| Autor: |
Fuchigami S; Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan., Niina T; Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan., Takada S; Department of Biophysics, Graduate School of Science, Kyoto University, Kyoto 606-8502, Japan. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of chemical theory and computation [J Chem Theory Comput] 2020 Oct 13; Vol. 16 (10), pp. 6609-6619. Date of Electronic Publication: 2020 Sep 03. |
| DOI: |
10.1021/acs.jctc.0c00234 |
| Abstrakt: |
High-speed atomic force microscopy (HS-AFM) can be used to observe the structural dynamics of biomolecules at the single-molecule level in real time under near-physiological conditions; however, its spatiotemporal resolution is limited. Complementarily, molecular dynamics (MD) simulations have higher spatiotemporal resolutions, albeit with some artifacts. Here, to integrate HS-AFM data and coarse-grained molecular dynamics (CG-MD) simulations, we develop a particle filter method that implements a sequential Bayesian data assimilation approach. We test the method in a twin experiment. First, we generate a reference HS-AFM movie from the CG-MD trajectory of a test molecule, a nucleosome; this serves as the "experimental measurement". Then, we perform a particle filter simulation with 512 particles, which captures the large-scale nucleosome structural dynamics compatible with the AFM movie. Comparing particle filter simulations with 8-8192 particles, we find that using greater numbers of particles consistently increases the likelihood of the whole AFM movie. By comparing the likelihoods for different ionic concentrations and time scale mappings, we find that the "true" concentration and time scale mapping can be inferred as the largest likelihood of the whole AFM movie but not that of each AFM image. The particle filter method provides a general approach for integrating HS-AFM data with MD simulations. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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