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bioRxiv · 10.1101/2022.08.12.503819

Improved model quality assessment using sequence and structural information by enhanced deep neural networks

Liu, J.·Zhao, K.·Zhang, G.
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Abstract

Protein model quality assessment plays an important role in protein structure prediction, protein design, and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5% and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (March 11 to June 04, 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, DeepAccNet, Atom_ProteinQA, and QMEAN3.

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Liu, J., Zhao, K., Zhang, G.. 2022-08-15. Improved model quality assessment using sequence and structural information by enhanced deep neural networks. https://doi.org/10.1101/2022.08.12.503819

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