10.1093/nar/gkag1050},
url = {https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1050/9005753},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - DBAASP V4: condition-resolved bioactivity and chemical structures for data-driven peptide and peptidomimetic discovery
AU - Malak Pirtskhalava
AU - Evgenia Alimbarashvili
AU - Maia Grigolava
AU - Mindia Chubinidze
AU - Boris Vishnepolsky
AU - Giorgi Zaalishvili
AU - Romeo Karaputadze
AU - Natia Samsonidze
AU - Alex Rosenthal
AU - Darrell E Hurt
AU - Michael Tartakovsky
AU - Andrei Gabrielian
PY - 2026
DA - 2026/10/31
JO - Nucleic Acids Research
DO - 10.1093/nar/gkag1050
UR - https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkag1050/9005753
AB - The current peptide renaissance—driven by metagenomic and proteomic mining, molecular de-extinction, protein language models, and generative or evolutionary design—is producing millions of candidates, but useful machine learning depends on experimentally anchored, context-rich labels. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP) is a manually curated resource linking peptides and peptidomimetics to quantitative antimicrobial, antibiofilm, anticancer, hemolytic, and mammalian-cell cytotoxicity measurements and their experimental conditions. DBAASP v4 contains >25 000 entries and extends the resource from a sequence-centered database to a structure-aware repository. Major additions include systematic SMILES representations, enhanced annotation of intrachain bonds and ring architectures, dedicated antibiofilm, and synergy records, PubChem links, >9200 molecular-dynamics trajectories, and new prediction services. The condition-resolved data support supervised learning, strain-, and target-specific models, model calibration, and surrogate fitness functions for genetic algorithms. SMILES representations further enable modeling of noncanonical peptides and peptidomimetics that cannot be adequately described by sequence alone. DBAASP therefore complements large peptide catalogues by providing the experimentally curated activity and structure layers, needed to convert sequence information into testable predictions, design, and optimization of biologically active compounds. DBAASP v4 is freely available at https://dbaasp.org.
SN - 0305-1048
SN - 1362-4962
ER -