Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra

The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models' predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum.

Mapping the electronic transitions of protonation sites in peptides using soft X-ray action spectroscopy

Near-edge X-ray absorption mass spectrometry (NEXAMS) around the nitrogen and oxygen K-edges was employed on gas-phase peptides to probe the electronic transitions related to their protonation sites, namely at basic side chains, the N-terminus and the amide oxygen. The experimental results are supported by replica exchange molecular dynamics and density-functional theory and restricted open-shell configuration with single calculations to attribute the transitions responsible for the experimentally observed resonances. We studied five tailor-made glycine-based pentapeptides, where we identified the signature of the protonation site of N-terminal proline, histidine, lysine and arginine, at 406 eV, corresponding to N 1s → σ*(NHx+) (x = 2 or 3) transitions, depending on the peptides. We compared the spectra of pentaglycine and triglycine to evaluate the sensitivity of NEXAMS to protomers. Separate resonances have been identified to distinguish two protomers in triglycine, the protonation site at the N-terminus at 406 eV and the protonation site at the amide oxygen characterized by a transition at 403.1 eV.

Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble

Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data.