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The formulation of biologics for increased shelf life stability is a complex task that depends on the chemical composition of both the active ingredient and any excipients in solution. 

Arginine has been a mainstay in biological formulation development for decades. To date, the way arginine modulates protein stability has been widely studied and debated. Here, we employed a hydrophobic polymer to decouple hydrophobic effects from other interactions relevant to protein folding. While existing hypotheses for the effects of arginine can generally be categorized as either direct or indirect, our results indicate that direct and indirect mechanisms of arginine co-exist and oppose each other. At low concentrations, arginine was observed to stabilize hydrophobic polymer folding via a sidechain-dominated direct mechanism, while at high concentrations, arginine stabilized polymer folding via a backbone-dominated indirect mechanism. Upon introducing partially charged polymer sites, arginine destabilized polymer folding. Further, we found arginine-induced destabilization of a model virus similar to direct-mechanism destabilization of the charged polymer and concentration-dependent stabilization of a model protein similar to the indirect mechanism of hydrophobic polymer stabilization. These findings highlight the modular nature of the widely used additive arginine, with relevance in the information-driven design of stable biological formulations. 

Can we modulate active site flexibility to engineer broad range temperature activity in thermophilic enzymes? 

Heterogeneous nucleation is the dominant form of liquid-to-solid transition in na- ture. Although molecular simulations are most uniquely suited to study nucleation, the waiting time to observe even a single nucleation event can easily exceed current computational capabilities. Therefore, there exists an imminent need for methods that enable computationally fast and feasible studies of heterogeneous nucleation. Seeding is a technique that has proven successful at dramatically expanding the range of computationally accessible nucleation rates in simulation studies of ho- mogeneous crystal nucleation. In this paper, we introduce a new seeding method for heterogeneous nucleation called Rigid Seeding (RSeeds). Crystalline seeds are treated as pseudo-rigid bodies and simulated on a surface with metastable liquid above its melting temperature. This allows the seeds to adapt to the surface and identify favorable seed–surface configurations, which is necessary for reliable predictions of crystal polymorphs that form and the corresponding heterogeneous nucle- ation rates. We demonstrate and validate RSeeds for heterogeneous ice nucleation on a flexible self-assembled monolayer surface, a mineral surface based on kaolinite, and two model surfaces. RSeeds predicts the correct ice polymorph, exposed crystal plane, and rotation on the surface. RSeeds is semiquantitative and can be used to estimate the critical nucleus size and nucleation rate when combined with classical nucleation theory. We demonstrate that RSeeds can be used to evaluate nucleation rates spanning many orders of magnitude. 

Molecular simulations are a powerful tool in the study of crystallization and polymorphic transitions yielding detailed information of transformation mechanisms with high spatiotemporal resolution. How- ever, characterizing various crystalline and amorphous phases as well as sampling nucleation events and structural transitions remain extremely challenging tasks. The integration of machine learning with molecular simulations has the potential of unprecedented advancement in the area of crystal nucleation and growth. In this article, we discuss recent progress in the analysis and sampling of structural trans- formations aided by machine learning and the resulting potential future directions opening in this area. 

Tweetable abstract How can we optimize the design of enzyme-responsive polymersomes to better treat disease? In this perspective, three common modes of enzymatic action in these nanoparticles are identified.