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<p>Recent advances in artificial intelligence have enabled accurate prediction of a protein’s stable structure solely based on its amino acid sequence. However, capturing the complete conformational landscape of a protein and its dynamic flexibility remains challenging. Here we developed modal-aligned conditional diffusion (Mac-Diff), a score-based diffusion model for generating the conformational ensembles for unseen proteins. Central to Mac-Diff is an attention module that enforces a delicate, locality-aware alignment between the conditional view (protein sequence) and the target view (residue pair geometry) to compute highly contextualized features for effective structural denoising and generation. Furthermore, Mac-Diff leverages semantically rich sequence embedding from protein language models such as ESM-2 in enforcing the protein sequence condition that captures evolutionary, structural and functional information. Mac-Diff showed promising results in generating realistic and diverse protein structures. It successfully recovered conformational distributions of fast-folding proteins, captured multiple meta-stable conformations that were observed only in long MD simulation trajectories and efficiently predicted alternative conformations for allosteric proteins. We believe that Mac-Diff offers a useful tool to improve understanding of protein dynamics and structural variability, with broad implications for structural biology, structure-based drug design and protein engineering.</p>

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Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月25日

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  • Baoli Wang,
  • Chenglin Wang,
  • Jingyang Chen,
  • Danlin Liu,
  • Changzhi Sun,
  • Jie Zhang,
  • Kai Zhang,
  • Honglin Li

摘要

Recent advances in artificial intelligence have enabled accurate prediction of a protein’s stable structure solely based on its amino acid sequence. However, capturing the complete conformational landscape of a protein and its dynamic flexibility remains challenging. Here we developed modal-aligned conditional diffusion (Mac-Diff), a score-based diffusion model for generating the conformational ensembles for unseen proteins. Central to Mac-Diff is an attention module that enforces a delicate, locality-aware alignment between the conditional view (protein sequence) and the target view (residue pair geometry) to compute highly contextualized features for effective structural denoising and generation. Furthermore, Mac-Diff leverages semantically rich sequence embedding from protein language models such as ESM-2 in enforcing the protein sequence condition that captures evolutionary, structural and functional information. Mac-Diff showed promising results in generating realistic and diverse protein structures. It successfully recovered conformational distributions of fast-folding proteins, captured multiple meta-stable conformations that were observed only in long MD simulation trajectories and efficiently predicted alternative conformations for allosteric proteins. We believe that Mac-Diff offers a useful tool to improve understanding of protein dynamics and structural variability, with broad implications for structural biology, structure-based drug design and protein engineering.