Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy
Abstract Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limi...
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| Format: | Article |
| Language: | English |
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Wiley
2024-11-01
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| Series: | Advanced Science |
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| Online Access: | https://doi.org/10.1002/advs.202400884 |
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| author | Yao Hu Hao Yang Mingwei Li Zhicheng Zhong Yongqi Zhou Fang Bai Qian Wang |
| author_facet | Yao Hu Hao Yang Mingwei Li Zhicheng Zhong Yongqi Zhou Fang Bai Qian Wang |
| author_sort | Yao Hu |
| collection | DOAJ |
| description | Abstract Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large‐scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human β‐cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes. |
| format | Article |
| id | doaj-art-f8b0bf1b3c494124945007c66190b7ab |
| institution | Kabale University |
| issn | 2198-3844 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj-art-f8b0bf1b3c494124945007c66190b7ab2024-11-27T11:21:53ZengWileyAdvanced Science2198-38442024-11-011144n/an/a10.1002/advs.202400884Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning StrategyYao Hu0Hao Yang1Mingwei Li2Zhicheng Zhong3Yongqi Zhou4Fang Bai5Qian Wang6Department of Physics University of Science and Technology of China Hefei Anhui 230026 ChinaShanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology ShanghaiTech University 393 Middle Huaxia Road Shanghai 201210 ChinaDepartment of Physics University of Science and Technology of China Hefei Anhui 230026 ChinaDepartment of Physics University of Science and Technology of China Hefei Anhui 230026 ChinaShanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology ShanghaiTech University 393 Middle Huaxia Road Shanghai 201210 ChinaShanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology ShanghaiTech University 393 Middle Huaxia Road Shanghai 201210 ChinaDepartment of Physics University of Science and Technology of China Hefei Anhui 230026 ChinaAbstract Inspired by the success of deep learning in predicting static protein structures, researchers are now actively exploring other deep learning algorithms aimed at predicting the conformational changes of proteins. Currently, a major challenge in the development of such models lies in the limited training data characterizing different conformational transitions. To address this issue, molecular dynamics simulations is combined with enhanced sampling methods to create a large‐scale database. To this end, the study simulates the conformational changes of 2635 proteins featuring two known stable states, and collects the structural information along each transition pathway. Utilizing this database, a general deep learning model capable of predicting the transition pathway for a given protein is developed. The model exhibits general robustness across proteins with varying sequence lengths (ranging from 44 to 704 amino acids) and accommodates different types of conformational changes. Great agreement is shown between predictions and experimental data in several systems and successfully apply this model to identify a novel allosteric regulation in an important biological system, the human β‐cardiac myosin. These results demonstrate the effectiveness of the model in revealing the nature of protein conformational changes.https://doi.org/10.1002/advs.202400884conformational changesdeep learningproteinstransition pathway |
| spellingShingle | Yao Hu Hao Yang Mingwei Li Zhicheng Zhong Yongqi Zhou Fang Bai Qian Wang Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy Advanced Science conformational changes deep learning proteins transition pathway |
| title | Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy |
| title_full | Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy |
| title_fullStr | Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy |
| title_full_unstemmed | Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy |
| title_short | Exploring Protein Conformational Changes Using a Large‐Scale Biophysical Sampling Augmented Deep Learning Strategy |
| title_sort | exploring protein conformational changes using a large scale biophysical sampling augmented deep learning strategy |
| topic | conformational changes deep learning proteins transition pathway |
| url | https://doi.org/10.1002/advs.202400884 |
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