Flexible spikes: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein enables viral entry into host cells by binding to the angiotensin-converting enzyme 2 (ACE2) receptor and is a major target for neutralizing antibodies. About 20 to 40 spikes decorate the surface of virions. Turoňová et al. now show that the spike is flexibly connected to the viral surface by three hinges that are well protected by glycosylation sites. The flexibility imparted by these hinges may explain how multiple spikes act in concert to engage onto the flat surface of a host cell. Science , this issue p. 203

Molecular self-organization driven by concerted many-body interactions produces the ordered structures that define both inanimate and living matter. Here we present an autonomous path sampling algorithm that integrates deep learning and transition path theory to discover the mechanism of molecular self-organization phenomena. The algorithm uses the outcome of newly initiated trajectories to construct, validate and—if needed—update quantitative mechanistic models. Closing the learning cycle, the models guide the sampling to enhance the sampling of rare assembly events. Symbolic regression condenses the learned mechanism into a human-interpretable form in terms of relevant physical observables. Applied to ion association in solution, gas-hydrate crystal formation, polymer folding and membrane-protein assembly, we capture the many-body solvent motions governing the assembly process, identify the variables of classical nucleation theory, uncover the folding mechanism at different levels of resolution and reveal competing assembly pathways. The mechanistic descriptions are transferable across thermodynamic states and chemical space.

Cells maintain membrane fluidity by regulating lipid saturation, but the molecular mechanisms of this homeoviscous adaptation remain poorly understood. We have reconstituted the core machinery for regulating lipid saturation in baker’s yeast to study its molecular mechanism. By combining molecular dynamics simulations with experiments, we uncover a remarkable sensitivity of the transcriptional regulator Mga2 to the abundance, position, and configuration of double bonds in lipid acyl chains, and provide insights into the molecular rules of membrane adaptation. Our data challenge the prevailing hypothesis that membrane fluidity serves as the measured variable for regulating lipid saturation. Rather, we show that Mga2 senses the molecular lipid-packing density in a defined region of the membrane. Our findings suggest that membrane property sensors have evolved remarkable sensitivities to highly specific aspects of membrane structure and dynamics, thus paving the way toward the development of genetically encoded reporters for such properties in the future.

Significance: Direct simulations explore the dynamics of physical systems at their natural pace. Molecular dynamics (MD) simulations (e.g., of macromolecular folding) extensively revisit typical configurations until rare and interesting transition events occur. Biasing the simulator away from regions already explored can, therefore, drastically accelerate the discovery of features. We propose an enhanced sampling simulation framework, where MD and machine learning adaptively bootstrap each other. Machine learning guides the search for important configurations by processing information from previous explorations. This search proceeds iteratively in an algorithmically orchestrated fashion without advance knowledge of suitable collective variables. Applied to a molecular sensor of lipid saturation in membranes, a helix dissociation pathway not seen in millisecond simulations is discovered at the second iteration.