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<p>Protein complexes carry out many essential functions in cells, but predicting their structures requires knowing their stoichiometry, meaning how many copies of each subunit are present. This information is often unavailable, making stoichiometry prediction crucial for complexes with unknown stoichiometry. Despite its importance, few computational methods address this challenge. Here we show that combining AlphaFold3 structure prediction with information from related known protein complexes enables accurate prediction of protein complex stoichiometry. Our method generates plausible subunit combinations, builds structural models for them using AlphaFold3, ranks them using AlphaFold3 scores, and further refines predictions with template-based information when available. In the 16th community-wide Critical Assessment of Techniques for Protein Structure Prediction, our method identifies the correct stoichiometry as the top prediction for 71.4% of targets and among the top three predictions for 92.9% of targets, outperforming other methods overall. This demonstrates the complementary strengths of AlphaFold3- and template-based predictions and highlights the applicability of our approach to uncharacterized protein complexes lacking stoichiometry data.</p>

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PreStoi allows accurate prediction of protein complex stoichiometry by integrating AlphaFold3 and template information

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月28日

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  • Jian Liu,
  • Pawan Neupane,
  • Jianlin Cheng

摘要

Protein complexes carry out many essential functions in cells, but predicting their structures requires knowing their stoichiometry, meaning how many copies of each subunit are present. This information is often unavailable, making stoichiometry prediction crucial for complexes with unknown stoichiometry. Despite its importance, few computational methods address this challenge. Here we show that combining AlphaFold3 structure prediction with information from related known protein complexes enables accurate prediction of protein complex stoichiometry. Our method generates plausible subunit combinations, builds structural models for them using AlphaFold3, ranks them using AlphaFold3 scores, and further refines predictions with template-based information when available. In the 16th community-wide Critical Assessment of Techniques for Protein Structure Prediction, our method identifies the correct stoichiometry as the top prediction for 71.4% of targets and among the top three predictions for 92.9% of targets, outperforming other methods overall. This demonstrates the complementary strengths of AlphaFold3- and template-based predictions and highlights the applicability of our approach to uncharacterized protein complexes lacking stoichiometry data.