AntiSite: Modality Dropout Enables Antibody Paratope Prediction With or Without Structure From a Single Model
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Summary
Reliable paratope identification is central to understanding antibody–antigen recognition and advancing therapeutic antibody discovery. AntiSite is a unified antibody paratope prediction framework that combines protein-language-model sequence embeddings with structure-derived molecular-surface features and, through modality dropout , trains a single checkpoint to predict both with and without a structure. This lets one model support sequence-only inference when no structure is available and structure-aware inference when an antibody structure is provided.
Availability and implementation
Source code, trained models and evaluation scripts are freely available at https://github.com/aggelos-michael-papadopoulos/AntiSite . Processed benchmark structures and corrected split metadata are archived on Zenodo at https://doi.org/10.5281/zenodo.21705412 .
Contact
angepapa@iti.gr
Supplementary information
Supplementary data are available at Bioinformatics online.
