Free Energy Analysis of Peptide-Induced Pore Formation in Lipid Membranes by Bridging Atomistic and Coarse-Grained Simulations.

Antimicrobial peptides (AMPs) are attractive materials for combating the antimicrobial resistance crisis because they can kill target microbes by directly disrupting cell membranes. Although thousands of AMPs have been discovered, their molecular mechanisms of action are still poorly understood. One broad mechanism for membrane disruption is the formation of membrane-spanning hydrophilic pores which can be stabilized by AMPs. In this study, we use molecular dynamics simulations to investigate the thermodynamics of pore formation in model single-component lipid membranes in the presence of one of three AMPs: aurein 1.2, melittin and magainin 2. To overcome the general challenge of modeling long time scale membrane-related behaviors, including AMP binding, clustering, and pore formation, we develop a generalizable methodology for sampling AMP-induced pore formation. This approach involves the long equilibration of peptides around a pore created with a nucleation collective variable by performing coarse-grained simulations, then backmapping equilibrated AMP-membrane configurations to all-atom resolution. We then perform all-atom simulations to resolve free energy profiles for pore formation while accurately modeling the interplay of lipid-peptide-solvent interactions that dictate pore formation free energies. Using this approach, we quantify free energy barriers for pore formation without direct biases on peptides or whole lipids, allowing us to investigate mechanisms of pore formation for these 3 AMPs that are a consequence of unbiased peptide diffusion and clustering. Further analysis of simulation trajectories then relates variations in pore lining by AMPs, AMP-induced lipid disruptions, and salt bridges between AMPs to the observed pore formation free energies and corresponding mechanisms. This methodology and mechanistic analysis have the potential to generalize beyond the AMPs in this study to improve our understanding of pore formation by AMPs and related antimicrobial materials.

Computationally modeling mammalian succinate dehydrogenase kinetics identifies the origins and primary determinants of ROS production.

Succinate dehydrogenase (SDH) is an inner mitochondrial membrane protein complex that links the Krebs cycle to the electron transport system. It can produce significant amounts of superoxide ([Formula: see text]) and hydrogen peroxide (H2O2); however, the precise mechanisms are unknown. This fact hinders the development of next-generation antioxidant therapies targeting mitochondria. To help address this problem, we developed a computational model to analyze and identify the kinetic mechanism of [Formula: see text] and H2O2 production by SDH. Our model includes the major redox centers in the complex, namely FAD, three iron-sulfur clusters, and a transiently bound semiquinone. Oxidation state transitions involve a one- or two-electron redox reaction, each being thermodynamically constrained. Model parameters were simultaneously fit to many data sets using a variety of succinate oxidation and free radical production data. In the absence of respiratory chain inhibitors, model analysis revealed the 3Fe-4S iron-sulfur cluster as the primary [Formula: see text] source. However, when the quinone reductase site is inhibited or the quinone pool is highly reduced, [Formula: see text] is generated primarily by the FAD. In addition, H2O2 production is only significant when the enzyme is fully reduced, and fumarate is absent. Our simulations also reveal that the redox state of the quinone pool is the primary determinant of free radical production by SDH. In this study, we showed the importance of analyzing enzyme kinetics and associated side reactions in a consistent, quantitative, and biophysically detailed manner using a diverse set of experimental data to interpret and explain experimental observations from a unified perspective.