mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics
Abstract Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and...
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| Format: | Article |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04140-z |
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| author | Antonio Mirarchi Toni Giorgino Gianni De Fabritiis |
| author_facet | Antonio Mirarchi Toni Giorgino Gianni De Fabritiis |
| author_sort | Antonio Mirarchi |
| collection | DOAJ |
| description | Abstract Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science. |
| format | Article |
| id | doaj-art-7f9e58c97a4b45afb504babdd4224d02 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-7f9e58c97a4b45afb504babdd4224d022024-12-01T12:09:02ZengNature PortfolioScientific Data2052-44632024-11-0111111110.1038/s41597-024-04140-zmdCATH: A Large-Scale MD Dataset for Data-Driven Computational BiophysicsAntonio Mirarchi0Toni Giorgino1Gianni De Fabritiis2Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)Biophysics Institute, National Research Council (CNR-IBF)Computational Science Laboratory, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB)Abstract Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.https://doi.org/10.1038/s41597-024-04140-z |
| spellingShingle | Antonio Mirarchi Toni Giorgino Gianni De Fabritiis mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics Scientific Data |
| title | mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics |
| title_full | mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics |
| title_fullStr | mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics |
| title_full_unstemmed | mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics |
| title_short | mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics |
| title_sort | mdcath a large scale md dataset for data driven computational biophysics |
| url | https://doi.org/10.1038/s41597-024-04140-z |
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