Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866909104197337088 |
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| author | Eronen, Eemeli A. Vladyka, Anton Gerbon, Florent Sahle, Christoph. J. Niskanen, Johannes |
| author_facet | Eronen, Eemeli A. Vladyka, Anton Gerbon, Florent Sahle, Christoph. J. Niskanen, Johannes |
| contents | We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse problem of spectrum interpretation is achieved. Structural and spectral variation across the sampled phase space is notable. Using these data, we train a neural network to predict the intensities of spectral regions of interest from the structure. These regions are defined by the temperature-difference profile of the simulated spectra, and the analysis yields a structural interpretation for their behavior. Even though the utilized local many-body tensor representation implicitly encodes the secondary structure of the peptide, our approach proves that this information is irrecoverable from the spectra. A hard X-ray Raman scattering experiment confirms the overall sensibility of the simulated spectra, but the predicted temperature-dependent effects therein remain beyond the achieved statistical confidence level. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2306_08512 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy Eronen, Eemeli A. Vladyka, Anton Gerbon, Florent Sahle, Christoph. J. Niskanen, Johannes Soft Condensed Matter Chemical Physics We apply a recently developed technique utilizing machine learning for statistical analysis of computational nitrogen K-edge spectra of aqueous triglycine. This method, the emulator-based component analysis, identifies spectrally relevant structural degrees of freedom from a data set filtering irrelevant ones out. Thus tremendous reduction in the dimensionality of the ill-posed nonlinear inverse problem of spectrum interpretation is achieved. Structural and spectral variation across the sampled phase space is notable. Using these data, we train a neural network to predict the intensities of spectral regions of interest from the structure. These regions are defined by the temperature-difference profile of the simulated spectra, and the analysis yields a structural interpretation for their behavior. Even though the utilized local many-body tensor representation implicitly encodes the secondary structure of the peptide, our approach proves that this information is irrecoverable from the spectra. A hard X-ray Raman scattering experiment confirms the overall sensibility of the simulated spectra, but the predicted temperature-dependent effects therein remain beyond the achieved statistical confidence level. |
| title | Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy |
| topic | Soft Condensed Matter Chemical Physics |
| url | https://arxiv.org/abs/2306.08512 |