Research Summary

Exploring Reactivity and Catalytic Potential of Aluminum Clusters: Site Selectivity, Nitrogen Activation, and Carbon-Halogen Bond Dissociation

This research investigates various aspects of aluminum clusters using density functional theory (DFT) calculations. The studies explore the reactivity and catalytic potential of aluminum clusters in different contexts.

The first aspect focuses on understanding the site selectivity within small-sized neutral and charged aluminum clusters. By employing reactivity descriptors, the researchers identify potential sites for adsorption and chemical reactions within the clusters. The results reveal that the reactivity of the clusters is influenced by their size, shape, and charge, affecting the availability of electrophilic and nucleophilic attack sites. Validation is performed through water molecule adsorption studies, confirming the most stable water-cluster complex forms on the site with the highest relative electrophilicity.

The second aspect investigates the activation of molecular nitrogen (N2) by silicon and phosphorus-doped aluminum clusters. The research highlights the enhanced N2 activation observed in the doped clusters compared to pristine clusters and high-energy counterparts. Changes in the N≡N bond length, N≡N bond stretching frequency, and adsorption energy indicate successful N2 activation. Molecular dynamics simulations demonstrate the efficiency of doped clusters in N2 activation at ambient temperatures. This research underscores the potential of doped aluminum clusters as catalysts for N2 activation and suggests new avenues for designing cost-effective and efficient catalysts.

The third aspect focuses on the oxidative addition of the C-I bond on aluminum nanoclusters. The study explores the reaction mechanism and energetics of this process using DFT calculations. The results reveal that while bulk aluminum is inert towards carbon-halogen bond dissociation, selected aluminum nanoclusters exhibit significantly lower activation barriers. The calculated energy barriers are comparable to those of conventional Pd catalysts, suggesting the potential of aluminum nanoclusters as alternative catalysts. Molecular dynamics simulations provide further insights into the reaction pathway, emphasizing the importance of studying aluminum clusters in the context of nanoscience and nanotechnology.

These studies collectively contribute to our understanding of the reactivity and catalytic potential of aluminum clusters. The investigations of site selectivity, nitrogen activation, and carbon-halogen bond dissociation shed light on the diverse applications and potential advancements in the field of aluminum cluster-based catalysis.

Computational Enzymology: Investigating Proton Transfer, QM Region Size, and Binding Modes in Enzymatic Reactions

This research summary encompasses three different aspects investigated in the field of computational enzymology. Each aspect focuses on distinct areas of enzymatic reactions, utilizing first principles modeling and computational methods.

One aspect of the research explores the biosynthetic pathway for the formation of selinadiene, a terpene compound. By employing DFT calculations, the researchers investigate the mechanistic details of the pathway. The results suggest that the substrate likely adopts a conformation conducive to sequential cyclizations during the formation of selina-4(15),7(11)-diene. However, a required proton transfer step is found to be energetically unfavorable in the gas phase, emphasizing the essential role of enzyme assistance. Docking studies propose potential enzyme interventions through pyrophosphate-assisted acid-base catalysis or electrostatic guidance.

Another aspect of the research explores the dependence of reaction energetics on the size of the quantum mechanical (QM) region in quantum mechanical-molecular mechanical (QM/MM) simulations of a proton transfer in a DNA base pair. Rigorous QM/MM potential and extensive sampling reveals that the free energy reaction profiles rapidly converge with respect to the QM region size, within approximately ±1 kcal/mol. This finding suggests that employing reasonably sized and selected QM regions is a valid approach for modeling complex biomolecular systems, highlighting the significance of appropriate modeling protocols over QM region size.

A further aspect addresses the challenge of predicting the binding modes of multiple ligand states along a reaction coordinate in enzyme active sites. We develop a new docking methodology named EnzyDock, which incorporates simulated annealing molecular dynamics and Monte Carlo sampling to predict chemically relevant binding modes. EnzyDock employs flexible docking, user-defined constraints, and restraints, classical force field potentials, and hybrid quantum mechanics-molecular mechanics potentials. Successful applications of EnzyDock to various enzyme systems, including terpene synthases, Diels-Alder reactions, racemases, and covalent docking, yield binding modes consistent with experimental observations and theoretical studies.

Collectively, these aspects contribute to the field of computational enzymology, providing insights into biosynthetic pathways, the impact of QM region size in simulations, and the development of methodologies for predicting binding modes in enzyme reactions. The research expands our understanding of enzymatic processes and has implications for enzyme design and inhibitor development.

Computational Approaches for Metabolite Characterization: NMR Structure Assignment, Collision Cross Section (CCS) Prediction, and Conformational Clustering

Metabolite characterization is a complex task in metabolomics research, requiring accurate identification and characterization of metabolites. This research focuses on various aspects of metabolite characterization, including NMR structure assignment, CCS  prediction, and conformational clustering, and presents computational approaches to address these aspects.

One aspect addressed is NMR structure assignment, which is crucial for determining metabolite structures. The proposed protocol combines machine learning (ML) models and DFT methods to accurately predict NMR chemical shifts. By employing conformation generation, filtering, clustering, and DFT calculations, the protocol enables the assignment of metabolite structures based on NMR data, enhancing metabolomic analysis.

Another aspect tackled is the prediction of CCS for small molecules. A computational workflow is developed using QM-ML models and DFT methods to accurately estimate CCS values. By considering molecular states, employing unsupervised clustering, and utilizing ion mobility coupled with mass spectrometry (IM-MS) experiments, the workflow provides valuable insights into metabolite conformational preferences and aids in metabolite annotation. To enhance the usability and accessibility of the CCS workflow, it is further integrated with the Snakemake workflow manager, enabling automation and streamlined data processing. Additionally, a user-friendly web server called pomics.org is created, offering the computational capabilities to compute CCS values for unknown metabolites and predict their structures. This freely available web server serves as a valuable resource for the scientific community, empowering researchers to explore and analyze metabolites with ease. The integration with Snakemake and the development of the pomics.org web server further enhance the workflow's accessibility and usability, making it an invaluable resource for metabolite characterization and structure prediction.

The research also introduces an autonomous graph-based clustering algorithm called AutoGraph, which automates the conformational clustering step. The algorithm utilizes the Louvain algorithm and does not require predefined criteria or hyperparameters. AutoGraph accurately clusters conformations of metabolites and small molecules, preserving energetic correlations on the potential energy surface. This approach offers flexibility and automation in conformational clustering within computational workflows.

Additionally, the study compares different conformational search engines in terms of their effectiveness in predicting global minima of molecular structures. By utilizing CCS analysis, the research provides insights into the strengths and limitations of these engines. This knowledge aids in decision-making regarding their utilization in metabolomics, drug discovery, and related fields.

These computational approaches address key aspects of metabolite characterization, providing valuable tools for NMR structure assignment, CCS prediction, and conformational clustering. By facilitating accurate metabolite identification and characterization, these approaches contribute to advancing metabolomics research and its applications in various scientific domains.