Biology subjects

Tonner, P. D.

Publications and source records attributed to Tonner, P. D..

7 recordsLinked to original sources

bioRxiv2025

Epistasis in Allosteric Proteins: Can Biophysical Models Provide a Better Framework for Prediction and Understanding?

The prediction of epistasis, or the interaction between mutations, is a complex challenge impacting protein science, healthcare, and biotechnology. For allosteric proteins, the prediction of epistatic effects is further complicated by the intricate networks of conformational states and binding interactions inherent to their function. Here, we explore these issues by systematically comparing biophysical and phenomenological models to analyze mutational effects and epistasis for the lac repressor protein, LacI. Using an extensive dataset consisting of dose-response measurements for 164 LacI variants, we find that while the phenomenological Hill model provides slightly better predictive accuracy, the biophysical model fits the data more parsimoniously, with significantly less epistasis in its parameters. Our results highlight the importance of the multi-state, multi-dimensional nature of allosteric function and the potential benefits of using biophysical models for the analysis of mutational effects and epistasis.

biophysics↗

bioRxiv2022

Precision engineering of biological function with large-scale measurements and machine learning

As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensors sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.

synthetic biology↗

bioRxiv2021

Interpretable modeling of genotype-phenotype landscapes with state-of-the-art predictive power

Large-scale measurements linking genetic background to biological function have driven a need for models that can incorporate these data for reliable predictions and insight into the underlying biochemical system. Recent modeling efforts, however, prioritize predictive accuracy at the expense of model interpretability. Here, we present LANTERN (https://github.com/usnistgov/lantern), a hierarchical Bayesian model that distills genotype-phenotype landscape (GPL) measurements into a low-dimensional feature-space that represents the fundamental biological mechanisms of the system while also enabling straightforward, explainable predictions. Across a benchmark of large-scale datasets, LANTERN equals or outperforms all alternative approaches, including deep neural networks. LANTERN furthermore extracts useful insights into the landscape including its inherent dimensionality, a latent space of additive mutational effects, and novel metrics of landscape structure. LANTERN facilitates straightforward discovery of fundamental mechanisms in GPLs, while also reliably extrapolating to unexplored regions of genotypic-space.

bioinformatics↗

bioRxiv2021

Predicted Coronavirus Nsp5 Protease Cleavage Sites in the Human Proteome: A Resource for SARS-CoV-2 Research

BackgroundThe coronavirus nonstructural protein 5 (Nsp5) is a cysteine protease required for processing the viral polyprotein and is therefore crucial for viral replication. Nsp5 from several coronaviruses have also been found to cleave host proteins, disrupting molecular pathways involved in innate immunity. Nsp5 from the recently emerged SARS-CoV-2 virus interacts with and can cleave human proteins, which may be relevant to the pathogenesis of COVID-19. Based on the continuing global pandemic, and emerging understanding of coronavirus Nsp5-human protein interactions, we set out to predict what human proteins are cleaved by the coronavirus Nsp5 protease using a bioinformatics approach. ResultsUsing a previously developed neural network trained on coronavirus Nsp5 cleavage sites (NetCorona), we made predictions of Nsp5 cleavage sites in all human proteins. Structures of human proteins in the Protein Data Bank containing a predicted Nsp5 cleavage site were then examined, generating a list of 92 human proteins with a highly predicted and accessible cleavage site. Of those, 48 are expected to be found in the same cellular compartment as Nsp5. Analysis of this targeted list of proteins revealed molecular pathways susceptible to Nsp5 cleavage and therefore relevant to coronavirus infection, including pathways involved in mRNA processing, cytokine response, cytoskeleton organization, and apoptosis. ConclusionsThis study combines predictions of Nsp5 cleavage sites in human proteins with protein structure information and protein network analysis. We predicted cleavage sites in proteins recently shown to be cleaved in vitro by SARS-CoV-2 Nsp5, and we discuss how other potentially cleaved proteins may be relevant to coronavirus mediated immune dysregulation. The data presented here will assist in the design of more targeted experiments, to determine the role of coronavirus Nsp5 cleavage of host proteins, which is relevant to understanding the molecular pathology of SARS-CoV-2 infection.

bioinformatics↗

bioRxiv2020

The genotype-phenotype landscape of an allosteric protein

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.

biophysics↗

bioRxiv2020

Improved stability of an engineered function using adapted bacterial strains

Engineering useful functions into cells is one of the primary goals of synthetic biology. However, engineering novel functions that remain stable for multiple generations remains a significant challenge. Here we report the importance of host fitness on the stability of an engineered function. We find that the initial fitness of the host cell affects the stability of the engineered function. We demonstrate that adapting a strain to the intended growth condition increases fitness and in turn improves the stability of the engineered function over hundreds of generations. This approach offers a simple and effective method to increase the stability of engineered functions without genomic modification or additional engineering and will be useful in improving the stability of novel, engineered functions in living cells. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/979385v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@dfeebeorg.highwire.dtl.DTLVardef@1b31a24org.highwire.dtl.DTLVardef@1795435org.highwire.dtl.DTLVardef@8775c4_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2019

A Bayesian Non-parametric Mixed-Effects Model of MicrobialPhenotypes

Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth.

systems biology↗