MViewEMA: Efficient Global Accuracy Estimation for Protein Complex Structural Models Using Multi-View Representation Learning

Abstract

Estimation of model accuracy (EMA) is crucial for the reliable application of protein structure models in biological research. With the rapid advancement of protein structure prediction techniques and the explosive growth of predicted structural data, existing EMA methods struggle to balance computational efficiency with estimation performance. Here, we present MViewEMA, a single-model EMA method that leverages a multi-view representation learning framework to integrate residue-residue interaction features from micro-environment, meso-environment, and macro-environment levels for global accuracy assessment of protein complex models. Benchmark results demonstrate that MViewEMA outperforms state-of-the-art EMA methods in global accuracy assessment, achieving more than a 10-fold improvement in computational efficiency compared to our previous method, DeepUMQA3. This method enables efficient selection of high-quality protein complex models from large-scale structural datasets and achieved top performance in model selection tracks during the CASP16 blind test, demonstrating its potential to enhance the accuracy of complex structure prediction when integrated into modern frameworks such as AlphaFold-Multimer, AlphaFold3, and DiffDock-PP.