Nonspherical large cages in structure I (sI) clathrates impose non-uniform motion of nonspherical guest molecules and anisotropic lineshapes in NMR spectra of the guest. In this work, we calculate the lineshape anisotropy of the linear CO2 molecule in large sI clathrate cages based on molecular dynamics simulations of this inclusion compound. The methodology is general and does not depend on the temperature and type of inclusion compound or guest species studied. The nonspherical shape of the sI clathrate hydrate large cages leads to preferential alignment of linear CO2 molecules in directions parallel to the two hexagonal faces of the cages. The angular distribution of the CO2 guests in terms of a polar angle θ and azimuth angle  and small amplitude vibrational motions in the large cage are characterized by molecular dynamics simulations at different temperatures in the stability range of the CO2 sI clathrate. These distributions are used to calculate the NMR powder spectrum of CO2 at different temperatures. The experimental 13C NMR lineshapes of CO2 guests in the large cages show a reversal of the skew between the low temperature (77 K) and the high temperature (238 K) limits of the stability of the clathrate. Good agreement between experimental lineshapes and calculated lineshapes is obtained. No assumptions regarding the nature of the guest motions in the cages are required.

No description available.

The results of a solid-state 11B NMR study of a series of boronic acids, boronic esters, and boronic acid catechol cyclic esters with aromatic substituents are reported in this thesis. Boron-11 electric field gradient (EFG) and chemical shift (CS) tensors obtained from analyses of spectra acquired in magnetic fields of 9.4 T and 21.1 T are demonstrated to be useful for gaining insight into the molecular and electronic structure about the boron nucleus. It can be concluded that when adequate electronic variation is present in the compounds being studied, Ω is generally the most characteristic boron NMR parameter of the molecular and electronic environment for boronic acids and esters. Importantly, these data are only reliably accessible in ultrahigh magnetic fields. The experimental span values result from a delicate interplay of several competing factors, including hydrogen bonding, the value of the dihedral angle, and the type of aromatic ring system present.

The results of a solid-state 11B NMR study of a series of boronic acids, boronic esters, and boronic acid catechol cyclic esters with aromatic substituents are reported in this thesis. Boron-11 electric field gradient (EFG) and chemical shift (CS) tensors obtained from analyses of spectra acquired in magnetic fields of 9.4 T and 21.1 T are demonstrated to be useful for gaining insight into the molecular and electronic structure about the boron nucleus. It can be concluded that when adequate electronic variation is present in the compounds being studied, Ω is generally the most characteristic boron NMR parameter of the molecular and electronic environment for boronic acids and esters. Importantly, these data are only reliably accessible in ultrahigh magnetic fields. The experimental span values result from a delicate interplay of several competing factors, including hydrogen bonding, the value of the dihedral angle, and the type of aromatic ring system present.

Nuclear magnetic resonance (NMR) is one of the main approaches for protein struc- ture determination. The biggest advantage of this approach is that it can determine the three-dimensional structure of the protein in the solution phase. Thus, the natural dynamics of the protein can be studied. However, NMR protein structure determina- tion is an expertise intensive and time-consuming process. If the structure determi- nation process can be accelerated or even automated by computational methods, that will significantly advance the structural biology field. Our goal in this dissertation is to propose highly efficient and error tolerant methods that can work well on real and noisy data sets of NMR. Our first contribution in this dissertation is the development of a novel peak pick- ing method (WaVPeak). First, WaVPeak denoises the NMR spectra using wavelet smoothing. A brute force method is then used to identify all the candidate peaks. Af- ter that, the volume of each candidate peak is estimated. Finally, the peaks are sorted according to their volumes. WaVPeak is tested on the same benchmark data set that was used to test the state-of-the-art method, PICKY. WaVPeak shows significantly better performance than PICKY in terms of recall and precision. Our second contribution is to propose an automatic method to select peaks pro- duced by peak picking methods. This automatic method is used to overcome the limitations of fixed number-based methods. Our method is based on the Benjamini- Hochberg (B-H) algorithm. The method is used with both WaVPeak and PICKY to automatically select the number of peaks to return from out of hundreds of candidate peaks. The volume (in WaVPeak) and the intensity (in PICKY) are converted into p-values. Peaks that have p-values below some certain threshold are selected. Ex- perimental results show that the new method is better than the fixed number-based method in terms of recall. To improve precision, we tried to eliminate false peaks using consensus of the B-H selected peaks from both PICKY and WaVPeak. On average, the consensus method is able to identify more than 88% of the expected true peaks, whereas less than 17% of the selected peaks are false ones. Our third contribution is to propose for the first time, the 3D extension of the Median-Modified-Wiener-Filter (MMWF), and its novel variation named MMWF*. These spatial filters have only one parameter to tune: the window-size. Unlike wavelet denoising, the higher dimensional extension of the newly proposed filters is relatively easy. Thus, they can be applied to denoise multi-dimensional NMR-spectra. We tested the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR- spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Finally, we propose a novel framework that simultaneously conducts slice picking and spin system forming, an essential step in resonance assignment. Our framework then employs a genetic algorithm, directed by both connectivity information and amino acid typing information from the spin systems to assign the spin systems to residues. The inputs to our framework can be as few as two commonly used spectra, i.e., CBCA(CO)NH and HNCACB. Different from existing peak picking and resonance assignment methods that treat peaks as the units, our method is based on slices, which are one-dimensional vectors in three-dimensional spectra that correspond to certain (N, H) values. Experimental results on both benchmark simulated data sets and four real protein data sets demonstrate that our method significantly outperforms the state-of-the-art methods especially on the more challenging real protein data sets, while using a less number of spectra than those methods. Furthermore, we show that using the chemical shift assignments predicted by our method for the four real proteins can lead to accurate calculation of their final three-dimensional structures by using CS-ROSETTA server.

