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Background <p>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.</p> Methods <p>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.</p> Results <p>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).</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Soobon Ko,
  • Honglan Li,
  • Hongeun Kim,
  • Woong-Hee Shin,
  • Junsu Ko,
  • Yoonjoo Choi

摘要

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.