000			02970ntm a22003137a 4500
003			AT-ISTA
005			20250915131137.0
008			250915s2024 au ||||| m||| 00| 0 eng d
040			_cISTA
100			_aGonzalez Somermeyer, Louisa _91084229
245			_aFitness landscapes of orthologous green fluorescent proteins
260			_bInstitute of Science and Technology Austria _c2024
500			_aThesis
505			_aAbstract
505			_aAcknowledgements
505			_aAbout the Author
505			_aList of Collaborators and Publications
505			_aList of Figures
505			_aList of Tables
505			_aList of Abbreviations
505			_a1 Introduction
505			_a2 Results
505			_a3 Discussion
505			_a4 Materials and Methods
505			_a5 References
520			_aUnderstanding the relationship between a given phenotype and its underlying genotype or genotypes is one of the most pressing challenges of biology, as it lies at the heart of not only basic understanding of evolutionary theory, but also of practical applications in medicine and bioengineering. Understanding this relationship is complicated by the ubiquitous phenomenon of epistasis, wherein mutation effects are dependent on their genetic context. Fitness landscapes — representations of phenotype as a function of genotype — are being increasingly used as a tool to study the effects and interactions of thousands of mutations, but are experimentally limited to exploring a small fraction of a protein’s theoretical sequence space. Furthermore, not all regions of said sequence space are necessarily equally informative. Thus, gene selection for landscape surveys should be carefully considered in order to maximize the usable output of necessarily limited data. In this work, we analyzed the fitness landscapes of orthologous green fluorescent proteins from four different species, by systematically measuring the phenotype, fluorescence, of tens of thousands of mutant genotypes from each protein. These landscapes were highly heterogeneous, with some genes being mutationally robust and displaying epistasis only rarely, and others being highly epistatic and mutationally fragile. We used this data to train machine learning models to predict fluorescence from genotype. Although the training data contained almost exclusively genotypes with less than 3% sequence divergence from the original wild-type sequences, we were able to create novel, functional genotypes with up to 20% sequence divergence. Counterintuitively however, genes with high mutational robustness and rare epistasis were more difficult to introduce large numbers of mutations into, not less. This represents the first study of large-scale fitness landscapes of a protein family, and provides insights into how to approach future landscape surveys and their applications in novel protein design.
856			_uhttps://doi.org/10.15479/at:ista:17850
942			_2ddc
999			_c768067 _d768067