Jackson, B. A. | Search bioRxiv

bioRxiv2024

Essential and virulence-related protein interactions of pathogens revealed through deep learning

Identification of bacterial protein-protein interactions and predicting the structures of the complexes could aid in the understanding of pathogenicity mechanisms and developing treatments for infectious diseases. Here, we developed a deep learning-based pipeline that leverages residue-residue coevolution and protein structure prediction to systematically identify and structurally characterize protein-protein interactions at the proteome-wide scale. Using this pipeline, we searched through 78 million pairs of proteins across 19 human bacterial pathogens and identified 1923 confidently predicted complexes involving essential genes and 256 involving virulence factors. Many of these complexes were not previously known; we experimentally tested 12 such predictions, and half of them were validated. The predicted interactions span core metabolic and virulence pathways ranging from post-transcriptional modification to acid neutralization to outer membrane machinery and should contribute to our understanding of the biology of these important pathogens and the design of drugs to combat them.

biochemistry↗

bioRxiv2023

Engineering Programmable Material-To-Cell Pathways Via Synthetic Notch Receptors To Spatially Control Cellular Phenotypes In Multi-Cellular Constructs

Synthetic Notch (synNotch) receptors are modular synthetic components that are genetically engineered into mammalian cells to detect signals presented by neighboring cells and respond by activating prescribed transcriptional programs. To date, synNotch has been used to program therapeutic cells and pattern morphogenesis in multicellular systems. However, cell-presented ligands have limited versatility for applications that require spatial precision, such as tissue engineering. To address this, we developed a suite of materials to activate synNotch receptors and serve as generalizable platforms for generating user-defined material-to-cell signaling pathways. First, we demonstrate that synNotch ligands, such as GFP, can be conjugated to cell- generated ECM proteins via genetic engineering of fibronectin produced by fibroblasts. We then used enzymatic or click chemistry to covalently link synNotch ligands to gelatin polymers to activate synNotch receptors in cells grown on or within a hydrogel. To achieve microscale control over synNotch activation in cell monolayers, we microcontact printed synNotch ligands onto a surface. We also patterned tissues comprising cells with up to three distinct phenotypes by engineering cells with two distinct synthetic pathways and culturing them on surfaces microfluidically patterned with two synNotch ligands. We showcase this technology by co-transdifferentiating fibroblasts into skeletal muscle or endothelial cell precursors in user-defined spatial patterns towards the engineering of muscle tissue with prescribed vascular networks. Collectively, this suite of approaches extends the synNotch toolkit and provides novel avenues for spatially controlling cellular phenotypes in mammalian multicellular systems, with many broad applications in developmental biology, synthetic morphogenesis, human tissue modeling, and regenerative medicine.

synthetic biology↗