The energetic landscape of CH-π interactions in protein-carbohydrate binding.

CH-π interactions between carbohydrates and aromatic amino acids play an essential role in biological systems that span all domains of life. Quantifying the strength and importance of these CH-π interactions is challenging because these interactions involve several atoms and can exist in many distinct orientations. To identify an orientational landscape of CH-π interactions, we constructed a dataset of close contacts formed between β-d-galactose residues and the aromatic amino acids, tryptophan, tyrosine, and phenylalanine, across crystallographic structures deposited in the Protein Data Bank.

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.

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.

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.