Extracting 3D parametric curves from 2D images of Helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Pore forming protein assembly and the use in nanopore sensing: a study on E. coli proteins ClyA and OmpG

Pore forming proteins are typically the proteins that form channels in membranes. They have several roles ranging from molecule transport to triggering the death of a cell. This work focuses on two E. coli pore forming proteins that have vastly differing roles in nature. Outer membrane protein G (OmpG) is an innocuous β-barrel porin while Cytolysin A (ClyA) is an α-helical pore forming toxin. For OmpG we probed its potential to be a nanopore sensor for protein detection and quantification. A small high affinity ligand, biotin, was covalently attached to loop 6 of OmpG and used to capture biotin-binding proteins. OmpG specifically interacted with target proteins and with a high sensitivity. In addition, we found that OmpG could discriminate among eight target proteins, four avidin homologues and four antibody homologues. Our work was the first revelation that a “noisy” nanopore sensor could not only discriminate among homologous proteins but could maintain this sensitivity and specificity in a serum sample. Unlike OmpG which is a monomeric pore, the ClyA pore is an oligomer which allowed us to probe its assembly. We found an alternative mechanism of assembly of the ClyA pore from its soluble monomer. Our results revealed an off-pathway oligomerization of ClyA. These oligomers are stable in solution but are less efficient at converting into transmembrane pores in the membrane than monomeric ClyA

Extracting 3D parametric curves from 2D images of helical objects

Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively

Biochemical analysis of the W28F mutant of human class Pi glutathione S-transferase

A dissertation submitted in fulfilment of the requirements for the degree of Master of Science at the University of the Witwatersrand. Johannesburg, October 1996.Glutathione S-transferase (GST) class Pi has two tryptophan residues which are conserved within domain one. Trp38 plays a functional role in sequestering glutathione at the active site, whereas Trp28 plays a structural role. The effects of the sterically-conservative substitution of Trp28 to Phe were investigated. When the W28F mutant was compared with the wild-type enzyme, mutation of Ttp28 to Phe was not well tolerated and resulted in a dimeric protein with impaired catalytic function and conformational stability. [Abbreviated Abstract. Open document to view full version]AC201

Designing Small Molecule Inhibitors of RNA-Binding Protein Musashi Using New Biochemical and Computational Approaches

ABSTRACT RNA-binding proteins (RBPs) are key regulators of post-transcriptional gene expression, and underlie many disease-relevant processes. However, they have historically been challenging to target with drug-like compounds. Inspired by the “anchor residues” of protein-protein interactions, we developed a computational approach for rationally designing small-molecule inhibitors of RBPs. In this dissertation, we first selected Musashi-1 and Musashi-2 to apply our “RNA mimicry” approach. Both Musashi proteins are well-studied RBPs, known principally as stem‑cell markers that are upregulated in many cancers. In the future, we hope our “RNA mimicry” approach can be generally applied to inhibitor design of diverse target RBPs. To design inhibitors of Musashi proteins, we applied our strategy by mimicking the three-dimensional interactions in the protein-RNA complex. As described in Chapter II, by using pharmacophoric screening, we searched for drug-like compounds that can present the same geometric arrangement of functional groups as the RNA in the complex. We hypothesized that such ligands would engage Musashi in a similar manner as the RNA binds to Musashi. Since the interaction geometries can be quite distinct from one another for different RBPs, we anticipated that this strategy would lead to inhibitors that were selective for Musashi. To facilitate characterization of these candidate Musashi inhibitors, I developed the “isothermal analysis” approach. As described in Chapter III, this method allows us to calculate quantitative binding constants by using differential scanning fluorimetry (DSF) data. The method requires only the protein unfolding information at a given temperature as a function of ligand concentration, and thus no thermodynamic parameters are included in the calculation. Finally, I describe the use of computational docking to better understand the basis for PROTAC-mediated degradation of target proteins. PROteolysis TArgeting Chimeras (PROTACs) are heterobifunctional small molecules which can induce target protein degradation through cell ubiquitination process. Rational design of PROTACs is still challenging, however, because of the limited structural understanding of their mechanism. In Chapter IV, I seek to predict the formation of the ternary structure complex by including both effects of the protein-protein interaction and effects of the chemical linker. Looking ahead, I hope to use these ternary structure models to explain the activity and selectivity of the given PROTAC molecules, and ultimately to use our designed Musashi inhibitors as a starting point for building new PROTACs to degrade Musashi. The text of Chapter II is a manuscript that is in preparation for publication as: Bai N‡, Adeshina Y‡, Lan L, Makhov PB, Xia Y, Gowthaman R, Miller SA, Johnson DK, Boumber Y, Xu L, Karanicolas J. Rationally designing inhibitors of the Musashi protein-RNA interaction by hotspot mimicry. ‡equally contributing co-authors The supporting information for this chapter is included as Appendix A.1. The text of Chapter III is a reprint of the material from: Bai N, Roder H, Dickson A, Karanicolas J. Isothermal analysis of ThermoFluor data can readily provide quantitative binding affinities. Sci. Rep. 9, p. 2650 (2019). Note: the software disseminated with this paper has accumulated 1000 downloads in the 9 months since publication The supporting information for this chapter is included as Appendix A.2. The text of Chapter IV is a manuscript that is in preparation for publication as: Bai N, Karanicolas J. Predicting PROTAC-mediated ternary complex formation using Rosetta. The supporting information for this chapter is included as Appendix A.3

