DBAASP V4: condition-resolved bioactivity and chemical structures for data-driven peptide and peptidomimetic discovery

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.

The authors' abstract, as published at the source. Nucleic Acids Research, 2026 · DOI ↗