--- OA_place: publisher _id: '17850' abstract: - lang: eng text: "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.\r\n\r\nIn 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." acknowledged_ssus: - _id: Bio - _id: LifeSc - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Louisa full_name: Gonzalez Somermeyer, Louisa id: 4720D23C-F248-11E8-B48F-1D18A9856A87 last_name: Gonzalez Somermeyer orcid: 0000-0001-9139-5383 citation: ama: Gonzalez Somermeyer L. Fitness landscapes of orthologous green fluorescent proteins. 2024. doi:10.15479/at:ista:17850 apa: Gonzalez Somermeyer, L. (2024). Fitness landscapes of orthologous green fluorescent proteins. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17850 chicago: Gonzalez Somermeyer, Louisa. “Fitness Landscapes of Orthologous Green Fluorescent Proteins.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17850. ieee: L. Gonzalez Somermeyer, “Fitness landscapes of orthologous green fluorescent proteins,” Institute of Science and Technology Austria, 2024. ista: Gonzalez Somermeyer L. 2024. Fitness landscapes of orthologous green fluorescent proteins. Institute of Science and Technology Austria. mla: Gonzalez Somermeyer, Louisa. Fitness Landscapes of Orthologous Green Fluorescent Proteins. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17850. short: L. Gonzalez Somermeyer, Fitness Landscapes of Orthologous Green Fluorescent Proteins, Institute of Science and Technology Austria, 2024. corr_author: '1' date_created: 2024-09-06T12:57:44Z date_published: 2024-09-06T00:00:00Z date_updated: 2026-04-07T13:25:01Z day: '06' ddc: - '570' degree_awarded: PhD department: - _id: GradSch - _id: FyKo doi: 10.15479/at:ista:17850 ec_funded: 1 file: - access_level: open_access checksum: d3303724e8d3c91321d71bbad4062048 content_type: application/pdf creator: lgonzale date_created: 2024-09-27T10:32:33Z date_updated: 2024-09-27T10:32:33Z file_id: '18151' file_name: louisa_thesis_draft__240904b.pdf file_size: 11219837 relation: main_file - access_level: closed checksum: 22e63f7f9014dffde2af7a47e7d1d014 content_type: application/vnd.openxmlformats-officedocument.wordprocessingml.document creator: lgonzale date_created: 2024-09-27T10:34:34Z date_updated: 2024-09-27T10:34:34Z file_id: '18152' file_name: louisa_thesis_draft__240904b.docx file_size: 43338677 relation: source_file file_date_updated: 2024-09-27T10:34:34Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '09' oa: 1 oa_version: Published Version page: '89' project: - _id: 2564DBCA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '665385' name: International IST Doctoral Program - _id: 26580278-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '771209' name: Characterizing the fitness landscape on population and global scales publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: link: - relation: software url: https://github.com/aequorea238/Orthologous_GFP_Fitness_Peaks record: - id: '11448' relation: part_of_dissertation status: public status: public supervisor: - first_name: Fyodor full_name: Kondrashov, Fyodor id: 44FDEF62-F248-11E8-B48F-1D18A9856A87 last_name: Kondrashov orcid: 0000-0001-8243-4694 title: Fitness landscapes of orthologous green fluorescent proteins tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: dissertation user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd year: '2024' ...