DeepUMQA-X: Comprehensive and insightful estimation of model accuracy for protein single-chain and complex.

An open protein model quality assessment server is essential for improving the accuracy of structure prediction and advancing the application of protein models in the biology community. In the post-AlphaFold2 era, protein complex structure prediction often relies on large-scale sampling for high-precision structures, while accurate scoring, ranking, and selection of protein models have become critical challenges that urgently need to be addressed. This work presents a comprehensive web server, DeepUMQA-X, which combines our single-model protocols for various evaluation metrics with a consensus strategy for protein model accuracy estimation (EMA). The server supports multiple protein single-chain or complex models as input, providing overall, interface, and residue accuracy estimates for each model. In the CASP16 EMA blind test, DeepUMQA-X achieved top performance across nearly all tracks, including QMODE1, QMODE2, QMODE3, and self-assessment. Remarkably, its single-model protocols outperformed all other single-model methods in accuracy assessment. Additionally, the server ranked first in a one-year (9 June 2023 to 1 June 2024) CAMEO-QE blind test. By integrating single-model approaches with a consensus-based strategy, DeepUMQA-X effectively bridges the performance gap between currently predominant consensus methods and the increasingly demanded single-model methods. The DeepUMQA-X server is freely available at http://zhanglab-bioinf.com/DeepUMQA-X.