ParaSurf: A Surface-Based Deep Learning Approach for Paratope-Antigen Interaction Prediction.

Abstract

Motivation: Identifying antibody binding sites, is crucial for developing vaccines and therapeutic antibodies, processes that are time-consuming and costly. Accurate prediction of the paratope's binding site can speed up the development by improving our understanding of antibody-antigen interactions.

Results: We present ParaSurf, a deep learning model that significantly enhances paratope prediction by incorporating both surface geometric and non-geometric factors. Trained and tested on three prominent antibody-antigen benchmarks, ParaSurf achieves state-of-the-art results across nearly all metrics. Unlike models restricted to the variable region, ParaSurf demonstrates the ability to accurately predict binding scores across the entire Fab region of the antibody. Additionally, we conducted an extensive analysis using the largest of the three datasets employed, focusing on three key components: (1) a detailed evaluation of paratope prediction for each Complementarity-Determining Region loop, (2) the performance of models trained exclusively on the heavy chain, and (3) the results of training models solely on the light chain without incorporating data from the heavy chain.

Availability and implementation: Source code for ParaSurf, along with the datasets used, preprocessing pipeline, and trained model weights, are freely available at https://github.com/aggelos-michael-papadopoulos/ParaSurf.

Supplementary information: Supplementary data are available at Bioinformatics online.