A unified framework for coarse grained molecular dynamics of proteins
Understanding protein dynamics is crucial for elucidating their biological functions. While all-atom molecular dynamics (MD) simulations provide detailed information, coarse-grained (CG) MD simulations capture the essential collective motions of proteins at significantly lower computational cost. In this article, we present a unified framework for coarse-grained molecular dynamics simulation of proteins. Our approach utilizes a tree-structured representation of collective variables, enabling reconstruction of protein Cartesian coordinates with high fidelity. The force field is constructed using a deep neural network trained on trajectories generated from conventional all-atom MD simulations. We demonstrate the framework's effectiveness using the 168-amino protein target T1027 from CASP14. Statistical distributions of the collective variables and time series of root mean square deviation (RMSD) obtained from our coarse-grained simulations closely resemble those from all-atom MD simulations. This method is not only useful for studying the movements of complex proteins, but also has the potential to be adapted for simulating other biomolecules like DNA, RNA, and even electrolytes in batteries..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 26. März Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhu, Jinzhen [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
530 |
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| PPN (Katalog-ID): |
XAR043054358 |
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| 520 | |a Understanding protein dynamics is crucial for elucidating their biological functions. While all-atom molecular dynamics (MD) simulations provide detailed information, coarse-grained (CG) MD simulations capture the essential collective motions of proteins at significantly lower computational cost. In this article, we present a unified framework for coarse-grained molecular dynamics simulation of proteins. Our approach utilizes a tree-structured representation of collective variables, enabling reconstruction of protein Cartesian coordinates with high fidelity. The force field is constructed using a deep neural network trained on trajectories generated from conventional all-atom MD simulations. We demonstrate the framework's effectiveness using the 168-amino protein target T1027 from CASP14. Statistical distributions of the collective variables and time series of root mean square deviation (RMSD) obtained from our coarse-grained simulations closely resemble those from all-atom MD simulations. This method is not only useful for studying the movements of complex proteins, but also has the potential to be adapted for simulating other biomolecules like DNA, RNA, and even electrolytes in batteries. | ||
| 650 | 4 | |a Physics - Chemical Physics |7 (dpeaa)DE-84 | |
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