The genotype‐phenotype landscape of an allosteric protein
Abstract Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype‐phenotype relationships remains elusive. Here, we report the large‐scale measurement of the genotype‐phenotype landscape for a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2021-03-01
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| Series: | Molecular Systems Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.15252/msb.202010179 |
| _version_ | 1797284747717640192 |
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| author | Drew S Tack Peter D Tonner Abe Pressman Nathan D Olson Sasha F Levy Eugenia F Romantseva Nina Alperovich Olga Vasilyeva David Ross |
| author_facet | Drew S Tack Peter D Tonner Abe Pressman Nathan D Olson Sasha F Levy Eugenia F Romantseva Nina Alperovich Olga Vasilyeva David Ross |
| author_sort | Drew S Tack |
| collection | DOAJ |
| description | Abstract Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype‐phenotype relationships remains elusive. Here, we report the large‐scale measurement of the genotype‐phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long‐read and short‐read DNA sequencing, we quantitatively measure the dose‐response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence‐structure‐function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural‐network models, but unpredictable changes also occur. For example, we were surprised to discover a new band‐stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions. |
| first_indexed | 2024-03-07T17:53:15Z |
| format | Article |
| id | doaj.art-d9a3618aa3a44816b3647fe677f82e63 |
| institution | Directory Open Access Journal |
| issn | 1744-4292 |
| language | English |
| last_indexed | 2024-03-07T17:53:15Z |
| publishDate | 2021-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Molecular Systems Biology |
| spelling | doaj.art-d9a3618aa3a44816b3647fe677f82e632024-03-02T13:36:04ZengSpringer NatureMolecular Systems Biology1744-42922021-03-01173n/an/a10.15252/msb.202010179The genotype‐phenotype landscape of an allosteric proteinDrew S Tack0Peter D Tonner1Abe Pressman2Nathan D Olson3Sasha F Levy4Eugenia F Romantseva5Nina Alperovich6Olga Vasilyeva7David Ross8National Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USASLAC National Accelerator Laboratory Menlo Park CA USANational Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USANational Institute of Standards and Technology Gaithersburg MD USAAbstract Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Yet a quantitative understanding of allosteric genotype‐phenotype relationships remains elusive. Here, we report the large‐scale measurement of the genotype‐phenotype landscape for an allosteric protein: the lac repressor from Escherichia coli, LacI. Using a method that combines long‐read and short‐read DNA sequencing, we quantitatively measure the dose‐response curves for nearly 105 variants of the LacI genetic sensor. The resulting data provide a quantitative map of the effect of amino acid substitutions on LacI allostery and reveal systematic sequence‐structure‐function relationships. We find that in many cases, allosteric phenotypes can be quantitatively predicted with additive or neural‐network models, but unpredictable changes also occur. For example, we were surprised to discover a new band‐stop phenotype that challenges conventional models of allostery and that emerges from combinations of nearly silent amino acid substitutions.https://doi.org/10.15252/msb.202010179allosterygenetic sensorgenotype‐phenotype relationshipshigh‐throughput measurementstranscription factor |
| spellingShingle | Drew S Tack Peter D Tonner Abe Pressman Nathan D Olson Sasha F Levy Eugenia F Romantseva Nina Alperovich Olga Vasilyeva David Ross The genotype‐phenotype landscape of an allosteric protein Molecular Systems Biology allostery genetic sensor genotype‐phenotype relationships high‐throughput measurements transcription factor |
| title | The genotype‐phenotype landscape of an allosteric protein |
| title_full | The genotype‐phenotype landscape of an allosteric protein |
| title_fullStr | The genotype‐phenotype landscape of an allosteric protein |
| title_full_unstemmed | The genotype‐phenotype landscape of an allosteric protein |
| title_short | The genotype‐phenotype landscape of an allosteric protein |
| title_sort | genotype phenotype landscape of an allosteric protein |
| topic | allostery genetic sensor genotype‐phenotype relationships high‐throughput measurements transcription factor |
| url | https://doi.org/10.15252/msb.202010179 |
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