Dataset Information

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

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 13.1% 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 successfully predicts the interface (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.

SUBMITTER: Xia Y

PROVIDER: S-EPMC10692239 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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Publications

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Xia Yuhao Y Zhao Kailong K Liu Dong D Zhou Xiaogen X Zhang Guijun G

Communications biology 20231201 1

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 popula ...[more]

PMID: 38040847

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Dataset Information

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Publications

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

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