Many proteins involved in signal transduction contain peptide recognition modules (PRMs) that recognize short linear motifs (SLiMs) within their interaction partners. Here, we used large‐scale peptide‐phage display methods to derive optimal ligands for 163 unique PRMs representing 79 distinct structural families. We combined the new data with previous data that we collected for the large SH3, PDZ, and WW domain families to assemble a database containing 7,984 unique peptide ligands for 500 PRMs representing 82 structural families. For 74 PRMs, we acquired enough new data to map the specificity profiles in detail and derived position weight matrices and binding specificity logos based on multiple peptide ligands. These analyses showed that optimal peptide ligands resembled peptides observed in existing structures of PRM‐ligand complexes, indicating that a large majority of the phage‐derived peptides are likely to target natural peptide‐binding sites and could thus act as inhibitors of natural protein–protein interactions. The complete dataset has been assembled in an online database (http://www.prm‐db.org) that will enable many structural, functional, and biological studies of PRMs and SLiMs. SYNOPSIS A database of thousands of peptides targeting hundreds of diverse peptide recognition modules is presented (www.prm‐db.org). Applications of this large dataset include exploring natural protein‐protein interactions and inhibitor design. The database contains 7,984 unique peptides, each binding to one of 500 peptide recognition modules (PRMs) representing 82 distinct structural families. Phage‐derived peptides reflect the hydrophobic character of natural peptides at specific positions critical for PRM recognition. Comparative structural analysis highlights the functional importance of specific positions in peptide binding profiles. Phage‐derived peptides resemble natural short linear motifs (SLiMs) and likely bind to the same sites. A database of thousands of peptides targeting hundreds of diverse peptide recognition modules is presented (www.prm‐db.org). Applications of this large dataset include exploring natural protein‐protein interactions and inhibitor design.