Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning
Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often bias...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-05-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/32668 |
| _version_ | 1811181109567815680 |
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| author | Yasuhiro Matsunaga Yuji Sugita |
| author_facet | Yasuhiro Matsunaga Yuji Sugita |
| author_sort | Yasuhiro Matsunaga |
| collection | DOAJ |
| description | Single-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. It is applied to the folding dynamics of the formin-binding protein WW domain. MD simulations over 400 μs led to an initial Markov state model (MSM), which was then "refined" using single-molecule Förster resonance energy transfer (FRET) data through hidden Markov modeling. The refined or data-assimilated MSM reproduces the FRET data and features hairpin one in the transition-state ensemble, consistent with mutation experiments. The folding pathway in the data-assimilated MSM suggests interplay between hydrophobic contacts and turn formation. Our method provides a general framework for investigating conformational transitions in other proteins. |
| first_indexed | 2024-04-11T09:14:08Z |
| format | Article |
| id | doaj.art-9d5cd7590799416dbdfc985b4247e693 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T09:14:08Z |
| publishDate | 2018-05-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-9d5cd7590799416dbdfc985b4247e6932022-12-22T04:32:25ZengeLife Sciences Publications LtdeLife2050-084X2018-05-01710.7554/eLife.32668Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learningYasuhiro Matsunaga0https://orcid.org/0000-0003-2872-3908Yuji Sugita1https://orcid.org/0000-0001-9738-9216Computational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Japan; JST PRESTO, Kawaguchi, JapanComputational Biophysics Research Team, RIKEN Center for Computational Science, Kobe, Japan; Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Japan; Laboratory for Biomolecular Function Simulation, RIKEN Center for Biosystems Dynamics Research, Kobe, JapanSingle-molecule experiments and molecular dynamics (MD) simulations are indispensable tools for investigating protein conformational dynamics. The former provide time-series data, such as donor-acceptor distances, whereas the latter give atomistic information, although this information is often biased by model parameters. Here, we devise a machine-learning method to combine the complementary information from the two approaches and construct a consistent model of conformational dynamics. It is applied to the folding dynamics of the formin-binding protein WW domain. MD simulations over 400 μs led to an initial Markov state model (MSM), which was then "refined" using single-molecule Förster resonance energy transfer (FRET) data through hidden Markov modeling. The refined or data-assimilated MSM reproduces the FRET data and features hairpin one in the transition-state ensemble, consistent with mutation experiments. The folding pathway in the data-assimilated MSM suggests interplay between hydrophobic contacts and turn formation. Our method provides a general framework for investigating conformational transitions in other proteins.https://elifesciences.org/articles/32668single-molecule experimentmolecular dynamics simulationsemi-supervised learningMarkov state modeltransfer learningtime-series analysis |
| spellingShingle | Yasuhiro Matsunaga Yuji Sugita Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning eLife single-molecule experiment molecular dynamics simulation semi-supervised learning Markov state model transfer learning time-series analysis |
| title | Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning |
| title_full | Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning |
| title_fullStr | Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning |
| title_full_unstemmed | Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning |
| title_short | Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning |
| title_sort | linking time series of single molecule experiments with molecular dynamics simulations by machine learning |
| topic | single-molecule experiment molecular dynamics simulation semi-supervised learning Markov state model transfer learning time-series analysis |
| url | https://elifesciences.org/articles/32668 |
| work_keys_str_mv | AT yasuhiromatsunaga linkingtimeseriesofsinglemoleculeexperimentswithmoleculardynamicssimulationsbymachinelearning AT yujisugita linkingtimeseriesofsinglemoleculeexperimentswithmoleculardynamicssimulationsbymachinelearning |