ATG3 proteins possess a unique amphipathic α-helix essential for the Atg8/LC3 lipidation reaction.

In our recent paper, we uncovered that ATG3 exhibits a large degree of structural dynamics on autophagic membranes to efficiently carry out LC3 lipidation. ATG3 proteins possess an amphipathic α-helix (AH) identified by a small number of bulky and hydrophobic residues. This biophysical fingerprint allows for transient membrane association of ATG3 and facilitates its enzymatic reaction. This study will pave the way for a structural and mechanistic understanding of how membrane association of ATG proteins is orchestrated during autophagosome formation.

Membrane association of the ATG8 conjugation machinery emerges as a key regulatory feature for autophagosome biogenesis.

Autophagy is a highly conserved intracellular pathway that is essential for survival in all eukaryotes. In healthy cells, autophagy is used to remove damaged intracellular components, which can be as simple as unfolded proteins or as complex as whole mitochondria. Once the damaged component is captured, the autophagosome engulfs it and closes, isolating the content from the cytoplasm. The autophagosome then fuses with the late endosome and/or lysosome to deliver its content to the lysosome for degradation. Formation of the autophagosome, sequestration or capture of content, and closure all require the ATG proteins, which constitute the essential core autophagy protein machinery. This brief 'nutshell' will highlight recent data revealing the importance of small membrane-associated domains in the ATG proteins. In particular, recent findings from two parallel studies reveal the unexpected key role of α-helical structures in the ATG8 conjugation machinery and ATG8s. These studies illustrate how unique membrane association modules can control the formation of autophagosomes.

Molecular Free Energies, Rates, and Mechanisms from Data-Efficient Path Sampling Simulations.

Molecular dynamics is a powerful tool for studying the thermodynamics and kinetics of complex molecular events. However, these simulations can rarely sample the required time scales in practice. Transition path sampling overcomes this limitation by collecting unbiased trajectories and capturing the relevant events. Moreover, the integration of machine learning can boost the sampling while simultaneously learning a quantitative representation of the mechanism. Still, the resulting trajectories are by construction non-Boltzmann-distributed, preventing the calculation of free energies and rates. We developed an algorithm to approximate the equilibrium path ensemble from machine-learning-guided path sampling data. At the same time, our algorithm provides efficient sampling, mechanism, free energy, and rates of rare molecular events at a very moderate computational cost. We tested the method on the folding of the mini-protein chignolin. Our algorithm is straightforward and data-efficient, opening the door to applications in many challenging molecular systems.

RNA folding pathways from all-atom simulations with a variationally improved history-dependent bias.

Atomically detailed simulations of RNA folding have proven very challenging in view of the difficulties of developing realistic force fields and the intrinsic computational complexity of sampling rare conformational transitions. As a step forward in tackling these issues, we extend to RNA an enhanced path-sampling method previously successfully applied to proteins. In this scheme, the information about the RNA's native structure is harnessed by a soft history-dependent biasing force promoting the generation of productive folding trajectories in an all-atom force field with explicit solvent. A rigorous variational principle is then applied to minimize the effect of the bias. Here, we report on an application of this method to RNA molecules from 20 to 47 nucleotides long and increasing topological complexity. By comparison with analog simulations performed on small proteins with similar size and architecture, we show that the RNA folding landscape is significantly more frustrated, even for relatively small chains with a simple topology. The predicted RNA folding mechanisms are found to be consistent with the available experiments and some of the existing coarse-grained models. Due to its computational performance, this scheme provides a promising platform to efficiently gather atomistic RNA folding trajectories, thus retain the information about the chemical composition of the sequence.

Unique amphipathic α helix drives membrane insertion and enzymatic activity of ATG3.

Autophagosome biogenesis requires a localized perturbation of lipid membrane dynamics and a unique protein-lipid conjugate. Autophagy-related (ATG) proteins catalyze this biogenesis on cellular membranes, but the underlying molecular mechanism remains unclear. Focusing on the final step of the protein-lipid conjugation reaction, the ATG8/LC3 lipidation, we show how the membrane association of the conjugation machinery is organized and fine-tuned at the atomistic level. Amphipathic α helices in ATG3 proteins (AHATG3) have low hydrophobicity and contain less bulky residues. Molecular dynamics simulations reveal that AHATG3 regulates the dynamics and accessibility of the thioester bond of the ATG3~LC3 conjugate to lipids, enabling the covalent lipidation of LC3. Live-cell imaging shows that the transient membrane association of ATG3 with autophagic membranes is governed by the less bulky-hydrophobic feature of AHATG3. The unique properties of AHATG3 facilitate protein-lipid bilayer association, leading to the remodeling of the lipid bilayer required for the formation of autophagosomes.