<p>Nuclear magnetic resonance (NMR) spectroscopy is an established technique for macromolecular structure determination at atomic resolution. However, the majority of the current structure determination approaches require a large set of experiments and use large amount of data to elucidate the three dimensional protein structure. While current structure determination protocols may perform well in data-rich settings, protein structure determination still remains to be a difficult task in a sparse-data setting. Sparse data arises in high-throughput settings, for larger proteins, membrane proteins, and symmetric protein complexes; thereby requiring novel algorithms that can compute structures with provable guarantees on solution quality and running time.</p><p>In this dissertation project we made an effort to address the key computational bottlenecks in NMR structural biology. Specifically, we improved and extended the recently-developed techniques by our laboratory, and developed novel algorithms and computational tools that will enable protein structure determination from sparse NMR data. An underlying goal of our project was to minimize the number of NMR experiments, hence the amount of time and cost to perform them, and still be able to determine protein structures accurately from a limited set of experimental data. The algorithms developed in this dissertation use the global orientational restraints from residual dipolar coupling (RDC) and residual chemical shift anisotropy (RCSA) data from solution NMR, in addition to a sparse set of distance restraints from nuclear Overhauser effect (NOE) and paramagnetic relaxation enhancement (PRE) measurements. We have used tools from algebraic geometry to derive analytic expressions for the bond vector and peptide plane orientations, by exploiting the mathematical interplay between RDC- or RCSA-derived sphero-conics and protein kinematics, which in addition to improving our understanding of the geometry of the restraints from these experimental data, have been used by our algorithms to compute the protein structures provably accurately. Our algorithms, which determine protein backbone global fold from sparse NMR data, were used in the high-resolution structure determination protocol developed in our laboratory to solve the solution NMR structures of the FF Domain 2 of human transcription elongation factor CA150 (RNA polymerase II C-terminal domain interacting protein), which have been deposited into the Protein Data Bank. We have developed a novel, sparse data, RDC-based algorithm to compute ensembles of protein loop conformations in the presence of a moderate level of dynamics in the loop regions. All the algorithms developed in this dissertation have been tested on experimental NMR data. The promising results obtained by our algorithms suggest that our algorithms can be successfully applied to determine high-quality protein backbone structures from a limited amount of experimental NMR data, and hence will be useful in automated NOE assignments and high-resolution protein backbone structure determination from sparse NMR data. The algorithms and the software tools developed during this project are made available as free open-source to the scientific community.</p> / Dissertation

Advances in the elaboration of vaccines and enzyme inhibitors rely on acquiring more knowledge about protein-carbohydrate binding events. Furthermore, the relationships between biological function and the three-dimensional properties of large glycans can be studied by focusing on the structural components they contained, namely, by scaling down the system under analysis. Chemical methods are useful assets as they allow the isolation and determination of epitopes; these small and recognizable fragments that lead to very specific interactions. In this thesis, biologically relevant saccharides were obtained using recently developed concepts in carbohydrate synthesis and NMR spectroscopy was used to unravel their conformational preferences. In paper I, the convergent synthesis of the tetrasaccharide found in the natural product solaradixine is described. Reactivity enhanced disaccharide glycosyl donors were coupled to a disaccharide acceptor in a 2 + 2 fashion. The computer program CASPER was subsequently used to verify the synthesized structure. The conformation arming concept employed in paper I was further investigated in paper II. An NMR-based methodology enabled the determination of the ring conformations of a set of donors. Subsequently, glycosylation reactions were performed and yields were correlated to donors ring shapes. Perturbations in the rings shape caused by bulky silyl ether protective groups were sufficient to boost the potency of several donors. As a matter of fact, complex branched oligosaccharides could be obtained in good to excellent yields. In paper III, NMR spectroscopy observables were measured to elucidate the ring shape, the mutual orientation of the rings across the glycosidic bond and the positions of the side chains of 5 trisaccharides found in larger structures. With the aid of molecular dynamics simulations, their overall conformational propensities were revealed. Finally, the software CASPER prediction skills were improved by adding, inter alia, NMR information of synthesized mono- and disaccharides to its database. Unassigned chemical shifts from polysaccharides served as input to challenge its ability to solve large carbohydrate structures. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 4: Manuscript.</p>

