Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding.

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

Short tandem repeats bind transcription factors to tune eukaryotic gene expression.

Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)-DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites.

Functional map of SARS-CoV-2 3CL protease reveals tolerant and immutable sites.

The SARS-CoV-2 3CL protease (3CLpro) is an attractive therapeutic target, as it is essential to the virus and highly conserved among coronaviruses. However, our current understanding of its tolerance to mutations is limited. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the 3CLpro and validate a subset of our results within authentic viruses. We reveal that the 3CLpro is highly malleable and is capable of tolerating mutations throughout the protein. Yet, we also identify specific residues that appear immutable, suggesting that these may be targets for future 3CLpro inhibitors. Finally, we utilize our screening as a basis to identify E166V as a resistance-conferring mutation against the clinically used 3CLpro inhibitor, nirmatrelvir. Collectively, the functional map presented herein may serve as a guide to better understand the biological properties of the 3CLpro and for drug development against coronaviruses.

The Functional Landscape of SARS-CoV-2 3CL Protease.

SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) as the etiologic agent of COVID-19 (coronavirus disease 2019) has drastically altered life globally. Numerous efforts have been placed on the development of therapeutics to treat SARS-CoV-2 infection. One particular target is the 3CL protease (3CL pro ), which holds promise as it is essential to the virus and highly conserved among coronaviruses, suggesting that it may be possible to find broad inhibitors that treat not just SARS-CoV-2 but other coronavirus infections as well. While the 3CL protease has been studied by many groups for SARS-CoV-2 and other coronaviruses, our understanding of its tolerance to mutations is limited, knowledge which is particularly important as 3CL protease inhibitors become utilized clinically. Here, we develop a yeast-based deep mutational scanning approach to systematically profile the activity of all possible single mutants of the SARS-CoV-2 3CL pro , and validate our results both in yeast and in authentic viruses. We reveal that the 3CL pro is highly malleable and is capable of tolerating mutations throughout the protein, including within the substrate binding pocket. Yet, we also identify specific residues that appear immutable for function of the protease, suggesting that these interactions may be novel targets for the design of future 3CL pro inhibitors. Finally, we utilize our screening results as a basis to identify E166V as a resistance-conferring mutation against the therapeutic 3CL pro inhibitor, nirmatrelvir, in clinical use. Collectively, the functional map presented herein may serve as a guide for further understanding of the biological properties of the 3CL protease and for drug development for current and future coronavirus pandemics.

High-Throughput Affinity Measurements of Transcription Factor and DNA Mutations Reveal Affinity and Specificity Determinants.

Transcription factors (TFs) bind regulatory DNA to control gene expression, and mutations to either TFs or DNA can alter binding affinities to rewire regulatory networks and drive phenotypic variation. While studies have profiled energetic effects of DNA mutations extensively, we lack similar information for TF variants. Here, we present STAMMP (simultaneous transcription factor affinity measurements via microfluidic protein arrays), a high-throughput microfluidic platform enabling quantitative characterization of hundreds of TF variants simultaneously. Measured affinities for ∼210 mutants of a model yeast TF (Pho4) interacting with 9 oligonucleotides (>1,800 Kds) reveal that many combinations of mutations to poorly conserved TF residues and nucleotides flanking the core binding site alter but preserve physiological binding, providing a mechanism by which combinations of mutations in cis and trans could modulate TF binding to tune occupancies during evolution. Moreover, biochemical double-mutant cycles across the TF-DNA interface reveal molecular mechanisms driving recognition, linking sequence to function. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.

Chemically Induced Cell Wall Stapling in Bacteria.

Transpeptidation reinforces the structure of cell-wall peptidoglycan, an extracellular heteropolymer that protects bacteria from osmotic lysis. The clinical success of transpeptidase-inhibiting ß-lactam antibiotics illustrates the essentiality of these cross-linkages for cell-wall integrity, but the presence of multiple, seemingly redundant transpeptidases in many species makes it challenging to determine cross-link function. Here, we present a technique to link peptide strands by chemical rather than enzymatic reaction. We employ biocompatible click chemistry to induce triazole formation between azido- and alkynyl-d-alanine residues that are metabolically installed in the peptidoglycan of Gram-positive or Gram-negative bacteria. Synthetic triazole cross-links can be visualized using azidocoumarin-d-alanine, an amino acid derivative that undergoes fluorescent enhancement upon reaction with terminal alkynes. Cell-wall stapling protects Escherichia coli from treatment with the broad-spectrum ß-lactams ampicillin and carbenicillin. Chemical control of cell-wall structure in live bacteria can provide functional insights that are orthogonal to those obtained by genetics.

BET-seq: Binding energy topographies revealed by microfluidics and high-throughput sequencing.

Biophysical models of transcriptional regulation rely on energetic measurements of the binding affinities between transcription factors (TFs) and target DNA binding sites. Historically, assays capable of measuring TF-DNA binding affinities have been relatively low-throughput (measuring ~103 sequences in parallel) and have required significant specialized equipment, limiting their use to a handful of laboratories. Recently, we developed an experimental assay and analysis pipeline that allows measurement of binding energies between a single TF and up to 106 DNA species in a single experiment (Binding Energy Topography by sequencing, or BET-seq) (Le et al., 2018). BET-seq employs the Mechanically Induced Trapping of Molecular Interactions (MITOMI) platform to purify DNA bound to a TF at equilibrium followed by high coverage sequencing to reveal relative differences in binding energy for each sequence. While we have previously used BET-seq to refine the binding affinity landscapes surrounding high-affinity DNA consensus target sites, we anticipate this technique will be applied in future work toward measuring a wide variety of TF-DNA landscapes. Here, we provide detailed instructions and general considerations for DNA library design, performing BET-seq assays, and analyzing the resulting data.

Host actin polymerization tunes the cell division cycle of an intracellular pathogen.

Growth and division are two of the most fundamental capabilities of a bacterial cell. While they are well described for model organisms growing in broth culture, very little is known about the cell division cycle of bacteria replicating in more complex environments. Using a D-alanine reporter strategy, we found that intracellular Listeria monocytogenes (Lm) spend a smaller proportion of their cell cycle dividing compared to Lm growing in broth culture. This alteration to the cell division cycle is independent of bacterial doubling time. Instead, polymerization of host-derived actin at the bacterial cell surface extends the non-dividing elongation period and compresses the division period. By decreasing the relative proportion of dividing Lm, actin polymerization biases the population toward cells with the highest propensity to form actin tails. Thus, there is a positive-feedback loop between the Lm cell division cycle and a physical interaction with the host cytoskeleton.