AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics
Abstract Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02465-9 |
| _version_ | 1797578232566906880 |
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| author | Tong Wang Xinheng He Mingyu Li Bin Shao Tie-Yan Liu |
| author_facet | Tong Wang Xinheng He Mingyu Li Bin Shao Tie-Yan Liu |
| author_sort | Tong Wang |
| collection | DOAJ |
| description | Abstract Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy. |
| first_indexed | 2024-03-10T22:19:06Z |
| format | Article |
| id | doaj.art-25bd12afe3bd4e0c8d83896c23b8488c |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-10T22:19:06Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-25bd12afe3bd4e0c8d83896c23b8488c2023-11-19T12:19:17ZengNature PortfolioScientific Data2052-44632023-08-0110111210.1038/s41597-023-02465-9AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamicsTong Wang0Xinheng He1Mingyu Li2Bin Shao3Tie-Yan Liu4Microsoft Research AI4ScienceMicrosoft Research AI4ScienceMicrosoft Research AI4ScienceMicrosoft Research AI4ScienceMicrosoft Research AI4ScienceAbstract Molecular dynamics (MD) simulations have revolutionized the modeling of biomolecular conformations and provided unprecedented insight into molecular interactions. Due to the prohibitive computational overheads of ab initio simulation for large biomolecules, dynamic modeling for proteins is generally constrained on force field with molecular mechanics, which suffers from low accuracy as well as ignores the electronic effects. Here, we report AIMD-Chig, an MD dataset including 2 million conformations of 166-atom protein Chignolin sampled at the density functional theory (DFT) level with 7,763,146 CPU hours. 10,000 conformations were initialized covering the whole conformational space of Chignolin, including folded, unfolded, and metastable states. Ab initio simulations were driven by M06-2X/6-31 G* with a Berendsen thermostat at 340 K. We reported coordinates, energies, and forces for each conformation. AIMD-Chig brings the DFT level conformational space exploration from small organic molecules to real-world proteins. It can serve as the benchmark for developing machine learning potentials for proteins and facilitate the exploration of protein dynamics with ab initio accuracy.https://doi.org/10.1038/s41597-023-02465-9 |
| spellingShingle | Tong Wang Xinheng He Mingyu Li Bin Shao Tie-Yan Liu AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics Scientific Data |
| title | AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics |
| title_full | AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics |
| title_fullStr | AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics |
| title_full_unstemmed | AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics |
| title_short | AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics |
| title_sort | aimd chig exploring the conformational space of a 166 atom protein chignolin with ab initio molecular dynamics |
| url | https://doi.org/10.1038/s41597-023-02465-9 |
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