Abstract This thesis presents computational studies of both the static and dynamic parameters of the nuclear magnetic resonance (NMR) spectroscopy of gaseous xenon. First, state-of-the-art static magnetic resonance parameters are computed in small xenon clusters by using methods of quantum chemistry, and second, time-dependent relaxation phenomena are investigated via molecular dynamics simulations at different experimental conditions. Based on the underlying quantum and classical mechanics concepts, computational methods represent a procedure complementary to experiments for investigating the properties of atoms, molecules, clusters and solids. Static NMR spectral parameters, chemical shift, shielding anisotropy and asymmetry parameter, nuclear quadrupole coupling, and spin-rotation coupling, are calculated using different electronic structure methods ranging from the uncorrelated Hartree-Fock method to correlated second-order Møller-Plesset many-body perturbation, complete/restricted active space multiconfiguration self-consistent field, and to coupled-cluster approaches. The bond length dependence of these properties is investigated in the xenon dimer (Xe2). A well-characterized property in experimental NMR, the second virial coefficient of nuclear shielding, is theoretically calculated by a variety of methods and convincingly verified against experimental findings. Here, it is mandatory to include effects from special relativity as well as electron correlation. As a side result, a purely theoretical potential energy curve for Xe2, comparable to best experimental ones, is calculated. A pairwise additive scheme is established to approximate the NMR properties in differently coordinated sites of xenon clusters Xen (n = 2 - 12). Especially the pairwise additive chemical shift values are found to be in close agreement with quantum-chemical results and only a small scaling factor close to unity is needed for the correct behavior. Finally, a dynamical magnetic resonance property, the experimental nuclear spin-lattice relaxation rate R1 of monoatomic Xe gas due to the chemical shift anisotropy (CSA) mechanism is validated from first principles. This approach is based on molecular dynamics simulations over a large range of temperatures and densities, combined with the pairwise additive approximation for the shielding tensor. Therein, the shielding time correlation function is seen to reflect the characteristic time scales related to both interatomic collisions and cluster formation. For the first time, the physics of gaseous xenon is detailed in full in the context of CSA relaxation.

Abstract In this work, evolution of structural properties of anhydrous and hydrated alkali halide clusters are studied using synchrotron radiation based photoelectron spectroscopy. Alkali metal core level spectra of small anhydrous RbCl, RbBr, CsCl and CsBr clusters indicate a NaCl structure. For larger CsBr clusters a structural phase transition to CsCl structure is likely the case. Alkali halide core level spectra of mixed RbBr-water clusters indicate that at dilute concentration the salt is dissolved by the water cluster but ion pairing increases with concentration. Modeling of gas phase cluster formation and electronic structure calculations of core level chemical shifts are used to interpret the experimental spectra.

Non-covalent bonds are an important class of intermolecular interactions, which result in the ordering of atoms and molecules on the supramolecular scale. One such type of interaction is brought about by the bond formation between a region of positive electrostatic potential (σ-hole) interacts and a Lewis base. Previously, the halogen bond has been extensively studied as an example of a σ-hole interaction, where the halogen atom acts as the bond donor. Similarly, carbon, and the other tetrel elements can participate in σ-hole bonds. This thesis explores the nature of the carbon tetrel bond through the use of computational chemistry and solid state nuclear magnetic resonance (NMR) spectroscopy. The results of calculations of interaction energies and NMR parameters are reported for a series of model compounds exhibiting tetrel bonding from a methyl carbon to the oxygen and nitrogen atoms in a range of functional groups. The ¹³C chemical shift (𝛿iso) and the ¹ᶜ𝐽(¹³C,¹⁷O/¹⁵N) coupling across the tetrel bond are recorded as a function of geometry. The sensitivity of the NMR parameters to the non-covalent interaction is demonstrated via an increase in 𝛿iso and in |¹ᶜ𝐽(¹³C,¹⁷O/¹⁵N)| as the tetrel bond strengthens. There is no direct correlation between the NMR trends and the interaction energy curves; the energy minimum does not appear to correspond to a maximum or minimum chemical shift or J-coupling value. Gauge-including projector-augmented wave density functional theory (DFT) calculations of 𝛿iso are reported for crystals which exhibit tetrel bonding in the solid state. Experimental 𝛿iso values for sarcosine, betaine and caffeine and their tetrel-bonded salts generally corroborate the computational findings. This work offers new insights into tetrel bonding and facilitates the incorporation of tetrel bonds as restraints in NMR crystallographic structure refinement.