Molecular modelling - Structure and Properties of carbene-based catalyst

Pomocí molekulového modelování je možné předpovídat chování nových látek a napomáhá při jinak obtížné interpretaci experimentálních dat. Cílem práce byla predikce vybraných vlastností polymeračních katalyzátorů na bázi karbenů, predikce jejich struktur a spektrálních charakteristik a studie mechanismu polymerace za otevření kruhu laktidu. K ověření chování karbenů a jejich prekurzorů ve formě chloridů byly studovány vybrané charakteristiky molekuly. Byl proveden výpočet vybraných molekulových orbitalů a elektrostatických map. Následně pomocí počítačových programů byly získány teoretické vazebné délky a úhly vybraných imidazolových a imidazolinových sloučenin, karbenů a jejich možných produktů hydrolýzy. Data strukturně podobných, již charakterizovaných sloučenin, byla získána z CCDC (Cambridge Crystallographic Data Centre) a následně byla konfrontována s vypočítanými daty. Byla změřena infračervená a Ramanova spektra imidazolové soli a infračervené spektrum příslušného karbenu. Tato spektra byla konfrontována s napredikovanými. Pro lepší interpretaci spekter byla spočítána spektra možných produktů hydrolýzy. Následně byl studován mechanismus polymerace za otevření kruhu laktidu. Na základě spočítaných energií stacionárních bodů byl navržen nový mechanismus polymerace.By using molecular modelling it is possible to predict the behaviour of new compounds and to help interpreting of the experimental data. The objective of the thesis was the prediction of selected properties of polymerization catalysts based on carbenes, the prediction of their structures and spectral characteristics and the study of the mechanism of the ring-opening polymerization of lactide. To confirm the behaviour of carbenes and their precursors based on chlorides selected characteristics of a molecule were studied. The calculation of selected molecular orbitals and electrostatic potential maps was made. Subsequently, bond distances and bond angles of selected imidazole and imidazoline compounds, “free” carbenes and their possible hydrolysis products were obtained by using computer programs. Data of structural similar compounds, which have already been characterized, were obtained from CCDC (Cambridge Crystallographic Data Centre) and were compared with the calculated data. Infrared and Raman spectra of the imidazole salt and the infrared spectrum of the appropriate carbene were measured. The measured spectra were compared with the predicted ones. For the better spectra interpretation the spectra of possible hydrolysis products were calculated. Subsequently, the mechanism of the ring-opening polymerization of lactide was investigated. Based on calculated energies of stationary points the novel mechanism of polymerization was suggested.

Relative energetics of acetyl-histidine protomers with and without Zn²⁺ and a benchmark of energy methods

We studied acetylhistidine (AcH), bare or microsolvated with a zinc cation by simulations in isolation. First, a global search for minima of the potential energy surface combining both, empirical and first-principles methods, is performed individually for either one of five possible protonation states. Comparing the most stable structures between tautomeric forms of negatively charged AcH shows a clear preference for conformers with the neutral imidazole ring protonated at the N-epsilon-2 atom. When adding a zinc cation to the system, the situation is reversed and N-delta-1-protonated structures are energetically more favorable. Obtained minima structures then served as basis for a benchmark study to examine the goodness of commonly applied levels of theory, i.e. force fields, semi-empirical methods, density-functional approximations (DFA), and wavefunction-based methods with respect to high-level coupled-cluster calculations, i.e. the DLPNO-CCSD(T) method. All tested force fields and semi-empirical methods show a poor performance in reproducing the energy hierarchies of conformers, in particular of systems involving the zinc cation. Meta-GGA, hybrid, double hybrid DFAs, and the MP2 method are able to describe the energetics of the reference method within chemical accuracy, i.e. with a mean absolute error of less than 1kcal/mol. Best performance is found for the double hybrid DFA B3LYP+XYG3 with a mean absolute error of 0.7 kcal/mol and a maximum error of 1.8 kcal/mol. While MP2 performs similarly as B3LYP+XYG3, computational costs, i.e. timings, are increased by a factor of 4 in comparison due to the large basis sets required for accurate results

First-principles study of radiation-induced radicals in solid-state amino acids and sugars: confrontation of density-functional calculations with experimental results

