تقرير
Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy
| العنوان: | Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy |
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| المؤلفون: | Eronen, Eemeli A., Vladyka, Anton, Gerbon, Florent, Sahle, Christoph. J., Niskanen, Johannes |
| المصدر: | Journal of Physics Communications 8 (2024) 025001 |
| سنة النشر: | 2023 |
| المجموعة: | Condensed Matter Physics (Other) |
| مصطلحات موضوعية: | Condensed Matter - Soft Condensed Matter, Physics - Chemical Physics |
| الوصف: | 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. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1088/2399-6528/ad1f73 |
| URL الوصول: | http://arxiv.org/abs/2306.08512 |
| رقم الأكسشن: | edsarx.2306.08512 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1088/2399-6528/ad1f73 |
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