LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model
Abstract 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,” i...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202309051 |
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| author | Xudong Wang Tong Zhang Guangbu Liu Zhen Cui Zhiyong Zeng Cheng Long Wenming Zheng Jian Yang |
| author_facet | Xudong Wang Tong Zhang Guangbu Liu Zhen Cui Zhiyong Zeng Cheng Long Wenming Zheng Jian Yang |
| author_sort | Xudong Wang |
| collection | DOAJ |
| description | Abstract 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). |
| format | Article |
| id | doaj-art-cca17df6818d4240a5fedea85b953922 |
| institution | OA Journals |
| issn | 2198-3844 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-cca17df6818d4240a5fedea85b9539222025-08-20T02:26:18ZengWileyAdvanced Science2198-38442025-05-011219n/an/a10.1002/advs.202309051LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph ModelXudong Wang0Tong Zhang1Guangbu Liu2Zhen Cui3Zhiyong Zeng4Cheng Long5Wenming Zheng6Jian Yang7School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaSchool of Automation Nanjing University of Science and Technology Nanjing 210094 ChinaSchool of Computer Engineering Nanyang Technological University No. 50, Nanyang Avenue Singapore 639798 SingaporeSchool of Biological Science & Medical Engineering Southeast University Nanjing 210096 ChinaSchool of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 ChinaAbstract 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).https://doi.org/10.1002/advs.202309051graph neural networklight‐weight deep learning modelprotein structure prediction |
| spellingShingle | Xudong Wang Tong Zhang Guangbu Liu Zhen Cui Zhiyong Zeng Cheng Long Wenming Zheng Jian Yang LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model Advanced Science graph neural network light‐weight deep learning model protein structure prediction |
| title | LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model |
| title_full | LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model |
| title_fullStr | LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model |
| title_full_unstemmed | LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model |
| title_short | LightRoseTTA: High‐Efficient and Accurate Protein Structure Prediction Using a Light‐Weight Deep Graph Model |
| title_sort | lightrosetta high efficient and accurate protein structure prediction using a light weight deep graph model |
| topic | graph neural network light‐weight deep learning model protein structure prediction |
| url | https://doi.org/10.1002/advs.202309051 |
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