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Abstract <p>Cyclic peptides with backbone N-methylated amino acids (BNMeAAs) and D-amino acids (D-AAs) have gained increasing attention for their stability, membrane permeability, and other therapeutic potentials. Currently, Rosetta simple_cycpep_predict (SCP) can predict their structures through energy-based calculations, but this approach is computationally intensive and time-consuming. Moreover, the available crystal structures of such cyclic peptides remain highly limited, hindering the development of data-driven structure prediction models. To address these challenges, we propose HighFold-MeD, a deep learning-based framework that distills knowledge from Rosetta SCP by fine-tuning the AlphaFold model. First, a cyclic peptide structure dataset is constructed using Rosetta SCP by sampling massive conformations for cyclic peptides with BNMeAAs and D-AAs and evaluating their energy scores. The AlphaFold model is then fine-tuned to incorporate the extended 56 BNMeAAs and D-AAs. Besides, a relative position cyclic matrix is introduced to explicitly model head-to-tail cyclization. Finally, a force field is employed to minimize steric clashes in the predicted structures. Empirical experiments demonstrate that HighFold-MeD achieves accuracy comparable to that of Rosetta based on the sampled datasets by the SCP module of Rosetta, with the key parameters that nstruct = 500 and cyclic_peptide: genkic_closure_attempts = 1000, while accelerating structure prediction by 50-fold, thereby significantly expediting the development of cyclic peptide-based therapeutics.</p> Scientific contribution <p>We propose HighFold-MeD, which provides a rapid and relatively accurate approach for predicting the structures of cyclic peptides containing backbone N-methylated amino acids and D-amino acids—key building blocks in peptide drug design. By distilling the knowledge of Rosetta SCP under specific parameters into a fine-tuned AlphaFold framework, our method achieves a 50-fold acceleration while maintaining relatively high accuracy, thereby enabling large-scale cyclic peptide drug design.</p>

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HighFold-MeD: a Rosetta distillation model to accelerate structure prediction of cyclic peptides with backbone N-methylation and d-amino acids

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Zhigang Cao,
  • Sen Cao,
  • Linghong Wang,
  • Zhiguo Wang,
  • Qingyi Mao,
  • Jingjing Guo,
  • Hongliang Duan

摘要

Abstract

Cyclic peptides with backbone N-methylated amino acids (BNMeAAs) and D-amino acids (D-AAs) have gained increasing attention for their stability, membrane permeability, and other therapeutic potentials. Currently, Rosetta simple_cycpep_predict (SCP) can predict their structures through energy-based calculations, but this approach is computationally intensive and time-consuming. Moreover, the available crystal structures of such cyclic peptides remain highly limited, hindering the development of data-driven structure prediction models. To address these challenges, we propose HighFold-MeD, a deep learning-based framework that distills knowledge from Rosetta SCP by fine-tuning the AlphaFold model. First, a cyclic peptide structure dataset is constructed using Rosetta SCP by sampling massive conformations for cyclic peptides with BNMeAAs and D-AAs and evaluating their energy scores. The AlphaFold model is then fine-tuned to incorporate the extended 56 BNMeAAs and D-AAs. Besides, a relative position cyclic matrix is introduced to explicitly model head-to-tail cyclization. Finally, a force field is employed to minimize steric clashes in the predicted structures. Empirical experiments demonstrate that HighFold-MeD achieves accuracy comparable to that of Rosetta based on the sampled datasets by the SCP module of Rosetta, with the key parameters that nstruct = 500 and cyclic_peptide: genkic_closure_attempts = 1000, while accelerating structure prediction by 50-fold, thereby significantly expediting the development of cyclic peptide-based therapeutics.

Scientific contribution

We propose HighFold-MeD, which provides a rapid and relatively accurate approach for predicting the structures of cyclic peptides containing backbone N-methylated amino acids and D-amino acids—key building blocks in peptide drug design. By distilling the knowledge of Rosetta SCP under specific parameters into a fine-tuned AlphaFold framework, our method achieves a 50-fold acceleration while maintaining relatively high accuracy, thereby enabling large-scale cyclic peptide drug design.