PREpiBind: Protein Representation-integrated Epitope–MHC Class II Binding Prediction

Abstract

Motivation Accurate peptide–MHC class II (pMHC-II) binding prediction is complicated by MHC polymorphism and context-dependent peptide recognition. Protein representations are usually evaluated in different pipelines, obscuring their contribution to predictive performance. Results We present PREpiBind, a joint-attention framework comparing ten protein representations under a fixed architecture and identical data splits across qualitative binding, mass spectrometry, and thresholded IC50 datasets. PLMs generally achieved the strongest pooled performance; ESM3 Large reached a ROC-AUC of 0.927 ± 0.002 on the qualitative dataset. PREpiBind PLM variants exceeded evaluated reference methods on qualitative and mass-spectrometry benchmarks, whereas NetMHCIIpan-4.3 was higher on thresholded IC50 using its binding-affinity head. PLM advantages narrowed in allele-wise and held-out MHC-molecule evaluations, and H2-out rankings depended on aggregation, indicating scenario-dependent representation performance. Availability and implementation Source code and the training and evaluation datasets are available at https://github.com/daylight-00/PREpiBind and archived at https://doi.org/10.5281/zenodo.22934281; trained model checkpoints are at https://huggingface.co/daylight-00/prepibind. Implemented in Python and released under the MIT License. Contact nicole23@snu.ac.kr Supplementary information Supplementary data are available online.