ContrastQA: A label‐guided graph contrastive learning‐based approach for protein complex structure quality assessment

Abstract

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.