Document Type

Dissertation - Open Access

Award Date

2022

Degree Name

Doctor of Philosophy (PhD)

Department / School

Chemistry and Biochemistry

First Advisor

Suvobrata Chakravarty

Keywords

Anion-quadruple Interaction, gnomAD: Genome Aggregation Database, In-silico prediction tools, Isothermal Titration Calorimetry, Polymerase Chain Reaction, SARS-CoV-2: Severe Acute Respiratory Syndrome

Abstract

All genetic variations in the human genome are due to mutations, and therefore determining the impact of the different categories of mutations, particularly nonsynonymous single nucleotide variants (SNVs) which are the most abundant in the population is essential for understanding the genetics of human diseases. Generations of mutation studies have focused on a relatively small number, partly due to technological limitations. However, advances in next-generation sequencing have allowed for empirical assessments of genome-wide mutations. Recent studies have shown that the genome sequence of any individual contains extensive protein-altering genetic variation (missense mutations) and of which only a few are unambiguously deleterious. Characterizing the phenotypes of these mutants promise to help explain the molecular basis of the genetics of human diseases and the differences between individuals’ susceptibility to infections. Here, we took advantage of the millions of publicly available SNVs to understand their impact on the population. In the first approach, we used COVID-19 as a disease model and employed 12 widely used tools to probe mutation phenotypes of thousands of SNVs of human proteins that are engaged in two pathways: (a) innate immune response and (b) SARSCoV- 2–host RNA–protein interactions during SARS-CoV-2 viral life cycle. In the second approach, we made reagents to probe the impact of SNVs on the functional relevance of a few select proteins, particularly with emphasis on how these missense mutations perturb noncovalent interactions significant in maintaining the stability of protein structures. Based on the in-silico evaluation, we identify 40 variants (out of 6,800 SNVs of 47 innate immunity proteins) that have the potential to link SARS-CoV-2 susceptibility to the genetic background of an individual. Similarly, among the interacting human proteins, 105 out of 3,802 SNVs are expected to compromise the virus-host interactions. Also, we successfully made a robust assay to comprehensively measure the strength of AQ interactions on protein interfaces. Analysis of the interaction energies of the mutant proteins with six different in silico protein stability prediction tools shows that AQ interactions on average typically produce strong stabilizing energies that could contribute to structural stability and their perturbation significantly destabilizes protein structures.

Number of Pages

152

Publisher

South Dakota State University

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Supplementary File

Available for download on Friday, December 15, 2023

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Rights Statement

In Copyright