DeepUMQA3: a web server for model quality assessment of protein complexes

Abstract

Model quality assessment is a crucial part of protein structure prediction and a gateway to proper usage of models in biomedical applications. Many methods have been proposed for assessing the quality of structural models of protein monomers, but few methods for evaluating protein complex models. As protein complex structure prediction becomes a new challenge, model quality assessment methods that can provide accurate evaluation of complex structures are urgently required. Here, we present DeepUMQA3, a web server for evaluating protein complex structures using deep neural network. For an input complex structure, features are extracted from three levels of overall complex, intra-monomer, and inter-monomer, and a improved deep residual neural network is used to predict per-residue lDDT and interface residue accuracy. DeepUMQA3 ranks first in the blind test of interface residue accuracy estimation in CASP15, with Pearson, Spearman and AUC of 0.564, 0.535 and 0.755 under the lDDT measurement, which are 18.5%, 23.6% and 10.9% higher than the second-best method, respectively. DeepUMQA3 can also accurately assess the accuracy of all residues in the entire complex and distinguish high- and low-precision residues/models. The websever of DeepUMQA3 are freely available at http://zhanglab-bioinf.com/DeepUMQA_server/.