High-throughput approaches to understand and engineer bacteriophages.

Bacteriophage research has been vital to fundamental aspects of modern biology. Advances in metagenomics have revealed treasure troves of new and uncharacterized bacteriophages ('phages') that are not yet understood. However, our ability to find new phages has outpaced our understanding of how sequence encodes function in phages. Traditional approaches for characterizing phages are limited in scale and face hurdles in determining how changes in sequence drive function. We describe powerful emerging technologies that can be used to clarify sequence-function relationships in phages through high-throughput genome engineering. Using these approaches, up to 105 variants can be characterized through pooled selection experiments and deep sequencing. We describe caveats when using these tools and provide examples of basic science and engineering goals that are pursuable using these approaches.

Discovering mechanisms of human genetic variation and controlling cell states at scale.

Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.

Computation-guided optimization of split protein systems.

Splitting bioactive proteins into conditionally reconstituting fragments is a powerful strategy for building tools to study and control biological systems. However, split proteins often exhibit a high propensity to reconstitute, even without the conditional trigger, limiting their utility. Current approaches for tuning reconstitution propensity are laborious, context-specific or often ineffective. Here, we report a computational design strategy grounded in fundamental protein biophysics to guide experimental evaluation of a sparse set of mutants to identify an optimal functional window. We hypothesized that testing a limited set of mutants would direct subsequent mutagenesis efforts by predicting desirable mutant combinations from a vast mutational landscape. This strategy varies the degree of interfacial destabilization while preserving stability and catalytic activity. We validate our method by solving two distinct split protein design challenges, generating both design and mechanistic insights. This new technology will streamline the generation and use of split protein systems for diverse applications.

Functional plasticity and evolutionary adaptation of allosteric regulation.

Allostery is a fundamental regulatory mechanism of protein function. Despite notable advances, understanding the molecular determinants of allostery remains an elusive goal. Our current knowledge of allostery is principally shaped by a structure-centric view, which makes it difficult to understand the decentralized character of allostery. We present a function-centric approach using deep mutational scanning to elucidate the molecular basis and underlying functional landscape of allostery. We show that allosteric signaling exhibits a high degree of functional plasticity and redundancy through myriad mutational pathways. Residues critical for allosteric signaling are surprisingly poorly conserved while those required for structural integrity are highly conserved, suggesting evolutionary pressure to preserve fold over function. Our results suggest multiple solutions to the thermodynamic conditions of cooperativity, in contrast to the common view of a finely tuned allosteric residue network maintained under selection.

De novo design of programmable inducible promoters.

Ligand-responsive allosteric transcription factors (aTF) play a vital role in genetic circuits and high-throughput screening because they transduce biochemical signals into gene expression changes. Programmable control of gene expression from aTF-regulated promoter is important because different downstream effector genes function optimally at different expression levels. However, tuning gene expression of native promoters is difficult due to complex layers of homeostatic regulation encoded within them. We engineered synthetic promoters de novo by embedding operator sites with varying affinities and radically reshaped binding preferences within a minimal, constitutive Escherichia coli promoter. Multiplexed cell-based screening of promoters for three TetR-like aTFs generated with this approach gave rich diversity of gene expression levels, dynamic ranges and ligand sensitivities and were 50- to 100-fold more active over their respective native promoters. Machine learning on our dataset revealed that relative position of the core motif and bases flanking the core motif play an important role in modulating induction response. Our generalized approach yields customizable and programmable aTF-regulated promoters for engineering cellular pathways and enables the discovery of new small molecule biosensors.

Engineering an allosteric transcription factor to respond to new ligands.

Genetic regulatory proteins inducible by small molecules are useful synthetic biology tools as sensors and switches. Bacterial allosteric transcription factors (aTFs) are a major class of regulatory proteins, but few aTFs have been redesigned to respond to new effectors beyond natural aTF-inducer pairs. Altering inducer specificity in these proteins is difficult because substitutions that affect inducer binding may also disrupt allostery. We engineered an aTF, the Escherichia coli lac repressor, LacI, to respond to one of four new inducer molecules: fucose, gentiobiose, lactitol and sucralose. Using computational protein design, single-residue saturation mutagenesis or random mutagenesis, along with multiplex assembly, we identified new variants comparable in specificity and induction to wild-type LacI with its inducer, isopropyl ß-D-1-thiogalactopyranoside (IPTG). The ability to create designer aTFs will enable applications including dynamic control of cell metabolism, cell biology and synthetic gene circuits.

