LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph Model Article Swipe
Xudong Wang
,
Tong Zhang
,
Guangbu Liu
,
Zhen Cui
,
Zhiyong Zeng
,
Long Cheng
,
Wenming Zheng
,
Jian Yang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.20.566676
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.20.566676
Accurately predicting protein structure, from amino acid sequences to three-dimensional structures, is of great significance in biological research. To tackle this issue, a representative deep big model, RoseTTAFold, has been proposed with promising success. Here, we report an ultra-lightweight deep graph network , named LightRoseTTA , to achieve accurate and high-efficient prediction for proteins. Notably, three highlights are possessed by our LightRoseTTA: (i) high-accurate structure prediction for proteins, being competitive with RoseTTAFold on multiple popular datasets including CASP14 and CAMEO; (ii) high-e ffi cient training and inference with an ultra-lightweight model, costing only one week on one single general NVIDIA 3090 GPU for model-training (vs 30 days on 8 high-speed NVIDIA V100 GPUs for RoseTTAFold) and containing only 1 . 4M parameters (vs 130M in RoseTTAFold); (iii) low dependency on multi-sequence alignments (MSA, widely-used homologous information), achieving the best performance on three MSA-insu ffi cient datasets: Orphan, De novo, and Orphan25 . Besides, our LightRoseTTA is transferable from general proteins to antibody data, as verified in our experiments. We visualize some case studies to demonstrate the high-quality prediction, and provide some insights on how the structure predictions facilitate the understanding of biological functions. We further make a discussion on the time and resource costs of LightRoseTTA and RoseTTAFold, and demonstrate the feasibility of lightweight models for protein structure prediction, which may be crucial in the resource-limited research for universities and academy institutions. We release our code and model to speed biological research .
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.11.20.566676
- https://www.biorxiv.org/content/biorxiv/early/2023/11/21/2023.11.20.566676.full.pdf
- OA Status
- green
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388850670
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388850670Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.11.20.566676Digital Object Identifier
- Title
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LightRoseTTA: High-efficient and Accurate Protein Structure Prediction Using an Ultra-Lightweight Deep Graph ModelWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-11-21Full 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
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https://doi.org/10.1101/2023.11.20.566676Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/11/21/2023.11.20.566676.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2023/11/21/2023.11.20.566676.full.pdfDirect OA link when available
- Concepts
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Computer science, Inference, Protein structure prediction, Graph, Artificial intelligence, Deep learning, Machine learning, Data mining, Protein structure, Theoretical computer science, Nuclear magnetic resonance, PhysicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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