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SARS-CoV-2 infection is controlled by the opening of the spike protein receptor binding domain (RBD), which transitions from a glycan-shielded (down) to an exposed (up) state in order to bind the human ACE2 receptor and infect cells. While snapshots of the up and down states have been obtained by cryoEM and cryoET, details of the RBD opening transition evade experimental characterization. Here, over 200 μs of weighted ensemble (WE) simulations of the fully glycosylated spike ectodomain allow us to characterize more than 300 continuous, kinetically unbiased RBD opening pathways. Together with biolayer interferometry experiments, we reveal a gating role for the N-glycan at position N343, which facilitates RBD opening. Residues D405, R408, and D427 also participate. The atomic-level characterization of the glycosylated spike activation mechanism provided herein achieves a new high-water mark for ensemble pathway simulations and offers a foundation for understanding the fundamental mechanisms of SARS-CoV-2 viral entry and infection.

The noncovalent equilibrium activation of a fluorogenic malachite green dye and its cognate fluorogen activating protein has been exploited to produce a sparse labeling distribution of densely tagged genetically encoded proteins, enabling single molecule detection and superresolution imaging in fixed and living cells. These sparse labeling conditions are achieved by control of the dye concentration in the milieu, and do not require any photoswitching or photoactivation. The labeling is achieved using physiological buffers and cellular media, and does not require additives or switching buffer to obtain superresolution images. We evaluate superresolution properties and images obtained from a selected fluorogen activating protein clone fused to actin, and show that the photon counts per object fall between those typically reported for fluorescent proteins and switching dye-pairs, resulting in 10-30 nm localization precision per object. This labeling strategy complements existing approaches, and may simplify multicolor labeling of cellular structures.

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The ability to detect single molecules over the electronic noise requires high performance detector systems. Electron Multiplying Charge-Coupled Device (EMCCD) cameras have been employed successfully to image single molecules. Recently, scientific Complementary Metal Oxide Semiconductor (sCMOS) based cameras have been introduced with very low read noise at faster read out rates, smaller pixel sizes and a lower price compared to EMCCD cameras. In this study, we have compared the two technologies using two EMCCD and three sCMOS cameras to detect single Cy5 molecules. Our findings indicate that the sCMOS cameras perform similar to EMCCD cameras for detecting and localizing single Cy5 molecules.

Recent approaches to the study of biological molecules employ manifold learning to single-particle cryo-EM data sets to map the continuum of states of a molecule into a low-dimensional space spanned by eigenvectors or "conformational coordinates". This is done separately for each projection direction (PD) on an angular grid. One important step in deriving a consolidated map of occupancies, from which the free energy landscape of the molecule can be derived, is to propagate the conformational coordinates from a given choice of "anchor PD" across the entire angular space. Even when one eigenvector dominates, its sign might invert from one PD to the next. The propagation of the second eigenvector is particularly challenging when eigenvalues of the second and third eigenvector are closely matched, leading to occasional inversions in their ranking as we move across the angular grid. In the absence of a computational approach, this propagation across the angular space has been done thus far "by hand" using visual clues, thus greatly limiting the general use of the technique. In this work we have developed a method that is able to solve the propagation problem computationally, by using optical flow and a probabilistic graphical model. We demonstrate its utility by selected examples.

This work is based on the manifold-embedding approach to study biological molecules exhibiting continuous conformational changes. Previous work established a method—now termed ManifoldEM—capable of reconstructing 3D movies and accompanying free-energy landscapes from single-particle cryo-EM images of macromolecules exercising multiple conformational degrees of freedom. While ManifoldEM has proven its viability in several experimental studies, critical limitations and uncertainties have been found throughout its extended development and use. Guided by insights from studies with cryo-EM ground-truth data, simulated from atomic structures undergoing conformational changes, we have built a novel framework, ESPER, able to retrieve the free-energy landscape and respective 3D Coulomb potential maps for all states simulated. As shown by a direct comparison of ground truth vs. recovered maps, and analysis of experimental data from the 80S ribosome and ryanodine receptor, ESPER offers substantial improvements relative to the previous work.

This work is based on the manifold-embedding approach to the study of biological molecules exhibiting conformational changes in a continuum. Previous studies established a workflow capable of reconstructing atomic-level structures in the conformational continuum from cryo-EM images so as to reveal the latent space of macromolecules undergoing multiple degrees of freedom. Here, we introduce a new approach that is informed by detailed heuristic analysis of manifolds formed by simulated heterogeneous cryo-EM datasets. These simulated models were generated with increasing complexity to account for multiple motions, state occupancies and CTF in a wide range of signal-to-noise ratios. Using these datasets as ground-truth, we provide detailed exposition of our findings using several conformational motions while exploring the available parameter space. Guided by these insights, we build a framework to leverage the high-dimensional geometric information obtained towards reconstituting the quasi-continuum of conformational states in the form of an energy landscape and respective 3D maps for all states therein. This framework offers substantial enhancements relative to previous work, for which a direct comparison of outputs has been provided.