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<p>Protein structure prediction from a single sequence has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here we propose a two-dimensional geometric template diffusion method, named TDFold, to generate high-quality pairwise geometries (including pairwise distances and orientations). These are subsequently used for accurate and highly efficient three-dimensional protein structure prediction. Given a protein sequence, TDFold infers three-dimensional structure via a network architecture consisting of two stages: two-dimensional geometric template generation and sequence-geometry collaborative learning. TDFold presents three key advantages compared with existing protein language models (for example, ESMFold and OmegaFold) and homology-based methods (for example, AlphaFold2, AlphaFold3 and RoseTTAFold): better single-sequence-based prediction performance, lower resource consumption and higher efficiency in inference. This work demonstrates the model effectiveness on homology-insufficient datasets such as Orphan and Orphan25 and popular CASP benchmarks, introducing an alternative solution for single-sequence protein structure prediction. It also accelerates protein-related research, particularly for resource-limited universities and academic institutions.</p>

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Two-dimensional geometric template diffusion for boosting single-sequence protein structure prediction

  • 期刊论文

  • 发表日期：

    2026年4月1日

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  • Xudong Wang,
  • Tong Zhang,
  • Zhen Cui,
  • Xu Guo,
  • Fuyun Wang,
  • Yuanzhi Wang,
  • Xing Cai,
  • Wenming Zheng

摘要

Protein structure prediction from a single sequence has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here we propose a two-dimensional geometric template diffusion method, named TDFold, to generate high-quality pairwise geometries (including pairwise distances and orientations). These are subsequently used for accurate and highly efficient three-dimensional protein structure prediction. Given a protein sequence, TDFold infers three-dimensional structure via a network architecture consisting of two stages: two-dimensional geometric template generation and sequence-geometry collaborative learning. TDFold presents three key advantages compared with existing protein language models (for example, ESMFold and OmegaFold) and homology-based methods (for example, AlphaFold2, AlphaFold3 and RoseTTAFold): better single-sequence-based prediction performance, lower resource consumption and higher efficiency in inference. This work demonstrates the model effectiveness on homology-insufficient datasets such as Orphan and Orphan25 and popular CASP benchmarks, introducing an alternative solution for single-sequence protein structure prediction. It also accelerates protein-related research, particularly for resource-limited universities and academic institutions.