Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER

Zheng, Wei; Wuyun, Qiqige; Li, Yang; Liu, Quancheng; Zhou, Xiaogen; Peng, Chunxiang; Zhu, Yiheng; Freddolino, Lydia; Zhang, Yang

doi:10.1038/s41587-025-02654-4

articleNature BiotechnologyMay 23, 2025HYBRID OA

Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER

WZWei Zheng QWQiqige Wuyun YLYang Li QLQuancheng Liu XZXiaogen Zhou

Nankai University · University of Michigan · +3 more institutions

PubMed

Indexed incrossrefpubmed

Abstract

The dominant success of deep learning techniques on protein structure prediction has challenged the necessity and usefulness of traditional force field-based folding simulations. We proposed a hybrid approach, deep-learning-based iterative threading assembly refinement (D-I-TASSER), which constructs atomic-level protein structural models by integrating multisource deep learning potentials with iterative threading fragment assembly simulations. D-I-TASSER introduces a domain splitting and assembly protocol for the automated modeling of large multidomain protein structures. Benchmark tests and the most recent critical assessment of protein structure prediction, 15 experiments demonstrate that D-I-TASSER…

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77

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FWCI: 50.12
Percentile: 100%
References: 97

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Topics & keywords

Topics

Keywords

Protein structure prediction
Domain (mathematical analysis)
Computational biology
Artificial intelligence
Computer science
Protein structure
Chemistry
Biology

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