AbAgym: A Well-Curated Dataset For The Mutational Analysis Of Antibody-Antigen Complexes A new dataset called AbAgym has been introduced, which is specifically designed for the mutational analysis of antibody-antigen complexes. This dataset is manually curated and contains approximately 335,000 mutations, including about 37,361 interface mutations, with experimentally quantified effects on antibody-antigen binding. AbAgym addresses the lack of comprehensive, well-curated datasets for training computational models in antibody design, despite the abundance of mutagenesis data on antibody-antigen interactions. The dataset was constructed by collecting and curating 67 deep mutational scanning (DMS) datasets from scientific literature, along with the 3D structures of each antibody-antigen complex. When exact sequences were not available in the Protein Data Bank (PDB), homology models were used. The data processing pipeline involved collecting and formatting DMS data, processing PDB structures (including remodeling with Swiss-Model and energy minimization with GROMACS), and identifying binding interfaces using a distance-based criterion (atoms within ≤6 Å). AbAgym includes a variety of antigen types, such as SARS-CoV-2 spike protein, HIV-1 envelope protein, lysozyme, nerve growth factors, and more. The study benchmarked established force field methods and recent machine learning models for predicting changes in binding affinity upon mutation. Force field methods showed modest performance, while machine learning models performed only marginally better than random, suggesting overtraining and poor generalization in the latter. Analysis of hotspot residues responsible for immune evasion highlighted the importance of accounting for biological complexities like conformational changes and oligomeric states, which are often overlooked but significantly influence antibody-antigen binding. AbAgym is freely available for academic use on GitHub, including data and metadata tables, and all modeled PDB files. 💻Code: github.com/3BioCompBio/Abagy… 📜Paper: biorxiv.org/content/10.1101/… #AbAgym #ComputationalBiology #AntibodyDesign #MutationalAnalysis #Bioinformatics