Systems Approaches to Understanding and Designing Allosteric Proteins.

The study of allostery has a central place in biology because of the myriad roles of allosteric proteins in cellular function. As technologies for probing the spatiotemporal resolution of biomolecules have become increasingly sophisticated, so has our understanding of the diverse structural and molecular mechanisms of allosteric proteins. Studies have shown that the allosteric signal is transmitted a through a network of residue-residue interactions connecting distal sites on a protein. Linking structural and dynamical changes to the functional role of individual residues will give a more complete molecular view of allostery. In this work, we highlight new mutational technologies that enable a systems-level, quantitative description of allostery that dissect the role of individual residues through large-scale functional screens. A molecular model for predicting allosteric hot spots can be developed by applying statistical tools on the resulting large sequence-structure-function data sets. Design of allosteric proteins with new function is essential for engineering biological systems. Previous design efforts demonstrate that the allosteric network is a powerful functional constraint in the design of novel or enhanced allosteric proteins. We discuss how a priori knowledge of an allosteric network could improve rational design by facilitating better navigation of the design space. Understanding the molecular "rules" governing allostery would elucidate the molecular basis of dysfunction in disease-associated allosteric proteins, provide a means for designing tailored therapeutics, and enable the design of new sensors and enzymes for synthetic biology.

Engineering allostery.

Allosteric proteins have great potential in synthetic biology, but our limited understanding of the molecular underpinnings of allostery has hindered the development of designer molecules, including transcription factors with new DNA-binding or ligand-binding specificities that respond appropriately to inducers. Such allosteric proteins could function as novel switches in complex circuits, metabolite sensors, or as orthogonal regulators for independent, inducible control of multiple genes. Advances in DNA synthesis and next-generation sequencing technologies have enabled the assessment of millions of mutants in a single experiment, providing new opportunities to study allostery. Using the classic LacI protein as an example, we describe a genetic selection system using a bidirectional reporter to capture mutants in both allosteric states, allowing the positions most crucial for allostery to be identified. This approach is not limited to bacterial transcription factors, and could reveal new mechanistic insights and facilitate engineering of other major classes of allosteric proteins such as nuclear receptors, two-component systems, G protein-coupled receptors, and protein kinases.

Synthetic biosensors for precise gene control and real-time monitoring of metabolites.

Characterization and standardization of inducible transcriptional regulators has transformed how scientists approach biology by allowing precise and tunable control of gene expression. Despite their utility, only a handful of well-characterized regulators exist, limiting the complexity of engineered biological systems. We apply a characterization pipeline to four genetically encoded sensors that respond to acrylate, glucarate, erythromycin and naringenin. We evaluate how the concentration of the inducing chemical relates to protein expression, how the extent of induction affects protein expression kinetics, and how the activation behavior of single cells relates to ensemble measurements. We show that activation of each sensor is orthogonal to the other sensors, and to other common inducible systems. We demonstrate independent control of three fluorescent proteins in a single cell, chemically defining eight unique transcriptional states. To demonstrate biosensor utility in metabolic engineering, we apply the glucarate biosensor to monitor product formation in a heterologous glucarate biosynthesis pathway and identify superior enzyme variants. Doubling the number of well-characterized inducible systems makes more complex synthetic biological circuits accessible. Characterizing sensors that transduce the intracellular concentration of valuable metabolites into fluorescent readouts enables high-throughput screening of biological catalysts and alleviates the primary bottleneck of the metabolic engineering design-build-test cycle.

Evolution-guided optimization of biosynthetic pathways.

