Snowprint: a predictive tool for genetic biosensor discovery
Abstract Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-024-05849-8 |
| _version_ | 1797273554580930560 |
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| author | Simon d’Oelsnitz Sarah K. Stofel Joshua D. Love Andrew D. Ellington |
| author_facet | Simon d’Oelsnitz Sarah K. Stofel Joshua D. Love Andrew D. Ellington |
| author_sort | Simon d’Oelsnitz |
| collection | DOAJ |
| description | Abstract Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio . |
| first_indexed | 2024-03-07T14:46:03Z |
| format | Article |
| id | doaj.art-c2176f902cc54e7ca595ea3d0771403e |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-07T14:46:03Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-c2176f902cc54e7ca595ea3d0771403e2024-03-05T19:59:41ZengNature PortfolioCommunications Biology2399-36422024-02-01711910.1038/s42003-024-05849-8Snowprint: a predictive tool for genetic biosensor discoverySimon d’Oelsnitz0Sarah K. Stofel1Joshua D. Love2Andrew D. Ellington3Department of Molecular Biosciences, University of Texas at AustinDepartment of Molecular Biosciences, University of Texas at AustinIndependent Web DeveloperDepartment of Molecular Biosciences, University of Texas at AustinAbstract Bioengineers increasingly rely on ligand-inducible transcription regulators for chemical-responsive control of gene expression, yet the number of regulators available is limited. Novel regulators can be mined from genomes, but an inadequate understanding of their DNA specificity complicates genetic design. Here we present Snowprint, a simple yet powerful bioinformatic tool for predicting regulator:operator interactions. Benchmarking results demonstrate that Snowprint predictions are significantly similar for >45% of experimentally validated regulator:operator pairs from organisms across nine phyla and for regulators that span five distinct structural families. We then use Snowprint to design promoters for 33 previously uncharacterized regulators sourced from diverse phylogenies, of which 28 are shown to influence gene expression and 24 produce a >20-fold dynamic range. A panel of the newly repurposed regulators are then screened for response to biomanufacturing-relevant compounds, yielding new sensors for a polyketide (olivetolic acid), terpene (geraniol), steroid (ursodiol), and alkaloid (tetrahydropapaverine) with induction ratios up to 10.7-fold. Snowprint represents a unique, protein-agnostic tool that greatly facilitates the discovery of ligand-inducible transcriptional regulators for bioengineering applications. A web-accessible version of Snowprint is available at https://snowprint.groov.bio .https://doi.org/10.1038/s42003-024-05849-8 |
| spellingShingle | Simon d’Oelsnitz Sarah K. Stofel Joshua D. Love Andrew D. Ellington Snowprint: a predictive tool for genetic biosensor discovery Communications Biology |
| title | Snowprint: a predictive tool for genetic biosensor discovery |
| title_full | Snowprint: a predictive tool for genetic biosensor discovery |
| title_fullStr | Snowprint: a predictive tool for genetic biosensor discovery |
| title_full_unstemmed | Snowprint: a predictive tool for genetic biosensor discovery |
| title_short | Snowprint: a predictive tool for genetic biosensor discovery |
| title_sort | snowprint a predictive tool for genetic biosensor discovery |
| url | https://doi.org/10.1038/s42003-024-05849-8 |
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