PepHammer – a lightweight web-based tool for bioactive peptide matching and identification

Abstract Peptides are gaining increasing attention as therapeutic agents. Already, peptide-based therapeutics play a key role in the treatment of diverse diseases, including diabetes, obesity, and other complex disorders, and their clinical relevance is expected to expand further in the coming years. Technological and computational advances have substantially enriched peptidomics, massively increasing the scale and depth of peptide identification. As a result, increasingly large and information-rich datasets are now available for downstream analysis and experimental validation. However, the rapid expansion of peptidomics datasets also leads to a corresponding increase in search space, complicating the efficient identification of peptides relevant to specific biological or clinical questions. To address this challenge, we present PepHammer, a lightweight web-based tool for bioactive peptide matching and identification. PepHammer allows users to input up to 10000 peptides (2–150 amino acids in length) and compare them against extensive databases of peptides with predicted or experimentally validated bioactivities and tissue associations using Hamming distance, Grantham distance, as well as partial or exact matching strategies. Via an example study of human milk peptidomics, we demonstrate that PepHammer rapidly provides an overview of the bioactivity and tissue-relational landscape, serving as a starting point for downstream analyses. PepHammer thus enables efficient exploration of large-scale peptidomics datasets and facilitates the identification of biologically relevant peptides. Competing Interest Statement The authors have declared no competing interest.