Jeremy Schmit
@SchmitBiophys
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Emergent behavior in biomolecules. I study how biomolecules interact. Not just 1 or 2 molecules, but moles of them.
Manhattan, Kansas
Joined September 2008
Thrilled to be a part of this collaboration!.
How do proteins mis-fold?. New preprint led by @JAunstrup from @alexander_buell @biophysics_dtu group with MD simulations by Abigail Barclay. We combined experimental measurements of Φ-values with restrained simulations to study the TSE for fibril growth.
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RT @Castaneda_lab: Congrats to the lab and collaborator @SchmitBiophys ! (more soon!) Polyubiquitin ligand-induced phase transitions are op….
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RT @JCIM_JCTC: Circuit Topology Approach for the Comparative Analysis of Intrinsically Disordered Proteins #IDP #compchem #cheminformatics….
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RT @PernillaWittung: Fun collaboration with @SchmitBiophys - I learnt a lot in process and our discovery opens the door for many more exper….
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RT @Castaneda_lab: Interested in designing ligands to optimize phase separation of scaffolds in condensates? With @SchmitBiophys, we dived….
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Thanks to @OASaleh and Fyl Pincus for a great visit to UCSB. And to all the physics and BMSE students for their fantastic questions. It's amazing how much the place has grown since I was there!.
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Afternoon fun at the Protein Folding #GRC with Ed O'Brian, @fried_lab, and @SulkowskaL. Not shown: @davethirum
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Getting an early start on #GRC Protein Folding Dynamics. Looking forward to seeing everyone in Ventura!
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Thanks so much for the opportunity to speak!.
@SchmitBiophys showed how the network structure encode the functional properties of condensate to give unique structure/function properties. He also reminded us that about the limitations of condensates as reaction crucibles: Slow diffusion. ⬇️.
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Honored and excited to present in @IDPseminars this week!.
Join us on Thursday for seminars by @ioana_ilie_UvA and @SchmitBiophys! . New attendees can register at:
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RT @jiexiao_lab: Assistant professors wanted! Come to join us at Johns Hopkins School of Medicine Biophysics!
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RT @IDPseminars: After a brief break, we are happy to announce our program for the coming months featuring:.@BoeynaemsSteven, @DrJoanShea @….
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Special thanks to lead author @PhanBioPhys for doing all the hard work and @HalfmannLab for helpful discussions. 9/9.
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Our result explains co-aggregation data in length variants. Since amyloid nuclei only involve a few amino acids from each chain, they are relatively insensitive to sequence or length differences. Differences are more important during elongation. 8/9.
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Once you get about 3 or 4 beta strands, it suddenly becomes favorable for the beta core to expand which gives a bigger binding surface for new molecules. This means the amyloid has transitioned from nucleation to elongation. 7/9.
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To minimize the loss of entropy, amyloid nuclei will remain small. This means that nuclei have a small beta core, with long disordered tails. 6/9.
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The two kinds of entropy mean that the surface tension is anisotropic, so the shape of an amyloid nucleus will change just like different crystals can be compact or needle-like depending on the facet surface energies. 5/9.
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The nucleation barrier in aggregating systems is due to entropy loss, which looks just like a surface tension after some math tricks. In amyloids there are two kinds of lost entropy: translational and conformational. 4/9.
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