Peter D Tonner | bioRxiv Science

bioRxiv2020

The genotype-phenotype landscape of an allosteric protein

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.

synthetic biology↗

bioRxiv2016

Detecting differential growth of microbial populations with Gaussian process regression

Microbial growth curves are used to study differential effects of media, genetics, and stress on microbial population growth. Consequently, many modeling frameworks exist to capture microbial population growth measurements. However, current models are designed to quantify growth under conditions that produce a specific functional form. Extensions to these models are required to quantify the effects of perturbations, which often exhibit non-standard growth curves. Rather than fix expected functional forms of different experimental perturbations, we developed a general and robust model of microbial population growth curves using Gaussian process (GP) regression. GP regression modeling of high resolution time-series growth data enables accurate quantification of population growth, and can be extended to identify differential growth phenotypes due to genetic background or stress. Additionally, confounding effects due to experimental variation can be controlled explicitly. Our framework substantially outperforms commonly used microbial population growth models, particularly when modeling growth data from environmentally stressed populations. We apply the GP growth model to a collection of growth measurements for seven transcription factor knockout strains of a model archaeal organism, Halobacterium salinarum. Using these models fitted to growth data, two statistical tests were developed to quantify the differential effects of genetic and environmental perturbations on microbial growth. These statistical tests accurately identify known regulators and implicate novel regulators of growth under standard and stress conditions. Furthermore, the fitted GP regression models are interpretable, recapitulating biological knowledge of growth response while providing new insights into the relevant parameters affecting microbial population growth.

Systems Biology↗