In this work, we present an extensive computational study of several radiationinduced radicals of biomolecules. In particular, two specific types of molecular systems will be highlighted: amino acids and sugars. Both systems are abundantly present in the natural world and are vital to the existence of life in all its forms. Amino acids are the building blocks of polypeptides and proteins, which are involved in nearly all biochemical processes. Sugars (or carbohydrates) also play a key role, not merely as sweeteners but rather as essential components in the biological energy storage and transport systems of animals and as chief structural material in plants. As can be expected, the radical adducts of these compounds have an equal importance in biochemistry. These radicals can arise in chemical reactions or can be induced as the result of radiation damage. Such species are normally very shortliving in gas phase or solution. In crystals on the other hand, the radicals become “trapped” inside the amino-acid or carbohydrate lattice and their reactivity will be sharply reduced. The solid state therefore offers the opportunity to extensively study the nature and structure of the (radiation-)induced organic radicals using various experimental techniques, of which Electron Paramagnetic Resonance spectroscopy (EPR) can be favoured as it can access an abundance of structural information about the radical. However, this technique does not provide the information as such, instead it has to be deduced from the EPR spectroscopic parameters, an analysis that is often complex and open to ambiguity. In addition, the radiation chemistry of sugars and especially amino acids in the solid state is an elaborate field of study and requires a profound understanding of the different physical and chemical processes taking place inside the crystal. This area of interest has received considerable attention in view of interesting applications in EPR dosimetry. Within this respect, we refer to the success of the alanine dosimeter for reference- and routine dosimetry in radiation therapy, biological research and industrial high-dose irradiation facilities. However, it was only after the publication of a detailed EPR study on this amino acid that an enhanced understanding of its radiation chemistry was established. Three radical species were in this way identified as contributing significantly to the observed composite spectrum and hence also to the overall dosimetric characteristics of the alanine system. As a result of the often-cumbersome analysis of the EPR parameters and the complexity of the associated radiation chemistry, the experimentalist is faced with a delicate task to propose appropriate models for the paramagnetic species present in the crystals. Over the last few years, it has become increasingly popular to rely on ab-initio molecular modeling techniques for this purpose. This success is in part due to the spectacular expansion of recent computer capabilities but is not in the least a result of the ongoing development of theoretical models and numerical algorithms in the field of quantum chemistry. Especially since the introduction of Density Functional Theory (DFT), a sharp quantitative as well as qualitative increase of theoretical calculations has been witnessed. The effectiveness of DFT can be largely attributed to a better incorporation of electron correlation as compared to more conventional ab-initio methods (such as e.g. Hartree Fock). Furthermore, this DFT algorithm does not require considerably more computer time as compared to conventional HF calculations but, in contrast, is significantly faster in comparison with other high-level correlation calculations (e.g. post HF), which renders it a very cost-effective method. Not only can these types of ab-initio calculations identify and verify proposed radical structures with the aid of optimization routines, predictions can also be made founded on entirely theoretical grounds. In addition, these methods offer the possibility to reproduce EPR quantities based on first principles. Evidently this presents a powerful tool to the experimentalist for the interpretation and analysis of EPR spectra. By now comparing measured and predicted spectroscopic parameters with each other, the true identity of an experimentally observed paramagnetic species can be linked directly to the structural characteristics of a theoretical model proposed for the specified radical. In this work, we will specifically make use of the link with experiment to characterize the radiation-induced radicals of amino acids and sugars from a theoretical point of view. A general computational strategy is reported, which outlines a basic procedure for the theoretical treatment and simulation of radicals in a solid state. This strategy is composed of four main steps. In an initial step, one or more radical models are proposed that might be consistent with the experimental EPR data of an observed paramagnetic species. The structures of these radical models are subsequently optimized within a well-defined model space, in either a DFT or semi-empirical framework. A third step concerns the determination of EPR parameters for the optimized structures, adopting an ab-initio level of theory. The results of these EPR calculations can also be sensitive to the used model space. In the final step, a conclusive analysis between calculated and measured EPR parameters is then possible. Applied on amino-acid and sugar systems, the drafted procedure will enable us to formulate specific conclusions with regard to the nature and identity of the radiation induced radicals, on the condition that an appropriate approximation is made for the solid-state environment of the radical. The extent of the model space during the optimization and EPR calculations is therefore of particular importance. In this work, it is examined what effect the size of the model space and the applied level of theory have on the calculated structural and spectroscopic properties of a simulated radical. This is achieved by introducing several model space approaches – classified from “single molecule”, over “cluster” to “periodic” – which incorporate an increasing amount of intermolecular interactions between the radical and its crystalline environment. Eventually, it is argued that the model space indeed plays a considerable role for the determination of a radical geometry and its associated EPR parameters. This aspect must therefore be carefully considered when initiating a computational study of radicals in the solid state. This work is organized in two main sections. The first section contains chapters 2 to 4 and outlines the conceptual framework of this thesis. In chapter 2, a concise overview is presented of some general principles in molecular modeling that are relevant to this work. The subsequent chapter deals with the basic concepts and theory of EPR spectroscopy. In the fourth chapter, we will introduce a general computational strategy that will be followed in the applications-section to determine EPR parameters on theoretical grounds. In the second, applied section (chapters 5 to 10), several investigations are made of radiation-induced radicals in solid-state systems. Chapters 5 and 6 deal with the amino-acid systems, alanine and glycine, respectively. After a general introduction into the applications and occurrence of radicals in sugar crystals (chapter 7), a report is given on the radicals in β-D-fructose (chapter 8), α-D-glucose (chapter 9) and α-L-sorbose (chapter 10). In the final chapter, some general conclusions are formulated

