Structural Vaccinology for Viral Vaccine Design

Although vaccines have proven pivotal against arrays of infectious viral diseases, there are still no effective vaccines against many viruses. New structural insights into the viral envelope, protein conformation, and antigenic epitopes can guide the design of novel vaccines against challenging viruses such as human immunodeficiency virus (HIV), hepatitis C virus, enterovirus A71, and dengue virus. Recent studies demonstrated that applications of this structural information can solve some of the vaccine conundrums. This review focuses on recent advances in structure-based vaccine design, or structural vaccinology, for novel and innovative viral vaccine design

The methyltransferase and helicase enzymes as therapeutic targets of Zika virus : a bio- computational analysis of interactions with potential inhibitors.

Doctoral of Philosophy in Pharmaceutical Sciences. University of KwaZulu-Natal, Westville, 2019.The rampant Zika virus has received worldwide attention after becoming a global crisis following the Brazilian epidemic in 2015. From an obscure and neglected pathogen, Zika virus is now a notorious virus associated with neurological disorders in infants and adults. Since 2016, the rapid research response from the global scientific community have led to the discovery of numerous potential small molecule inhibitors and vaccines against the Zika virus. Although, in spite of this massive research initiative, there is still no effective antiviral nor vaccine that has made it out of clinical trials. The design and development of new chemical entities demands excessive cost, time and resources. Therefore, this study applies computer-aided drug design techniques, which accelerates the rational drug design process. Computational approaches including molecular docking, virtual screening, molecular modeling and molecular dynamics facilitate the filtration of large databases of compounds to sift out potential lead compounds. Furthermore, research has dedicated several resources toward FDA-approved drug repurposing. Generally, drugs have similar effects on viruses of the same family; hence drugs that have previously been effective in treating other flaviviruses, such as Dengue virus and West Nile virus, are being tested for its potential inhibition of Zika virus. However, the ability of these drugs to pass the bloodbrain barrier to treat infected neurons poses a challenge to anti-Zika virus drug discovery. This study proposes innovative strategies to design drugs that are capable of passing the blood-brain barrier, and to be able to use drugs that are impermeable via drug delivery mechanisms. This study also assesses the bioavailability and blood-brain barrier permeability of screened drugs to scrutinize the list of potential Zika virus inhibitors. Apart from identifying potential inhibitors, understanding the structural dynamics of viral targets and molecular mechanisms underlying potential inhibition of the virus is imperative. This study explores the structural and molecular dynamics of key targets of the Zika virus, the NS3 helicase and the NS5 methyltransferase enzymes, using computational approaches mentioned above and several others elaborated in this thesis. These computational methods also allowed the identification of precise interactions, amino acid residues, inhibitory mechanisms and pharmacophoric features involved in binding of lead compounds to these enzymes. IX Chapter 4 represents the first study of this thesis, which presents a concise literature background of Zika virus and identifies blood-brain barrier permeability as a core challenge in anti-Zika virus drug development. This study also provides approaches that may enable researchers to create effective anti-Zika virus drugs. Chapter 5 is the subsequent study of this thesis, which applies molecular dynamics to comparatively investigate the mechanism of inhibition and binding mode of two potential inhibitors, sinefungin and compound 5, to the NS5 methyltransferase. The specific pharmacophoric moieties of the most stable inhibitor are also identified in this study. Chapter 6 is the final study of this thesis, which examines the structural dynamics of the Zika virus NS3 helicase enzyme upon binding of ATPase inhibitor and flavivirus lead compound, resveratrol, and reports the key interactions and amino acid residues of the NS3 helicase that contribute highly to binding of resveratrol. This thesis presents an all-inclusive in silico assessment to advance research in drug design and development of Zika virus inhibitors, thus providing a greater understanding of the structural dynamics that occur in unbound and inhibitor-bound Zika virus target enzymes. Therefore, the constituents of this thesis are considered an essential platform in the progression of research toward anti-ZIKV drug design, discovery and delivery against Zika virus

Propagation of Imprecise Probabilities through Black Box Models

From the decision-based design perspective, decision making is the critical element of the design process. All practical decision making occurs under some degree of uncertainty. Subjective expected utility theory is a well-established method for decision making under uncertainty; however, it assumes that the DM can express his or her beliefs as precise probability distributions. For many reasons, both practical and theoretical, it can be beneficial to relax this assumption of precision. One possible means for avoiding this assumption is the use of imprecise probabilities. Imprecise probabilities are more expressive of uncertainty than precise probabilities, but they are also more computationally cumbersome. Probability Bounds Analysis (PBA) is a compromise between the expressivity of imprecise probabilities and the computational ease of modeling beliefs with precise probabilities. In order for PBA to be implemented in engineering design, it is necessary to develop appropriate computational methods for propagating probability boxes (p-boxes) through black box engineering models. This thesis examines the range of applicability of current methods for p-box propagation and proposes three alternative methods. These methods are applied towards the solution of three successively complex numerical examples.M.S.Committee Chair: Paredis, Chris; Committee Member: Bras, Bert; Committee Member: McGinnis, Leo

DEEP LEARNING METHODS FOR PREDICTION OF AND ESCAPE FROM PROTEIN RECOGNITION

Protein interactions drive diverse processes essential to living organisms, and thus numerous biomedical applications center on understanding, predicting, and designing how proteins recognize their partners. While unfortunately the number of interactions of interest still vastly exceeds the capabilities of experimental determination methods, computational methods promise to fill the gap. My thesis pursues the development and application of computational methods for several protein interaction prediction and design tasks. First, to improve protein-glycan interaction specificity prediction, I developed GlyBERT, which learns biologically relevant glycan representations encapsulating the components most important for glycan recognition within their structures. GlyBERT encodes glycans with a branched biochemical language and employs an attention-based deep language model to embed the correlation between local and global structural contexts. This approach enables the development of predictive models from limited data, supporting applications such as lectin binding prediction. Second, to improve protein-protein interaction prediction, I developed a unified geometric deep neural network, ‘PInet’ (Protein Interface Network), which leverages the best properties of both data- and physics-driven methods, learning and utilizing models capturing both geometrical and physicochemical molecular surface complementarity. In addition to obtaining state-of-the-art performance in predicting protein-protein interactions, PInet can serve as the backbone for other protein-protein interaction modeling tasks such as binding affinity prediction. Finally, I turned from ii prediction to design, addressing two important tasks in the context of antibodyantigen recognition. The first problem is to redesign a given antigen to evade antibody recognition, e.g., to help biotherapeutics avoid pre-existing immunity or to focus vaccine responses on key portions of an antigen. The second problem is to design a panel of variants of a given antigen to use as “bait” in experimental identification of antibodies that recognize different parts of the antigen, e.g., to support classification of immune responses or to help select among different antibody candidates. I developed a geometry-based algorithm to generate variants to address these design problems, seeking to maximize utility subject to experimental constraints. During the design process, the algorithm accounts for and balances the effects of candidate mutations on antibody recognition and on antigen stability. In retrospective case studies, the algorithm demonstrated promising precision, recall, and robustness of finding good designs. This work represents the first algorithm to systematically design antigen variants for characterization and evasion of polyclonal antibody responses