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Item type:Publication, Access status: with files , Dynamic structure investigation and spectra prediction of biomolecules using machine learning techniques(2024); The investigation of biomolecular structures and the prediction of their spectra using experimental and theoretical studies in the gas phase represent fundamental steps in comprehending their intrinsic properties and biological functions. Nonetheless, the complexity of the potential energy surface of biomolecules, combined with limitations in computational resources, limits the interpretation of experimental observations. Integrating supervised and unsupervised machine learning (ML) techniques into theoretical calculations is considered as an effective way to address these challenges. Infrared (IR) and X-ray absorption spectroscopy (XAS) has proven to be powerful experimental techniques to study the electronic and spatial structure of biomolecules such as peptides and proteins. Reproducing and validating the features observed in spectra resulting from these experiments often requires the use of sophisticated ab initio calculations and comprehensive understanding of biomolecules’ configurational space. In this thesis, I introduced a novel approach in interpretation of IR experimental spectrum of a peptide which aims enhancing the exploratory power of searching configurational space by combining REMD simulations, unsupervised machine learning, and ab initio calculations. This scheme relies on a set of structural descriptors and data-driven clustering technique which accounts for canonical ensemble of real experimental condition to obtain an accurate computed spectrum. We show that by partitioning the configurational space into subensembles of imilar conformations i.e. clusters, an accurate IR spectrum can be calculated by averaging the IR contribution of each representative conformer in each cluster, weighted according to the population of each cluster. While this approach unravels important fingerprints of experimental spectroscopic data, the calculation of IR and particularly XAS spectra, due to its inherently expensive theoretical computation, is often computationally prohibitive task for even medium-sized molecules. To remedy the computational obstacles associated with spectra prediction, we develope a data-driven supervised ML frameworks, i.e. graph neural networks which are trained on a custom-generated XAS dataset to find a mapping between structures and spectroscopic signals, thus bypassing the need for expensive ab initio quantum chemistry calculations. To insure the interpretability of GNN models’ predictions, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. Within this approach, we show that it is possible to link the peaks observed in the spectra to certain core and virtual orbitals from the quantum chemical calculations and obtain an in-depth understanding of the ML predicted XAS spectrum. The results presented in this thesis show that the integration of supervised and unsupervised ML techniques can effectively enhance the interpretation of spectroscopic data and make efficient use of the expensive ab initio calculations.Publicationtype: Doctoral Thesis120 265 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble(Macmillan Publishers Limited, part of Springer Nature, 2023-12); ; ; ; 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.Publicationtype: Journal ArticleScopus© Citations 4 121 112 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Integrating explainability into graph neural network models for the prediction of X-ray absorption spectra(American Chemical Society (ACS), 2023-10-09); ; ; 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.Publicationtype: Journal Article18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Mapping the electronic transitions of protonation sites in peptides using soft X-ray action spectroscopy(2023-09-18); ; Scopus© Citations 1 12 48