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Background <p>Accurate prediction of protein complex structures remains a significant challenge, particularly when stoichiometry information is unavailable. In the recent Critical Assessment of Structure Prediction Round XVI (CASP16), the “Phase 0” challenge was introduced to stimulate progress in this area. However, existing evaluation tools, such as OpenStructure, might introduce systematic biases when evaluating models with stoichiometries different from the target, sometimes favoring those with excess subunits and inflating scores for models with incorrect stoichiometries.</p> Results <p>To address this issue, we developed the Reciprocal Best Matching (RBM) pipeline. RBM compares predicted and target structures by bidirectionally matching interfaces and assigning penalizations to unmatched interfaces. This approach penalizes incorrect stoichiometries in a consistent and unbiased manner while preserving strong correlation with established CASP metrics. Application of RBM in CASP16 assessments revealed improved discrimination between correctly and incorrectly stoichiometric models.</p> Conclusions <p>Our method, RBM, could correct the systematic bias in the existing assessment protocol for protein complex structure prediction without stoichiometry information. We provide a standalone software implementation of our RBM pipeline to stimulate further method development in protein complex structure prediction and to support future CASP experiments.</p>

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Reciprocal best matching: a new pipeline for scoring models with unknown stoichiometry in CASP experiments

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  • 发表日期：

    2026年4月17日

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  • Rongqing Yuan,
  • Jing Zhang,
  • Qian Cong

摘要

Background

Accurate prediction of protein complex structures remains a significant challenge, particularly when stoichiometry information is unavailable. In the recent Critical Assessment of Structure Prediction Round XVI (CASP16), the “Phase 0” challenge was introduced to stimulate progress in this area. However, existing evaluation tools, such as OpenStructure, might introduce systematic biases when evaluating models with stoichiometries different from the target, sometimes favoring those with excess subunits and inflating scores for models with incorrect stoichiometries.

Results

To address this issue, we developed the Reciprocal Best Matching (RBM) pipeline. RBM compares predicted and target structures by bidirectionally matching interfaces and assigning penalizations to unmatched interfaces. This approach penalizes incorrect stoichiometries in a consistent and unbiased manner while preserving strong correlation with established CASP metrics. Application of RBM in CASP16 assessments revealed improved discrimination between correctly and incorrectly stoichiometric models.

Conclusions

Our method, RBM, could correct the systematic bias in the existing assessment protocol for protein complex structure prediction without stoichiometry information. We provide a standalone software implementation of our RBM pipeline to stimulate further method development in protein complex structure prediction and to support future CASP experiments.