Effects of cholesterol on the mechanism of fengycin, a biofungicide.

Fengycins are a class of antifungal lipopeptides synthesized by the bacteria Bacillus subtilis, commercially available as the primary component of the agricultural fungicide Serenade. They are toxic to fungi but far less to mammalian cells. One key difference between mammalian and fungal cell membranes is the presence of cholesterol only in the former; recent experimental work showed that the presence of cholesterol reduces fengycin-induced membrane leakage. Since our previous all-atom and coarse-grained simulations suggested that aggregation of membrane-bound fengycin is central to its ability to disrupt membranes, we hypothesized that cholesterol might reduce fengycin aggregation. Here, we test this hypothesis using coarse-grained molecular dynamics simulations, with sampling enhanced via the weighted ensemble method. The results indicate that cholesterol subtly alters the size distribution for fengycin aggregates, limits the lateral range of their membrane disordering, and reduces the ability of aggregates to bend the membrane. Taken together, these phenomena may account for cholesterol's effects on fengycin activity.

Ionizable Amino Lipids Distribution and Effects on DSPC/Cholesterol Membranes: Implications for Lipid Nanoparticle Structure.

Lipid nanoparticles (LNPs) containing ionizable aminolipids are among the leading platforms for the successful delivery of nucleic-acid-based therapeutics, including messenger RNA (mRNA). The two recently FDA-approved COVID-19 vaccines developed by Moderna and Pfizer/BioNTech belong to this category. Ionizable aminolipids, cholesterol, and DSPC lipids are among the key components of such formulations, crucially modulating physicochemical properties of these formulations and, consequently, the potency of these therapeutics. Despite the importance of these components, the distribution of these molecules in LNPs containing mRNA is not clear. In this study, we used all-atom molecular dynamics (MD) simulations to investigate the distribution and effects of the Lipid-5 (apparent pKa of the lipid nanoparticle = 6.56), a rationally designed and previously reported ionizable aminolipid by Moderna, on lipid bilayers [Mol. Ther. 2018, 26, 1509-1519]. The simulations were conducted with half of the aminolipids charged and half neutral approximately to the expected ionization in the microenvironment of the LNP surface. In all five simulated systems in this work, the cholesterol content was kept constant, whereas the DSPC and Lipid-5 concentrations were changed systematically. We found that at higher concentrations of the ionizable aminolipids, the neutral aminolipids form a disordered aggregate in the membrane interior that preferentially includes cholesterol. The rules underlying the lipid redistribution could be used to rationally choose lipids to optimize the LNP function.

Therapeutic enzyme engineering using a generative neural network.

Enhancing the potency of mRNA therapeutics is an important objective for treating rare diseases, since it may enable lower and less-frequent dosing. Enzyme engineering can increase potency of mRNA therapeutics by improving the expression, half-life, and catalytic efficiency of the mRNA-encoded enzymes. However, sequence space is incomprehensibly vast, and methods to map sequence to function (computationally or experimentally) are inaccurate or time-/labor-intensive. Here, we present a novel, broadly applicable engineering method that combines deep latent variable modelling of sequence co-evolution with automated protein library design and construction to rapidly identify metabolic enzyme variants that are both more thermally stable and more catalytically active. We apply this approach to improve the potency of ornithine transcarbamylase (OTC), a urea cycle enzyme for which loss of catalytic activity causes a rare but serious metabolic disease.

Selectivity and Mechanism of Fengycin, an Antimicrobial Lipopeptide, from Molecular Dynamics.

Fengycin is a cyclic lipopeptide used as an agricultural fungicide. It is synthesized by Bacillus subtilis as an immune response against fungal infection and functions by damaging the target's cell membrane. Previous molecular dynamics simulations and experiments have led to the hypothesis that the aggregation of fengycins on the membrane surface plays a key role in cell disruption. Here, we used microsecond-scale all-atom molecular dynamics simulations to understand the specificity, selectivity, and structure of fengycin oligomers. Our simulations suggest that fengycin is more likely to form stable oligomers in model fungal membranes (phosphatidylcholine) compared to the model bacterial membranes (phosphatidylethanolamine:phosphatidylglycerol). Furthermore, we characterize the differences in the structure and kinetics of the membrane-bound aggregates and discuss their functional implications.