Engineering biosynthetic pathways for chemical production requires extensive optimization of the host cellular metabolic machinery. Because it is challenging to specify a priori an optimal design, metabolic engineers often need to construct and evaluate a large number of variants of the pathway. We report a general strategy that combines targeted genome-wide mutagenesis to generate pathway variants with evolution to enrich for rare high producers. We convert the intracellular presence of the target chemical into a fitness advantage for the cell by using a sensor domain responsive to the chemical to control a reporter gene necessary for survival under selective conditions. Because artificial selection tends to amplify unproductive cheaters, we devised a negative selection scheme to eliminate cheaters while preserving library diversity. This scheme allows us to perform multiple rounds of evolution (addressing ∼10(9) cells per round) with minimal carryover of cheaters after each round. Based on candidate genes identified by flux balance analysis, we used targeted genome-wide mutagenesis to vary the expression of pathway genes involved in the production of naringenin and glucaric acid. Through up to four rounds of evolution, we increased production of naringenin and glucaric acid by 36- and 22-fold, respectively. Naringenin production (61 mg/L) from glucose was more than double the previous highest titer reported. Whole-genome sequencing of evolved strains revealed additional untargeted mutations that likely benefit production, suggesting new routes for optimization.

A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery.

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.

A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery.

New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.

Rugged fitness landscapes minimize promiscuity in the evolution of transcriptional repressors.

How a protein's function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1,158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity, even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

Systematic genome-wide discovery of host factors governing bacteriophage infectivity.

Bacterial host factors regulate the infection cycle of bacteriophages. Except for some well-studied host factors (e.g., receptors or restriction-modification systems), the contribution of the rest of the host genome on phage infection remains poorly understood. We developed PHAGEPACK, a pooled assay that systematically and comprehensively measures each host-gene impact on phage fitness. PHAGEPACK combines CRISPR interference with phage packaging to link host perturbation to phage fitness during active infection. Using PHAGEPACK, we constructed a genome-wide map of genes impacting T7 phage fitness in permissive E. coli, revealing pathways previously unknown to affect phage packaging. When applied to the non-permissive E. coli O121, PHAGEPACK identified pathways leading to host resistance; their removal increased phage susceptibility up to a billion-fold. Bioinformatic analysis indicates phage genomes carry homologs or truncations of key host factors, potentially for fitness advantage. In summary, PHAGEPACK offers valuable insights into phage-host interactions, phage evolution, and bacterial resistance.

Highly multiplexed design of an allosteric transcription factor to sense novel ligands.

Allosteric transcription factors (aTF), widely used as biosensors, have proven challenging to design for detecting novel molecules because mutation of ligand-binding residues often disrupts allostery. We developed Sensor-seq, a high-throughput platform to design and identify aTF biosensors that bind to non-native ligands. We screened a library of 17,737 variants of the aTF TtgR, a regulator of a multidrug exporter, against six non-native ligands of diverse chemical structures - four derivatives of the cancer therapeutic tamoxifen, the antimalarial drug quinine, and the opiate analog naltrexone - as well as two native flavonoid ligands, naringenin and phloretin. Sensor-seq identified novel biosensors for each of these ligands with high dynamic range and diverse specificity profiles. The structure of a naltrexone-bound design showed shape-complementary methionine-aromatic interactions driving ligand specificity. To demonstrate practical utility, we developed cell-free detection systems for naltrexone and quinine. Sensor-seq enables rapid, scalable design of new biosensors, overcoming constraints of natural biosensors.

High-resolution structure prediction and the crystallographic phase problem.

The energy-based refinement of low-resolution protein structure models to atomic-level accuracy is a major challenge for computational structural biology. Here we describe a new approach to refining protein structure models that focuses sampling in regions most likely to contain errors while allowing the whole structure to relax in a physically realistic all-atom force field. In applications to models produced using nuclear magnetic resonance data and to comparative models based on distant structural homologues, the method can significantly improve the accuracy of the structures in terms of both the backbone conformations and the placement of core side chains. Furthermore, the resulting models satisfy a particularly stringent test: they provide significantly better solutions to the X-ray crystallographic phase problem in molecular replacement trials. Finally, we show that all-atom refinement can produce de novo protein structure predictions that reach the high accuracy required for molecular replacement without any experimental phase information and in the absence of templates suitable for molecular replacement from the Protein Data Bank. These results suggest that the combination of high-resolution structure prediction with state-of-the-art phasing tools may be unexpectedly powerful in phasing crystallographic data for which molecular replacement is hindered by the absence of sufficiently accurate previous models.