The development of an empirically corrected semi-empirical method and its application to macromolecular complexes

Computational chemistry is a growing field crossing interdisciplinary scientific fields because of the ability to predict physical properties while reducing costs and waste materials; however, there are limiting factors. Computationally modeling systems governed by non-bonded interactions, especially van der Waals (dispersion) interactions, is currently a difficult task, since many conventional quantum mechanical techniques neglect such interactions. Methods that are capable of modeling such interactions are computationally extremely expensive, limiting system size to only a few dozen atoms. Therefore, such computations are intractable for exploring in the upper limits of the nanoscopic world. One avenue of nanotechnology involves engineering machines at the molecular level that are capable of producing useful work. Such devices promise to be applicable in a wide range of areas, such as molecular-scale electronics, nanometer-scale engineering, medicine, and space science to name a few. In order to model such large systems, semi-empirical methods appear to be an attractive option; however, the popular semi-empirical methods (e.g. AM1) do not model long-range dispersion but this is not their only shortcoming. For weakly interacting systems, hydrogen bonding also poses a concern. Therefore, an empirically-corrected AM1 method that uses two empirical correction terms, one for dispersion and one for hydrogen-bonding interactions, has been developed and termed AM1-FS1. The AM1-FS1 method has been tested and used to study several carbon nanostructure complexes and rotaxane systems and is found to produce results in good agreement with experimental and other first-principles calculations.Ph.D., Theoretical Chemistry -- Drexel University, 201

QM/MM benchmarking of cyanobacteriochrome Slr1393g3 absorption spectra

Cyanobacteriochromes are compact and spectrally diverse photoreceptor proteins that are promising candidates for biotechnological applications. Computational studies can contribute to an understanding at a molecular level of their wide spectral tuning and diversity. In this contribution, we benchmark methods to model a 110 nm shift in the UV/Vis absorption spectrum from a red- to a green-absorbing form of the cyanobacteriochrome Slr1393g3. Based on an assessment of semiempirical methods to describe the chromophore geometries of both forms in vacuo, we find that DFTB2+D leads to structures that are the closest to the reference method. The benchmark of the excited state calculations is based on snapshots from quantum mechanics/molecular mechanics molecular dynamics simulations. In our case, the methods RI-ADC(2) and sTD-DFT based on CAM-B3LYP ground state calculations perform the best, whereas no functional can be recommended to simulate the absorption spectra of both forms with time-dependent density functional theory. Furthermore, the difference in absorption for the lowest energy absorption maxima of both forms can already be modelled with optimized structures, but sampling is required to improve the shape of the absorption bands of both forms, in particular for the second band. This benchmark study can guide further computational studies, as it assesses essential components of a protocol to model the spectral tuning of both cyanobacteriochromes and the related phytochromes

