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- W4211166941 abstract "Accurate potential energy landscapes are key for predicting the behavior of many systems ranging on scales from astronomy to nanoscience. In particular, potential energy landscapes of protein configurations can be used to predict stable states, rates of change, and binding energies. However, measuring such protein potential energy landscapes is difficult because dynamics are rapid, noise is large, and thermal kicks often are often stronger than the potentials themselves. Here we describe a way to overcome these difficulties by inferring potential energy landscapes using FRET measurements paired with Bayesian nonparametric machine learning inference." @default.
- W4211166941 created "2022-02-13" @default.
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- W4211166941 date "2022-02-01" @default.
- W4211166941 modified "2023-10-05" @default.
- W4211166941 title "Inferring potentials with FRET" @default.
- W4211166941 doi "https://doi.org/10.1016/j.bpj.2021.11.2801" @default.
- W4211166941 hasPublicationYear "2022" @default.
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