Abstract
Proteins are a crucial part of all organisms, maintaining structural integrity and carrying out enzymatic functions. As a string of amino acids is translated, it must fold into its native conformation and bind to other subunits or ligands. Protein folding and binding are intricate processes involving covalent and non-covalent interactions among atoms, and the direction of the processes is governed by chemical thermodynamics and kinetics. A perturbation of the evolutionarily-calibrated state of equilibrium can result in a loss of function or pathogenic aggregation. Thus, understanding protein stability and how it impacts function and fitness is an intense area of study in various disciplines ranging from medicine to protein engineering.In this work, we utilize high-throughput computational and experimental methods to systematically investigate the relationship between protein stability and organismal fitness in bacteriophage fX174. Chapter 1 focuses on FoldX, which is a stability prediction software. Even though computational methods of predicting protein stability are indispensable in large scale studies, most algorithms do not provide a measure of uncertainty of their predictions. In order to compensate for this limitation and increase the utility of FoldX, we demonstrate predictive modeling of FoldX error using a linear regression framework. In Chapter 2, we present findings from a deep mutational scanning (DMS) experiment using bacteriophage fX174, where we use statistical modeling of the relationship between capsid protein stability and the viability and fitness of hundreds of phage variants. The modeling shows that we can quantify the stability threshold beyond which the phage is no longer viable. However, protein stability is inadequate in predicting quantitative fitness effects among viable phages. Finally, in Chapter 3, we extend the DMS study of fX174 variants to investigate the relationship between growth and decay rates and the effects of high temperature on these variants. We find that the rates of growth and decay are inversely correlated, meaning that the higher the growth rate, the slower the decay rate, and vice versa. We also find that there are distinct groups of variants that survive in high temperatures, and others that do not survive even in normal temperatures. However, studying these variants using FoldX and molecular dynamics simulation does not reveal distinguishing characteristics between these groups.
Altogether, this work combines high-throughput computational and experimental methods and statistical modeling, and contributes to our understanding of how mutational changes in stability impact fitness.