Development of Semiempirical Models for Metalloproteins

Theoretical models and computational techniques are useful for gaining insight into the interactions, movements, and functions of atoms and molecules, ranging from small chemical systems with few atoms to large biological molecules with many atoms. Due to the inability of force field methods to accurately describe different properties of metalloenzymes and the prohibitive computing cost of high-level quantum methods, computationally efficient models are needed. This dissertation describes the development of new quantum semiempirical models for metalloproteins. The original AM1 (Austin Model 1) based on the neglect of diatomic differential overlap approximations was re-parameterized to describe the structural and energetic properties of biomolecules that mimic the active sites of metalloproteins. The biologically inspired genetic algorithm PIKAIA was used to optimize the parameters for each chemical element. Structures and energies of various clusters analogous to complexes found in metalloproteins were prepared as a training set using hybrid density functional theory. Models were trained to reproduce all of the properties included in the small training set. The optimized models were validated for large testing sets that incorporate bigger complexes and related reactions. Finally, the optimized models were used to study biologically-relevant processes in condensed phase using molecular dynamics simulations. All the gas- and liquid-phase results from the optimized models were compared with original semiempirical models as well as available high-level theoretical and experimental results. Metal ions play crucial roles in biological systems. They actively participate in structural, catalytic, and co-catalytic activities of a large number of enzymes. The development of semiempirical models is divided into three parts. First, new AM1 parameters for hydrogen and oxygen were developed to describe gas-phase proton transfer reactions in water and static and dynamic properties of liquid water. Gas-phase results were compared with original AM1, RM1, and PM3 models, whereas liquid results were compared with original AM1, AM1-W, and AM1PG-W models, and with available experimental results. It is found that the optimized model reproduces experimental data better than other available semiempirical models. Second, using the previously optimized model for hydrogen and oxygen, the AM1 model is re-parameterized for zinc and sulfur to describe important physical and chemical properties of zinc, water, hydrogen sulfide complexes mimicking structural motifs found in zinc enzymes. Metal-induced pKa shifts are computed for water and hydrogen sulfide, and compared with available theoretical and experimental results. Third, using previously optimized parameters for hydrogen, oxygen, and zinc, AM1 parameters for carbon and nitrogen are optimized to study proton transfer, nucleophilic attacks, and peptide hydrolysis mechanisms in zinc metalloproteases. Overall, the optimized models give promising results for the various properties of biomolecules in gas-phase clusters and in condensed phase. Particularly, the water model reproduces the proton transfer related properties in gas-phase and the structure, dielectric properties, and infrared spectra of liquid water. The zinc/sulfur model reproduces the hydration structure of zinc cation and zinc-bound hydrogen sulfide. Results for the coordination configurations of zinc solvated in water and in hydrogen sulfide confirm the versatility of the model. The optimized model for carbon and nitrogen improves the overall performance compared to AM1 and PM3. The optimized model for carbon and nitrogen reproduces structures and various energetic terms for zinc-ligands systems (representing the active sites of zinc enzymes) when compared to density functional theory results. The optimized model can be used to study metal-ligand reactivity in zinc enzymes

Peptide-Cation Systems: Conformational Search, Benchmark Evaluation, and Force Field Parameter Adjustment Using Regularized Linear Regression

Metal cations often play an important role in shaping the three-dimensional structure of peptides. As an example, the model system AcPheAla5LysH+ is investigated in order to fully understand the forces that stabilize its helical structure. In particular, the question of whether the local fixation of the positive charge at the peptide's C-terminus is a prerequisite for forming helices is addressed by replacing the protonated lysine residue by alanine and a sodium cation. The combination of gas-phase cold-ion vibrational spectroscopy with molecular simulations based on density-functional theory (DFT) revealed that the charge localization at the C-terminus is imperative for helix formation in the gas phase as this stabilizes the structure through a cation-helix dipole interaction. For sodiated AcPheAla6, globular rather than helical structures were found caused by the strong cation-backbone and cation-pi interactions. Interestingly, the global minimum-energy structure from simulation is not present in the experiment where the system remains kinetically trapped in a solution-state structure. Thereby calculated energies and IR spectra that are sufficiently accurate relied on DFT with computationally costly hybrid functionals, while for the structure search low-computational-cost force field (FF) models are crucial. This inspired a study where the goodness of commonly applied levels of theory, i.e. FFs, semi-empirical methods, density-functional approximations, composite methods, and wavefunction-based methods are being evaluated with respect to benchmark-grade coupled-cluster calculations. Acetylhistidine - either bare or in presence of a zinc cation - thereby serves as a molecular benchmark system. Neither FFs nor semi-empirical methods are reliable enough for a description of these systems within "chemical accuracy" of 1 kcal/mol. Accurate energetic description within chemical accuracy is achieved for all systems using the meta-GGA SCAN or computationally more demanding hybrid functionals. The double-hybrid functional B3LYP+XYG3 is best resembling the benchmark method DLPNO-CCSD(T). Despite poor energetic performances of conventional FFs for peptides in the gas phase, their low computational costs still render them appealing tools for large-scale structure searches. Consequently, a machine learning approach is presented where the torsional parameters and (if desired) van der Waals parameters in the potential-energy function of a particular FF are adjusted by fitting it against DFT energies using regularized regression models like LASSO or Ridge regression. For the peptide AcAla2NMe, this resulted in a significant improvement when comparing to standard OPLS-AA parameters. For more challenging peptide-cation systems, e.g. AcAla2NMe + Na+, this approach does not give satisfying results, which is caused by the formulation of the potential energy of the FF itself: While derived empirical partial charges using Hirshfeld partitioning or the electrostatic potential (ESP) decrease the accuracy, part of the energetic discrepancy can be "compensated" due to the flexibility of the torsional contributions in terms of the energetic description