Protein Structure Determination Using Chemical Shifts

In this PhD thesis, a novel method to determine protein structures using chemical shifts is presented.Comment: Univ Copenhagen PhD thesis (2014) in Biochemistr

Elucidation of the disulfide folding pathway of hirudin by a topology-based approach

A theoretical model for the folding of proteins containing disulfide bonds is introduced. The model exploits the knowledge of the native state to favour the progressive establishment of native interactions. At variance with traditional approaches based on native topology, not all native bonds are treated in the same way; in particular, a suitable energy term is introduced to account for the special strength of disulfide bonds (irrespective of whether they are native or not) as well as their ability to undergo intra-molecular reshuffling. The model thus possesses the minimal ingredients necessary to investigated the much debated issue of whether the re-folding process occurs through partially structured intermediates with native or non-native disulfide bonds. This strategy is applied to a context of particular interest, the re-folding process of Hirudin, a thrombin-specific protease inhibitor, for which conflicting folding pathways have been proposed. We show that the only two parameters in the model (temperature and disulfide strength) can be tuned to reproduce well a set of experimental transitions between species with different number of formed disulfide. This model is then used to provide a characterisation of the folding process and a detailed description of the species involved in the rate-limiting step of Hirudin refolding.Comment: 14 pages, 9 figure

A Correlation between Protein Function and Ligand Binding Profiles

We report that proteins with the same function bind the same set of small molecules from a standardized chemical library. This observation led to a quantifiable and rapidly adaptable method for protein functional analysis using experimentally-derived ligand binding profiles. Ligand binding is measured using a high-throughput NMR ligand affinity screen with a structurally diverse chemical library. The method was demonstrated using a set of 19 proteins with a range of functions. A statistically significant similarity in ligand binding profiles was only observed between the two functionally identical albumins and between the five functionally similar amylases. This new approach is independent of sequence, structure or evolutionary information, and therefore, extends our ability to analyze and functionally annotate novel genes

Assessing Optic Neuritis in a Mouse Model of Multiple Sclerosis with Diffusion MR Imaging

Optic neuritis (ON) is an early manifestation in patients of multiple sclerosis (MS), typically resulting in visual dysfunction. The inflammatory demyelination of the optic nerve in ON closely resembles pathologies of the rest of central nervous system (CNS) white matter in MS. Since accumulated axonal degeneration in MS was considered as the potential cause leading to permanent disability, correlating optic nerve pathology and visual function in ON could be a model system to investigate the relationship between functional outcome and neuropathology. It may also present a new way to reflect the disease progression in MS. Various MR techniques have been used to assess inflammation (inflammatory cell infiltration and vasogenic edema) of ON, but rarely demonstrated the ability to image cellularity changes non-invasively. Diffusion MRI measures the Brownian motion of water molecules in the microstructure of biological tissues. Diffusion tensor imaging (DTI) holds the promise to provide a specific biomarker of axonal injury and demyelination in CNS white matter by axial diffusivity (the diffusion parallel to white matter fibers) and radial diffusivity (the diffusion perpendicular to white matter fibers), respectively. However, DTI assumes a single diffusion tensor model and thus takes an average of varied diffusion components. In contrast, our recently developed diffusion basis spectrum imaging (DBSI) resolves the complex diffusion components and provides relatively accurate directional diffusivities and diffusion component fractions, relating to the detail and accurate pathological picture of the disease or injury. In the current work, in vivo 25-direction DBSI was applied to the optic nerve of mice with experimental autoimmune encephalomyelitis (EAE), an animal model of MS, with visual impairment at onset of ON. Our results demonstrate that inflammation correlated well with visual impairment in acute ON. DBSI successfully detected inflammatory cell infiltration and optic nerve white matter pathology in EAE that was consistent with histology, supporting the capability of DBSI to quantify increased cellularity, axonal injury and myelin damage in the optic nerve of EAE mice

