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.

CH-π Interactions Are Required for Human Galectin-3 Function.

Glycan-binding proteins, or lectins, recognize distinct structural elements of polysaccharides, to mediate myriad biological functions. Targeting glycan-binding proteins involved in human disease has been challenging due to an incomplete understanding of the molecular mechanisms that govern protein-glycan interactions. Bioinformatics and structural studies of glycan-binding proteins indicate that aromatic residues with the potential for CH-π interactions are prevalent in glycan-binding sites. However, the contributions of these CH-π interactions to glycan binding and their relevance in downstream function remain unclear. An emblematic lectin, human galectin-3, recognizes lactose and N-acetyllactosamine-containing glycans by positioning the electropositive face of a galactose residue over the tryptophan 181 (W181) indole forming a CH-π interaction. We generated a suite of galectin-3 W181 variants to assess the importance of these CH-π interactions to glycan binding and function. As determined experimentally and further validated with computational modeling, variants with smaller or less electron-rich aromatic side chains (W181Y, W181F, W181H) or sterically similar but nonaromatic residues (W181M, W181R) showed poor or undetectable binding to lactose and attenuated ability to bind mucins or agglutinate red blood cells. The latter functions depend on multivalent binding, highlighting that weakened CH-π interactions cannot be overcome by avidity. Two galectin-3 variants with disrupted hydrogen bonding interactions (H158A and E184A) showed similarly impaired lactose binding. Molecular simulations demonstrate that all variants have decreased binding orientation stability relative to native galectin-3. Thus, W181 collaborates with the endogenous hydrogen bonding network to enhance binding affinity for lactose, and abrogation of these CH-π interactions is as deleterious as eliminating key hydrogen bonding interactions. These findings underscore the critical roles of CH-π interactions in carbohydrate binding and lectin function and will aid the development of novel lectin inhibitors.

Homology modeling of TMPRSS2 yields candidate drugs that may inhibit entry of SARS-CoV-2 into human cells.

The most rapid path to discovering treatment options for the novel coronavirus SARS-CoV-2 is to find existing medications that are active against the virus. We have focused on identifying repurposing candidates for the transmembrane serine protease family member II (TMPRSS2), which is critical for entry of coronaviruses into cells. Using known 3D structures of close homologs, we created seven homology models. We also identified a set of serine protease inhibitor drugs, generated several conformations of each, and docked them into our models. We used three known chemical (non-drug) inhibitors and one validated inhibitor of TMPRSS2 in MERS as benchmark compounds and found six compounds with predicted high binding affinity in the range of the known inhibitors. We also showed that a previously published weak inhibitor, Camostat, had a significantly lower binding score than our six compounds. All six compounds are anticoagulants with significant and potentially dangerous clinical effects and side effects. Nonetheless, if these compounds significantly inhibit SARS-CoV-2 infection, they could represent a potentially useful clinical tool.