Biophysical Society Bulletin | February 2024
ASP GLU ARG LYS HIS TRP TYR PHE ASN GLN SE
ASP GLU ARG LYS HIS TRP TYR PHE ASN GLN SER
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Know the Editor Jeffrey Saucerman University of Virginia Editorial Board Member Biophysical Journal
Editor’s Pick GLU ARG LYS HIS TRP TYR PHE ASN GLN SER THR CYS PRO VAL MET ILE LEU GLY 0.3 0.2
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8.8 Å
2.6 Å
Jeffrey Saucerman
1.9 Å
Biophysical Journal Phase separation of multicomponent peptide mixtures into dehydrated clusters with hydrophilic cores William H. Brown and Davit A. Potoyan “Brown and Potoyan provide fascinating new insights into the molecular-level processes behind the liquid-liquid phase separation that may underlie the organization and function of membraneless organelles in cells. Using atomistic simulations of a (relatively) simple tripeptide solution, they illuminate direct and water-mediated interactions driving peptide clus tering. The paper reports an "inverse hydrophobic order” in which aromatic and charged residues accumulate in the core, while non-aromatic hydrophobic groups remain mostly at the surface. This inverse hydrophobic order runs counter to the organiza tion found in folded proteins, and has significant implications for modeling and interpretation of biomolecular condensates. It arises from the changing balance of specific and non-spe cific interactions as clusters grow and expel loosely bound water molecules. A second factor is that water-mediated hydrogen bonding stabilizes clusters: even through the net dehydration accompanying maturation, water molecules me diate contacts between polar and charged residues. A beautiful feature of the article is the use of a simplified tripeptide system, just complex enough to capture the maturation of condensates from nucleation through dense clustering. The model is sufficiently simple to make clear key
What are you currently working on that excites you? My lab combines computational modeling and quantitative experiments to understand the molecular networks that drive remodeling of the heart. Most biophysical modeling by our lab and others has focused on predicting how known mechanisms work together to produce function. But recently, we have found ways to discover new pathways underlying drugs or genet ic drivers by combining mechanistic modeling with machine learning. In a new paper by my PhD student Anders Nelson , we used this strategy to discover and validate signaling pathways that explain how drugs regulate myofibroblast activation. What has been your biggest “aha” moment in science? While we have focused primarily on intracelluar signaling, we collaborated with Merry Lindsey at Meharry Medical College to see if we could model the complex intercellular signaling dynamics after heart attack. Inflammation and fibrosis are typically described as sequential phases, with causal relation ships unclear. During the early pandemic on a late-night walk with my dog, it occurred to me that there were striking paral lels between inflammation-fibrosis and excitation-contraction coupling, which I had studied 15 years earlier in my postdoc with Don Bers . Modeling by my student Mukti Chowkwale demonstrated that this analogy ran deep, with both systems configured with dual positive feedback amplifiers separated by a transducer (TGFbeta acting like calcium). This also happens to be the design of a karaoke machine. These predictions have guided our subsequent modeling and experimental work on multiple aspects of what we now call inflammation-fibrosis coupling.
mechanisms behind biomolecular condensation.” Version of Record Published December 31, 2023 DOI: https:/doi.org/10.1016/j.bpj.2023.12.027
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February 2024
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