Benchmarking sequence-based and AlphaFold-based methods for pMHC-II binding core prediction: distinct strengths and consensus approaches.

BACKGROUND: Interactions between peptide and MHC class II (pMHC-II) are crucial for T-cell recognition and immune responses, as MHC-II molecules present peptide fragments to T cells, enabling the distinction between self and non-self antigens. Accurately predicting the pMHC-II binding core is particularly important because it provides insights into pMHC-II interactions and T-cell receptor engagement. Given the high polymorphism and peptide-binding promiscuity of MHC-II molecules, computational prediction methods are essential for understanding pMHC-II interactions. While sequence-based methods are widely used, recent advances in AlphaFold-based structure prediction have opened new possibilities for improving pMHC-II binding core predictions.

METHODS: We constructed a non-redundant dataset of 72 pMHC-II complexes from the IMGT database, supplemented with shuffled negative peptides and curated non-binders. Two sequence-based methods (NetMHCIIpan-4.3 and DeepMHCII) and two structure-based methods (AlphaFold2 fine-tuned (AF-FT) and AlphaFold3 (AF3)) were benchmarked for binding and core prediction. Performance was evaluated using standard metrics (precision, recall, F1 score), and consensus strategies integrating sequence- and structure-based models were developed for scenarios with known and unknown binding status.

RESULTS: The AlphaFold-based methods showed strong performance in predicting positive binders, with AF3 achieving the highest positive recall (0.86) and AF2-FT performing similarly (0.81). However, both methods frequently misclassified unbound peptides as binders. NetMHCIIpan excelled at identifying non-binders, achieving the highest negative recall (0.93), but had lower positive recall (0.44). In contrast, DeepMHCII demonstrated moderate performance without any notable strength. Consensus approaches combining AlphaFold-based methods for binder identification with filtering using NetMHCIIpan improved overall prediction precision (0.94 and 0.87 for known and unknown binding status, respectively).

CONCLUSIONS: This study highlights the complementary strengths of AlphaFold-based and sequence-based methods for predicting pMHC-II binding core regions. AlphaFold-based methods excel in predicting positive binders, while NetMHCIIpan is highly effective at identifying non-binders. Future research should focus on improving the prediction of unbound peptides for AlphaFold-based models. Since NetMHCIIpan's binding core predictive ability is already high, future efforts should concentrate on enhancing its binding prediction to further improve overall accuracy.