Geometric Epitope and Paratope Prediction

Marco Pegoraro, Clémentine Carla Juliette Dominé, Emanuele Rodolà, Petar Veličković, Andreea Deac

Keywords: graph learning, learning on surface, drug discovery, paratope-epitope prediciton

TL;DR: GEP (Geometric Epitope-Paratope) Prediction proposes different geometric representations of the molecules to create accurate predictors for predicting antibody-antigen binding sites.

Abstract: Antibody-antigen interactions play a crucial role in identifying and neutralizing harmful foreign molecules. In this paper, we investigate the optimal representation for predicting the binding sites in the two molecules and emphasize the importance of geometric information. Specifically, we compare different geometric deep learning methods applied to proteins' inner (I-GEP) and outer (O-GEP) structures. We incorporate 3D coordinates and spectral geometric descriptors as input features to fully leverage the geometric information. Our research suggests that surface-based models are more efficient than other methods, and our O-GEP experiments have achieved state-of-the-art results with significant performance improvements.

Submission Type: Extended abstract (max 4 main pages).

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Submission Number: 30