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We report a statistical analysis of computational nitrogen K-edge spectra ofaqueous triglycine. Structural and spectral variation across the sampled phasespace is notable and can be traced to the collaborative action of numerousstructural degrees of freedom. We train a neural network to predict theintensities of spectral regions of interest from the structure. These regionsare defined by the temperature-difference profile of the simulated spectra, andthe analysis yields a structural interpretation for their behaviour. Eventhough the utilized local many-body tensor representation implicitly encodesthe secondary structure of the peptide, the applied emulator-based componentanalysis proves that this information is irrecoverable from the spectra. A hardX-ray Raman scattering experiment confirms the overall sensibility of thesimulated spectra, but the predicted temperature-dependent effects thereinremain beyond the achieved statistical confidence level.

Information Bottleneck in Peptide Conformation Determination by X-ray Absorption Spectroscopy