| Popis: |
Allostery is a fundamental biophysical mechanism that underlies cellular sensing, signaling, and metabolism. Quantitative methods to characterize the genotype-phenotype relationships for allosteric proteins would provide data needed to improve engineering of biological systems, to uncover the role of allosteric mis-regulation in disease, and to develop allosterically targeted drugs1. 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 determine the dose-response curves for nearly 105 variants of the LacI sensor. With the resulting data, we train a deep neural network (DNN) capable of predicting the dose-response curves for additional LacI genotypes in silico. We then map the impact of amino acid substitutions on the allosteric function of LacI. Additionally, we demonstrate engineering of allosteric function with unprecedented accuracy by identifying LacI variants from the measured landscape with quantitatively specified dose-response curves. Finally, we discover sensors with previously unreported band-stop dose-response curves. Overall, our results provide the first high-coverage, quantitative view of genotype-phenotype relationships for an allosteric protein, revealing a surprising diversity of phenotypes and showing that each phenotype is accessible via multiple distinct genotypes. |
