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bioRxiv · 10.1101/2023.05.05.539574

Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning

Xia, Y.·Zhao, K.·Liu, D.·Zhou, X.·Zhang, G.
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Abstract

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 11.8% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly generates models with acceptable quality (DockQ [≥] 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

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Xia, Y., Zhao, K., Liu, D., Zhou, X., Zhang, G.. 2023-05-08. Domain-based multi-domain protein and complex structure prediction using inter-domain interactions from deep learning. https://doi.org/10.1101/2023.05.05.539574

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