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

Abstract

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.

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Cited by in corpus (3)

• Structural Descriptors and Information Extraction from X-ray Emission Spectra: Aqueous Sulfuric Acid
• Implementation of the Emulator-based Component Analysis
• Encoder-Decoder Neural Networks in Interpretation of X-ray Spectra