Work

Germline-aware deep learning models and benchmarks for predicting antibody VH–VL pairing

Year: 2025

Type: article

Abstract: Variable heavy (VH) and variable light (VL) chain pairing is a critical determinant of antibody diversity, stability, and antigen-binding specificity. Identifying productive VH - VL combinations experimentally is labor-intensive and costly, motivating the development of computational methods that can more efficiently predict compatible heavy - light chain pairs. In this work, we present a comprehensive framework that includes a new benchmark dataset and three deep learning models, each trained with a different negative sampling strategy: random pairing, V-gene mismatching, and full V(D)J germline mismatching. Our dataset includes natural pairs and these three types of synthetic negatives to simulate increasingly realistic biological constraints. Furthermore, we present a lightweight yet highly effective BERT-based model that achieves over 90% accuracy in discriminating natural from synthetic VH - VL pairs. Through extensive evaluation, we demonstrate that V(D)J-informed negative sampling significantly improves model generalization and biological interpretability. By providing reproducible baselines and a biologically grounded benchmark, this work lays the foundation for future development of efficient computational tools in antibody engineering.

Source: mAbs

DOI: 10.1080/19420862.2025.2570749

Authors: Sara Joubbi, Enrico D’Arco, Giuseppe Maccari, Paolo Milazzo, Alessio Micheli

Institutions: University of Pisa, Toscana Life Sciences

Language: English

Topic: Monoclonal and Polyclonal Antibodies Research

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $3,575

Funder: Ministero della Salute