Biology subjects

Bethel, N.

Publications and source records attributed to Bethel, N..

3 recordsLinked to original sources

bioRxiv2023

Modulation of FGF pathway signaling and vascular differentiation using designed oligomeric assemblies

Growth factors and cytokines signal by binding to the extracellular domains of their receptors and drive association and transphosphorylation of the receptor intracellular tyrosine kinase domains, initiating downstream signaling cascades. To enable systematic exploration of how receptor valency and geometry affects signaling outcomes, we designed cyclic homo-oligomers with up to 8 subunits using repeat protein building blocks that can be modularly extended. By incorporating a de novo designed fibroblast growth-factor receptor (FGFR) binding module into these scaffolds, we generated a series of synthetic signaling ligands that exhibit potent valency- and geometry-dependent Ca2+ release and MAPK pathway activation. The high specificity of the designed agonists reveal distinct roles for two FGFR splice variants in driving endothelial and mesenchymal cell fates during early vascular development. The ability to incorporate receptor binding domains and repeat extensions in a modular fashion makes our designed scaffolds broadly useful for probing and manipulating cellular signaling pathways. HighlightsO_LIDe novo designed cyclic oligomers with tunable geometric properties C_LIO_LICyclic, homo-oligomeric FGFR binding modules induce geometry- and valency-dependent activity of isoform-specific FGF signaling C_LIO_LIModulation of FGFR isoform activity controls bifurcation of endothelial and mesenchymal fate during vascular development C_LIO_LIC-isoform activation favors arterial endothelial cell formation while B-isoform induces pericyte differentiation C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/532666v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@151f6eforg.highwire.dtl.DTLVardef@2ae877org.highwire.dtl.DTLVardef@103e0d1org.highwire.dtl.DTLVardef@19cd695_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2022

Robust deep learning based protein sequence design using ProteinMPNN

While deep learning has revolutionized protein structure prediction, almost all experimentally characterized de novo protein designs have been generated using physically based approaches such as Rosetta. Here we describe a deep learning based protein sequence design method, ProteinMPNN, with outstanding performance in both in silico and experimental tests. The amino acid sequence at different positions can be coupled between single or multiple chains, enabling application to a wide range of current protein design challenges. On native protein backbones, ProteinMPNN has a sequence recovery of 52.4%, compared to 32.9% for Rosetta. Incorporation of noise during training improves sequence recovery on protein structure models, and produces sequences which more robustly encode their structures as assessed using structure prediction algorithms. We demonstrate the broad utility and high accuracy of ProteinMPNN using X-ray crystallography, cryoEM and functional studies by rescuing previously failed designs, made using Rosetta or AlphaFold, of protein monomers, cyclic homo-oligomers, tetrahedral nanoparticles, and target binding proteins. One-sentence summaryA deep learning based protein sequence design method is described that is widely applicable to current design challenges and shows outstanding performance in both in silico and experimental tests.

biophysics↗

bioRxiv2021

Computational design of nanoscale rotational mechanics in de novo protein assemblies

Natural nanomachines like the F1/F0-ATPase contain protein components that undergo rotation relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein rotary machinery from designed axle and ring components. Using cryoelectron microscopy, we find that axle-ring systems assemble as designed and populate diverse rotational states depending on symmetry match or mismatch and the designed interface energy landscape. These mechanical systems with internal rotational degrees of freedom are a step towards the systematic design of genetically encodable nanomachines. One-Sentence SummaryComputationally designed self-assembling protein rotary machines sample internal degrees of freedom sculpted within the energy landscape.

synthetic biology↗