Innate lymphoid cell plasticity and heterogeneity in human tissues

The human immune system is a vital mechanism that protects the host from outside threats. The two main branches of this system, consisting of innate and adaptive immunity, aid the host when dangers of both imminent and protracted nature occur. Innate lymphoid cells (ILC) play a key role in innate immunity and are classified into distinct groups based on their function, transcriptional profile and development. Their discovery is young, with much research needed to understand all aspects of their phenotype, genotype, behavior in homeostasis and disease. This thesis describes the breakthroughs my collaborators and I were able to reach during my five years of doctoral studies, in order to better understand the biology and physiology of ILC. In the first part of this thesis I describe the history and classification of ILC, the efforts being done so far by the field to understand their development, and the possible combinations of changes in ILC status, known as plasticity or trans-differentiation properties of ILC under particular environments or stimuli. As Paper II’s results are based on a cohort of Inflammatory Bowel Disease (IBD) samples, and in Paper I we analyze ILC in intestinal biopsies from IBD patients, I also outline the main characteristics representing this disorder while focusing on the role of ILC in IBD. Next, after defining the aim of my studies, I describe how the data was obtained, as a big part of my work consisted in learning how to handle methods and technologies such as flow cytometry, fluorescence-activated cell sorting (FACS) and single cell RNA sequencing, among others. Finally, a discussion of the results is aimed at highlighting the major breakthroughs achieved. In Paper I, we set out to better understand the function of the Ikaros family of transcription factors in ILC, focusing our efforts on IKZF3 (encoding Aiolos) and its role in ILC trans- differentiation via a drug-induced silencing approach with the immune-modulatory agent lenalidomide. In Paper II, a large cohort of IBD samples was analyzed in order to find disturbances in peripheral blood ILC protein expression. We were able to uncover differences in several activation proteins in the IBD cohort, when compared to a like-sized cohort of samples from healthy controls. In Paper III, a big effort was put into implementing Smart- seq2 RNA sequencing technology to a large number of ILC from a variety of tissues. This allowed us to better understand the heterogeneity of ILC in the circulation, secondary lymphoid and mucosal tissues. We generated a large dataset that will require time to be exploited in full, constituting a roadmap for future studies aimed at understanding human ILC biology and function. In summary, the work presented in this thesis provides findings and datasets that have the potential to advance the ILC field

Development and Application of Chemometric Methods for Modelling Metabolic Spectral Profiles

The interpretation of metabolic information is crucial to understanding the functioning of a biological system. Latent information about the metabolic state of a sample can be acquired using analytical chemistry methods, which generate spectroscopic profiles. Thus, nuclear magnetic resonance spectroscopy and mass spectrometry techniques can be employed to generate vast amounts of highly complex data on the metabolic content of biofluids and tissue, and this thesis discusses ways to process, analyse and interpret these data successfully. The evaluation of J -resolved spectroscopy in magnetic resonance profiling and the statistical techniques required to extract maximum information from the projections of these spectra are studied. In particular, data processing is evaluated, and correlation and regression methods are investigated with respect to enhanced model interpretation and biomarker identification. Additionally, it is shown that non-linearities in metabonomic data can be effectively modelled with kernel-based orthogonal partial least squares, for which an automated optimisation of the kernel parameter with nested cross-validation is implemented. The interpretation of orthogonal variation and predictive ability enabled by this approach are demonstrated in regression and classification models for applications in toxicology and parasitology. Finally, the vast amount of data generated with mass spectrometry imaging is investigated in terms of data processing, and the benefits of applying multivariate techniques to these data are illustrated, especially in terms of interpretation and visualisation using colour-coding of images. The advantages of methods such as principal component analysis, self-organising maps and manifold learning over univariate analysis are highlighted. This body of work therefore demonstrates new means of increasing the amount of biochemical information that can be obtained from a given set of samples in biological applications using spectral profiling. Various analytical and statistical methods are investigated and illustrated with applications drawn from diverse biomedical areas