ProPepBA: a binding affinity database for canonical and chemically modified protein-peptide interactions
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Updated: Oct 30, 2026

Computational Prediction of Amino Acid Preferences of Potentially Multispecific Peptide-Binding Domains Involved in Protein-Protein Interactions
Published on: January 26, 2024
Sankaran Venkatachalam1, M Michael Gromiha1
1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Abstract:
Protein-peptide interactions play a crucial role in various biological processes, including cell signaling, immune responses, and host defense. The strength of these interactions is quantified by binding affinity, which is essential for understanding peptide recognition, uncovering biological mechanisms, and facilitating the design of peptide-based drugs. Although thousands of experimentally determined protein-peptide binding affinities are available, the information is scattered throughout the literature, making it difficult to use for systematic analysis and computational modeling. Moreover, existing databases for protein-peptide binding affinities have limitations, such as small data size, lack of experimental conditions, and mutation information. To bridge this gap, we developed ProPepBA, a comprehensive database of experimentally determined protein-peptide binding affinities that has been meticulously curated. The current version includes 3962 binding affinity data for canonical peptides sourced from approximately 500 research articles/patents, along with 608 records for chemically modified peptides, including phosphorylation, acetylation, methylation, and terminal modifications. Each entry is provided with information on protein and peptide, thermodynamic parameters, experimental conditions, and literature references. Additionally, the entries are cross-linked to databases such as UniProt, PDB, AlphaFoldDB, and PubMed. ProPepBA offers a user-friendly web interface with advanced search options, enabling efficient data retrieval. By integrating experimental binding affinity data with sequence, structural, and thermodynamic information, ProPepBA serves as a valuable resource for exploring sequence-structure-affinity relationships, development of computational tools, and discovering high affinity peptide binders to facilitate peptide-based drug design. The database is freely accessible at https://web.iitm.ac.in/bioinfo3/ProPepBA.
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