Deep metagenomic mining reveals bacteriophage sequence motifs driving host specificity.

Bacteriophages can adapt to new hosts by altering sequence motifs through recombination or convergent evolution. Where such motifs exist and what fitness advantage they confer remains largely unknown. We report a new method, Bacteriophage Library Informed Sequence Scoring (BLISS), to discover sequence motifs in metagenomic datasets governing phage activity. BLISS uses experimental deep mutational scanning data to create sequence profiles to enable deep mining of metagenomes for functional motifs which are otherwise invisible to searches. We experimentally tested 10,073 BLISS-derived sequence motifs for the receptor-binding protein of the T7 phage. The screen revealed hundreds of T7 variants with novel host specificity with functional motifs sourced from distant families besides other major phyla. Position, substitution and location preferences on T7 dictated different specificities. To demonstrate therapeutic utility, we engineered highly active T7 variants against urinary tract pathogens. BLISS is a powerful tool to unlock the functional potential encoded in phage metagenomes.

Discovering chromatin dysregulation induced by protein-coding perturbations at scale.

Although population-scale databases have expanded to millions of protein-coding variants, insight into variant mechanisms has not kept pace. We present PROD-ATAC, a high-throughput method for discovering the effects of protein-coding variants on chromatin. A pooled library of variants is expressed in a disease-agnostic cell line, and single-cell ATAC resolves each variant's effect on chromatin. Using PROD-ATAC, we characterized the effects of >100 oncofusions (a class of cancer-causing chimeric proteins) and controls and revealed that pioneer activity is a common feature of fusions spanning an enormous range of fusion frequencies. Further, fusion-induced dysregulation can be context-agnostic as observed mechanisms often overlapped with cancer and cell-type specific prior knowledge. We also showed that gain-of-function pioneering is common among oncofusions. This work provides a global view of fusion-induced chromatin. We uncovered convergent mechanisms among disparate oncofusions and shared modes of dysregulation across different cancers. PROD-ATAC is generalizable to any set of protein-coding variants.

Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins.

A fundamental question in protein science is where allosteric hotspots - residues critical for allosteric signaling - are located, and what properties differentiate them. We carried out deep mutational scanning (DMS) of four homologous bacterial allosteric transcription factors (aTFs) to identify hotspots and built a machine learning model with this data to glean the structural and molecular properties of allosteric hotspots. We found hotspots to be distributed protein-wide rather than being restricted to 'pathways' linking allosteric and active sites as is commonly assumed. Despite structural homology, the location of hotspots was not superimposable across the aTFs. However, common signatures emerged when comparing hotspots coincident with long-range interactions, suggesting that the allosteric mechanism is conserved among the homologs despite differences in molecular details. Machine learning with our large DMS datasets revealed global structural and dynamic properties to be a strong predictor of whether a residue is a hotspot than local and physicochemical properties. Furthermore, a model trained on one protein can predict hotspots in a homolog. In summary, the overall allosteric mechanism is embedded in the structural fold of the aTF family, but the finer, molecular details are sequence-specific.

Engineering a Dynamic Controllable Infectivity Switch in Bacteriophage T7.

Transcriptional repressors play an important role in regulating phage life cycle. Here, we examine how synthetic transcription repressors can be used in bacteriophage T7 to create a dynamic, controllable infectivity switch. We engineered T7 phage by replacing a large region of the early phage genome with different combinations of ligand-responsive promoters and ribosome binding sites (RBS) designed to control the phage RNA polymerase, gp1. Phages with engineered infectivity switch are fully viable at levels comparable to wildtype T7, when not repressed, indicating the phage can be engineered without loss of fitness. The most effective switch used a TetR-responsive promoter and an attenuated RBS, resulting in a 2-fold increase in latent period and a 10-fold decrease in phage titer when repressed. Phage activity can be further tuned using different inducer concentrations. Our study provides a proof of concept for how a simple synthetic circuit introduced into the phage genome enables user control over phage infectivity.