Information bottleneck in peptide conformation determination by x-ray absorption spectroscopy
: Eronen Eemeli A., Vladyka Anton, Gerbon Florent, Sahle Christoph J., Niskanen Johannes
Publisher: IOP Publishing
: 2024
: Journal of Physics Communications
: J. Phys. Commun.
: 025001
: 8
: 2
DOI: https://doi.org/10.1088/2399-6528/ad1f73(external)
: https://iopscience.iop.org/article/10.1088/2399-6528/ad1f73(external)
: https://research.utu.fi/converis/portal/detail/Publication/386797904(external)
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.