Methyl group assignment using pseudocontact shifts with PARAssign

Macromolecular Biochemistr

Revisiting Allostery In Lac Repressor

Lac repressor (LacI) is an allosterically regulated transcription factor which controls expression of the lac operon in bacteria. LacI consists of a DNA-binding domain (DBD) and regulatory domain (RD), connected by a linker called the “hinge”. Binding of a small molecule inducer to the RD relieves repression through what is presumed to be a series of conformational changes mediated through the hinge. Despite decades of study, our understanding of this allosteric transition remains incomplete—mostly inferred from partial crystal structures and low-resolution scattering studies. In principle, solution-NMR could provide structural and dynamical information unobtainable by X-ray methods. However, due to LacI’s high molecular weight, low solubility, and transient stability, such studies have been limited to the non-allosteric, isolated DBD. Here, we present a solution-NMR study of the changes in structure and dynamics that underlie the allosteric transition of intact LacI. First, an optimized expression system is presented which enables characterization of LacI using NMR methodologies for high molecular weight proteins. Next, alternative NMR data sampling methods are implemented and further extended to overcome the low-solubility and transient stability limitations. Finally, these developments are combined to characterize LacI in each of its functional states. It is shown that the RD but not the DBD of apo LacI exists in an equilibrium between induced and repressed states with exchange occurring on the �s-ms timescale. Inducer binding in the absence of operator mostly quenches exchange but does not result in structural changes in the hinge or DBD. Conformational dynamics detected in the induced state are shown to be localized to a “network” of RD residues previously characterized to be critical for allostery. These dynamics are shown to be quenched in non-allosteric mutants which suggests functional relevance. Operator binding results in globally quenched dynamics and dramatic changes to the structure of the hinge. Inducer binding in the presence of operator results in only minor structural perturbation in the hinge and DBD. However, dynamics are shown to be activated in the RD. These results suggest that conformational dynamics may be critical to the allosteric transition of LacI

Novel approaches for bond order assignment and NMR shift prediction

Molecular modelling is one of the cornerstones of modern biological and pharmaceutical research. Accurate modelling approaches easily become computationally overwhelming and thus, different levels of approximations are typically employed. In this work, we develop such approximation approaches for problems arising in structural bioinformatics. A fundamental approximation of molecular physics is the classification of chemical bonds, usually in the form of integer bond orders. Many input data sets lack this information, but several problems render an automated bond order assignment highly challenging. For this task, we develop the BOA Constructor method which accounts for the non-uniqueness of solutions and allows simple extensibility. Testing our method on large evaluation sets, we demonstrate how it improves on the state of the art. Besides traditional applications, bond orders yield valuable input for the approximation of molecular quantities by statistical means. One such problem is the prediction of NMR chemical shifts of protein atoms. We present our pipeline NightShift for automated model generation, use it to create a new prediction model called Spinster, and demonstrate that it outperforms established, manually developed approaches. Combining Spinster and BOA Constructor, we create the Liops-model that for the first time allows to efficiently include the influence of non-protein atoms. Finally, we describe our work on manual modelling techniques, including molecular visualization and novel input paradigms.Methoden des molekularen Modellierens gehören zu den Grundpfeilern moderner biologischer und pharmazeutischer Forschung. Akkurate Modelling-Methoden erfordern jedoch enormen Rechenaufwand, weshalb üblicherweise verschiedene Näherungsverfahren eingesetzt werden. Im Promotionsvortrag werden solche im Rahmen der Promotion entwickelten Näherungen für verschiedene Probleme aus der strukturbasierten Bioinformatik vorgestellt. Eine fundamentale Näherung der molekularen Physik ist die Einteilung chemischer Bindungen in wenige Klassen, meist in Form ganzzahliger Bindungsordnungen. In vielen Datensätzen ist diese Information nicht enthalten und eine automatische Zuweisung ist hochgradig schwierig. Für diese Problemstellung wird die BOA Constructor-Methode vorgestellt, die sowohl mit uneindeutigen Lösungen umgehen kann als auch vom Benutzer leicht erweitert werden kann. In umfangreichen Tests zeigen wir, dass unsere Methode dem bisherigen Stand der Forschung überlegen ist. Neben klassischen Anwendungen liefern Bindungsordnungen wertvolle Informationen für die statistische Vorhersage molekularer Eigenschaften wie z.B. der chemischen Verschiebung von Proteinatomen. Mit der von uns entwickelten NightShift-Pipeline wird ein Verfahren zur automatischen Generierung von Vorhersagemodellen präsentiert, wie z.B. dem Spinster-Modell, das den bisherigen manuell entwickelten Verfahren überlegen ist. Die Kombination mit BOA Constructor führt zum sogenannten Liops-Modell, welches als erstes Modell die effiziente Berücksichtigung des Einflusses von nicht-Proteinatomen erlaubt

Structure-based molecular design: identification of modulators of the NCS1:D2 interaction

