Fitness landscapes of orthologous green fluorescent proteins (Record no. 768067)

MARC details
000 -LEADER
fixed length control field 02970ntm a22003137a 4500
003 - CONTROL NUMBER IDENTIFIER
control field AT-ISTA
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250915131137.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 250915s2024 au ||||| m||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency ISTA
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Gonzalez Somermeyer, Louisa
9 (RLIN) 1084229
245 ## - TITLE STATEMENT
Title Fitness landscapes of orthologous green fluorescent proteins
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc. Institute of Science and Technology Austria
Date of publication, distribution, etc. 2024
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General note Thesis
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Formatted contents note Abstract
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Formatted contents note Acknowledgements
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Formatted contents note About the Author
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Formatted contents note List of Collaborators and Publications
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Formatted contents note List of Figures
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Formatted contents note List of Tables
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Formatted contents note List of Abbreviations
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Formatted contents note 1 Introduction
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Formatted contents note 2 Results
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Formatted contents note 3 Discussion
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Formatted contents note 4 Materials and Methods
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Formatted contents note 5 References
520 ## - SUMMARY, ETC.
Summary, etc. Understanding 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 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://doi.org/10.15479/at:ista:17850">https://doi.org/10.15479/at:ista:17850</a>
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  Not Lost Dewey Decimal Classification     Library Library 15/09/2025   Quiet Room AT-ISTA#003318 16/09/2025 15/09/2025 Book

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