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Background <p>Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational methods have been developed to allow rapid and efficient large-scale screening of peptide toxicity. However, these methods mainly rely on the primary sequence and often ignore critical structural information, which limits their predictive accuracy.</p> Results <p>In this study, we introduce a novel framework named StrucToxNet that integrates a pre-trained protein language model with an equivariant graph neural network to improve peptide toxicity prediction. By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. Testing on the independent dataset indicates that StrucToxNet outperforms existing sequence-based models in various metrics, achieving higher balanced accuracy and overall performance.</p> Conclusions <p>The results demonstrate the robustness and generalizability of StrucToxNet, marking it a reliable tool in the computational screening of toxic peptides and facilitating safer peptide-based drug development.</p>

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Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月28日

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  • Shihu Jiao,
  • Xiucai Ye,
  • Tetsuya Sakurai,
  • Quan Zou,
  • Wu Han,
  • Chao Zhan

摘要

Background

Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in drug development. Several computational methods have been developed to allow rapid and efficient large-scale screening of peptide toxicity. However, these methods mainly rely on the primary sequence and often ignore critical structural information, which limits their predictive accuracy.

Results

In this study, we introduce a novel framework named StrucToxNet that integrates a pre-trained protein language model with an equivariant graph neural network to improve peptide toxicity prediction. By combining sequence embeddings from the ProtT5 language model and 3D structural data predicted by ESMFold, StrucToxNet can capture both sequential and spatial characteristics of peptides. Testing on the independent dataset indicates that StrucToxNet outperforms existing sequence-based models in various metrics, achieving higher balanced accuracy and overall performance.

Conclusions

The results demonstrate the robustness and generalizability of StrucToxNet, marking it a reliable tool in the computational screening of toxic peptides and facilitating safer peptide-based drug development.