The interactions between specific proteins (PPIs) is known to be critical for numerous biological processes, implicating them in many pathological conditions, thus modulation of PPIs has substantial therapeutic potential. The complexity, topography and, in some cases, the hydrophobic nature of the PPIs presents a considerable challenge. One important PPI of therapeutic interest, that has been implicated in the treatment of bi-polar and schizophrenia disorders, occurs between neuronal calcium sensor 1 (NCS1) and the dopamine receptor 2 (D2). The research detailed in this thesis describes the application of structure-based drug design (SBDD) to select small molecule compounds for synthesis and biophysical assessment against the NCS1 D2 target. The use of a structure-based drug design method, has been seen in previous PPI studies and uses a combination of techniques, including computational modelling used in conjunction with “hit identification” and ”hit to lead” optimisation processes in a drug discovery pipeline. The biophysical analyses of the first generation synthesised were hampered by problems associated with limited aqueous solubility, restricting the determination of accurate affinity values. Thus a second generation of ligands were developed with addition of solubilising groups to the scaffold, based on that of the compound 1-benzyl-N-((2-methoxy-4,6-dimethylpyridin-3-yl)methyl)-3,5-dimethyl-1H-pyrazole-4-carboxamide (Inhibitor 2). The second generation of compounds displayed improved aqueous solubility, in particular; 1-(4-chlorobenzyl)-3,5-dimethyl-N-((5-(morpholine-4-carbonyl)pyridin-3-yl)methyl)-1H-pyrazole-4-carboxamide (Inhibitor 5), presented the most promising hit. A fragment based approach was also investigated, adapting the SBDD approach by developing a computational pipeline to select 28 compounds from a library of 1137 for biophysical screening. A two-step biophysical screening protocol was developed; employing high throughput NMR techniques, five fragments were identified alongside a hit fragment candidate 5-methyl-3-phenyl-1H-pyrazole (4.21). This research presents two applications of an in silico screening protocol able to identify ligands targeting PPIs. Through verification via biophysical techniques, a number of compounds were determined as hits however, no affinity for the target was determined. This project highlights that despite some successes, many challenges remain in the development of targeting PPIs with small molecules

Combining Computational And Experimental Approaches To Study Disordered And Aggregation Prone Proteins

Over the past two decades disordered proteins have become more widely recognized, challenging the canonical structure-function paradigm associated with proteins. These highly dynamic proteins have been identified across a wide range of species and play a variety of functional roles. Furthermore, the structural plasticity of these proteins gives way to their increased aggregation susceptibility, compared to canonical, well-folded proteins, placing disordered proteins at the center of many neurodegenerative diseases. Despite the increased recognition of the abundance and complexity of disordered proteins, their structural features and the mechanisms by which they transit between functional and pathological roles remains elusive. The efforts described herein focus on leveraging both experimental and computational approaches to study the structure and dynamics of these proteins. Fluorescence-based experiment have proven useful for studying these systems as the intrinsic heterogeneity of this class of proteins, which precludes the use of many traditional structural biochemistry techniques, can be accommodated. Therefore, initial efforts focused on developing new minimally perturbing fluorescence probes and coupling these probes with site-selective labeling strategies. Subsequent efforts focused on identifying methods which could predict where these probes would be tolerated to boost protein yield and avoid structural perturbation. These and other fluorescence probes were employed in Förster Resonance Energy Transfer (FRET) experiments, to study the conformational ensemble of α-synuclein, a disordered protein whose aggregation is implicated in Parkinson’s Disease pathogenesis. Experimental FRET data was paired with molecular modeling in PyRosetta to simulate the conformational ensembles of α-synuclein in the presence and absence of 2 M TMAO. The accuracy of the resultant ensembles was corroborated by comparison to other experimental data. Following this initial success using experimentally constrained simulations, attention was directed towards the development of algorithms capable of generating accurate structural representations of both disordered and ordered proteins de novo. Lastly, this work showcases the utility of a high-throughput in-silico screening approach in identifying a compound that binds selectively to α-synuclein fibrils with nanomolar affinity. Overall this work highlights several computational and experimental approaches which are broadly applicable to the study of disordered and aggregation prone protein

Optimisation and applications of chemical exchange saturation transfer MRI techniques for cancer imaging on clinical scanners

Chemical Exchange Saturation Transfer (CEST) is receiving growing attention in the field of cancer imaging due to its ability to provide molecular information with good spatial resolution within clinically acceptable scan-times. Translation to the clinic requires a solid evidence-base demonstrating the clinical utility and a range of anatomical regions and pathologies have already been studied. These have traditionally been evaluated in terms of asymmetry-based metrics, the most common of which is the magnetization transfer ratio. However, alternative and potentially more informative metrics are also possible. Investigation of fitting metrics has not been reported at clinical field strengths and there is currently no standard approach for optimising the acquisition and post-processing protocols. The work described in this thesis focuses on the practical development and implementation of z-spectrum fitting methods in vivo at 3.0T. After the technical and clinical introductory chapters, chapter three describes the evaluation and comparison of the use of two different lineshapes for modelling the water direct saturation effect. Chapter four describes the optimization of an acquisition and post-processing protocol suitable for CEST imaging of the human prostate at 3.0T. The repeatability of the method is evaluated and in chapter five the optimized protocol is applied in two cancer patients. In chapter six a method is proposed for identification of CEST and NOE resonances in z- spectra acquired at low-field strengths. Chapter seven describes a pre-clinical study of healthy rat brains at 9.4T highlighting the need to consider the interplay between CEST and perfusion effects. In chapter eight the effects of gadolinium administration on CEST signal and contrast in glioma patients is investigated. I hope that the work described herein and the contributions stemming from it will be of some practical benefit to scientists and clinicians interested in exploring the future potential of the growing field of CEST imaging