LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model Article Swipe
Xudong Wang
,
Tong Zhang
,
Guangbu Liu
,
Zhen Cui
,
Zhiyong Zeng
,
Long Cheng
,
Wenming Zheng
,
Jian Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202309051
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202309051
Accurately predicting protein structure, from sequences to 3D structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, is proposed with promising success. Here, “a light‐weight deep graph network, named LightRoseTTA,” is reported to achieve accurate and highly efficient prediction for proteins. Notably, three highlights are possessed by LightRoseTTA: i) high‐accurate structure prediction for proteins, being “competitive with RoseTTAFold” on multiple popular datasets including CASP14 and CAMEO; ii) high‐efficient training and inference with a light‐weight model, costing “only 1 week on one single NVIDIA 3090 GPU for model‐training” (vs 30 days on 8 NVIDIA V100 GPUs for RoseTTAFold) and containing “only 1.4M parameters” (vs 130M in RoseTTAFold); iii) low dependency on multi‐sequence alignment (MSA), achieving the best performance on three MSA‐insufficient datasets: Orphan, De novo, and Orphan25. Besides, LightRoseTTA is “transferable” from general proteins to antibody data, as verified in the experiments. The time and resource costs of LightRoseTTA and RoseTTAFold are further discussed to demonstrate the feasibility of light‐weight models for protein structure prediction, which may be crucial in resource‐limited research for universities and academic institutions. The code and model are released to speed biological research ( https://github.com/psp3dcg/LightRoseTTA ).
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202309051
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202309051
- OA Status
- gold
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408884609
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- OpenAlex ID
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https://openalex.org/W4408884609Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/advs.202309051Digital Object Identifier
- Title
-
LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph ModelWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-03-25Full publication date if available
- Authors
-
Xudong Wang, Tong Zhang, Guangbu Liu, Zhen Cui, Zhiyong Zeng, Long Cheng, Wenming Zheng, Jian YangList of authors in order
- Landing page
-
https://doi.org/10.1002/advs.202309051Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202309051Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202309051Direct OA link when available
- Concepts
-
Computer science, Inference, Protein structure prediction, Graph, Deep learning, Artificial intelligence, Dependency (UML), Machine learning, Protein structure, Theoretical computer science, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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