Computationally-guided design and selection of high performing ribosomal active site mutants

Simple item page
dc.contributor.authorKofman, Camila
dc.contributor.authorWatkins, Andrew M.
dc.contributor.authorKim, Do Soon
dc.contributor.authorWilli, Jessica A.
dc.contributor.authorWooldredge, Alexandra C.
dc.contributor.authorKarim, Ashty S.
dc.contributor.authorDas, Rhiju
dc.contributor.authorJewett, Michael C.
dc.date.accessioned2025-03-04T20:22:24Z
dc.date.available2025-03-04T20:22:24Z
dc.date.issued2022
dc.descriptionOpen access. Creative Commons Attribution 4.0 International license (CC BY 4.0) applies
dc.description.abstractUnderstanding how modifications to the ribosome affect function has implications for studying ribosome biogenesis, building minimal cells, and repurposing ribosomes for synthetic biology. However, efforts to design sequence-modified ribosomes have been limited because point mutations in the ribosomal RNA (rRNA), especially in the catalytic active site (peptidyl transferase center; PTC), are often functionally detrimental. Moreover, methods for directed evolution of rRNA are constrained by practical considerations (e.g. library size). Here, to address these limitations, we developed a computational rRNA design approach for screening guided libraries of mutant ribosomes. Our method includes in silico library design and selection using a Rosetta stepwise Monte Carlo method (SWM), library construction and in vitro testing of combined ribosomal assembly and translation activity, and functional characterization in vivo. As a model, we apply our method to making modified ribosomes with mutant PTCs. We engineer ribosomes with as many as 30 mutations in their PTCs, highlighting previously unidentified epistatic interactions, and show that SWM helps identify sequences with beneficial phenotypes as compared to random library sequences. We further demonstrate that some variants improve cell growth in vivo, relative to wild type ribosomes. We anticipate that SWM design and selection may serve as a powerful tool for rTNA engineering.
dc.description.peer-reviewYes
dc.identifier.citationKofman, C., Watkins, A. M., Kim, D. S., Willi, J. A., Wooldredge, A. C., Karim, A. S., Das, R., & Jewett, M. C. (2022). Computationally-guided design and selection of high performing ribosomal active site mutants. Nucleic Acids Research, 50(22), 13143-13154. https://doi.org/10.1093/nar/gkac1036
dc.identifier.urihttps://hdl.handle.net/10133/7017
dc.language.isoen
dc.publisherOxford Academic
dc.publisher.departmentDepartment of Chemistry & Biochemistry
dc.publisher.facultyArts and Science
dc.publisher.institutionNorthwestern University
dc.publisher.institutionStanford University
dc.publisher.institutionGenentech
dc.publisher.institutionInceptive Nucleics, Inc.
dc.publisher.institutionUniversity of Lethbridge
dc.publisher.urlhttps://doi.org/10.1093/nar/gkac1036
dc.subjectRibosomes
dc.subjectRibosomal RNA
dc.subjectMutant ribosomes
dc.subjectrRNA
dc.subject.lcshRibosomes--Research
dc.titleComputationally-guided design and selection of high performing ribosomal active site mutants
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Willi-computationally-guided.pdf
Size:
6.75 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.75 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Willi, Jessica