FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction

Joshi, D.; Priyadarsinee, L.; Chatterjee, B.; Natarajan, A. M.; Sastry, G. N.

doi:10.64898/2026.10.31.763304

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bioRxiv · 10.64898/2026.10.31.763304

FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction

Joshi, D.·Priyadarsinee, L.·Chatterjee, B.·Natarajan, A. M.·Sastry, G. N.

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Abstract

Linear B-cell epitopes are antigenic regions recognised by antibodies that play crucial roles in vaccine development, antibody production, and other therapeutic applications.Their prediction can support peptide-based vaccine candidate screening and diagnostic assay development, but the experimental data required for model development may be distributed across institutions that cannot freely pool their records. We present FedEpitope, a federated framework that adapts the ESM-2 protein language model for peptide-level linear B-cell epitope prediction using Low-Rank Adaptation (LoRA), keeping client-partitioned data local and restricting the communicated trainable model state to approximately 1.11 MB. Simulated benchmarks of FedAvg and FedProx algorithms, evaluated using homology-aware data splits of five biologically heterogeneous, organism-partitioned clients, achieve promising global test performance without centralising training data, with FedAvg outperforming all single-client models in mean AP, F1, and MCC. Additional experiments characterise post-federation personalisation, record-level differential privacy, sensitivity to controlled client heterogeneity, LoRA rank, and generalisation to the independent BCPreds and NetBCE benchmarks, demonstrating the feasibility of parameter-efficient federated protein language model adaptation for data-localised B-cell epitope prediction and the associated trade-offs among predictive performance, personalisation, privacy, communication efficiency, and distribution shift.

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Joshi, D., Priyadarsinee, L., Chatterjee, B., Natarajan, A. M., Sastry, G. N.. 2026-11-03. FedEpitope: Parameter-Efficient Federated Protein Language Models for Linear B-Cell Epitope Prediction. https://doi.org/10.64898/2026.10.31.763304

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