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ContrastQA: A label-guided graph contrastive learning-based approach for protein complex structure quality assessment
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DOI：10.1002/pro.70734.png)
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Abstract
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Despite recent progress, the Estimation of Model Accuracy (EMA) for protein complexes remains less advanced compared to that for protein monomers. A key challenge lies in effectively integrating both interface-specific and global structural information to accurately assess the quality of protein complexes. Here, we introduce ContrastQA, the first EMA framework for protein complexes that incorporates the proposed label-guided graph contrastive learning based on interface quality. By integrating a geometric graph neural network to model global structural features, ContrastQA effectively captures both local (interface-level) and global (structure-level) information for accurate model quality estimation. ContrastQA achieved ranking losses of 0.123 and 0.116 on the TMscore and GDT-TS metrics on the CASP16 dataset, which are 0.015 (10.9%) and 0.012 (8.7%) lower than the second-best EMA method with ranking losses of 0.138 and 0.128. Our study demonstrates the strong effectiveness of the label-guided graph contrastive learning module, particularly in selecting high-quality models. These findings suggest that our graph contrastive learning framework serves as a valuable pre-training strategy for learning protein structure representations.
Keywords:
deep learning
graph contrastive learning
protein complexes
protein interface
protein quality assessment
samples selection strategy
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