CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find effectively models both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for analysis of the assembling 50S ribosome dataset (Davis et al., EMPIAR-10076), including preparation of inputs, network training, and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single particle cryo-EM datasets and with moderate experience programming in Python and navigating Jupyter notebooks. It requires 3-4 days to complete..
Ribosome assembly is orchestrated by many assembly factors, including ribosomal RNA methyltransferases whose precise role is poorly understood. Here, we leverage the power of cryo-EM and machine learning to discover that the bacterial methyltransferase KsgA performs a novel "proofreading" function in assembly of the ribosomal small subunit by recognizing and partially disassembling particles that have matured but are not competent for translation. We propose that this activity allows inactive particles an opportunity to reassemble into an active state, thereby increasing overall assembly fidelity. Detailed structural quantifications in our datasets additionally enabled expansion of the Nomura assembly map to highlight rRNA helix and r-protein interdependencies, which newly details how binding and docking of these elements are tightly coupled. These results have wide-ranging implications in our understanding of the quality control mechanisms governing ribosome biogenesis, and showcase the power of heterogeneity analysis in cryo-EM to unveil functionally relevant information in biological systems.
CryoDRGN is a machine learning system for heterogeneous cryo-EM reconstruction of proteins and protein complexes from single particle cryo-EM data. Central to this approach is a deep generative model for heterogeneous cryo-EM density maps, which we empirically find effectively models both discrete and continuous forms of structural variability. Once trained, cryoDRGN is capable of generating an arbitrary number of 3D density maps, and thus interpreting the resulting ensemble is a challenge. Here, we showcase interactive and automated processing approaches for analyzing cryoDRGN results. Specifically, we detail a step-by-step protocol for analysis of the assembling 50S ribosome dataset (Davis et al., EMPIAR-10076), including preparation of inputs, network training, and visualization of the resulting ensemble of density maps. Additionally, we describe and implement methods to comprehensively analyze and interpret the distribution of volumes with the assistance of an associated atomic model. This protocol is appropriate for structural biologists familiar with processing single particle cryo-EM datasets and with moderate experience programming in Python and navigating Jupyter notebooks. It requires 3-4 days to complete.
The human ether-à-go-go-related gene (hERG) encodes the pore-forming alpha subunits of the channel responsible for the rapidly activating delayed rectifier potassium current (I Kr) in the heart. Reductions in I Kr cause long QT syndrome, which predisposes individuals to potentially fatal arrhythmias that can be triggered by stress. One link between stress and hERG function is protein kinase C (PKC) activation. Up to date, PKC activation has been shown to have conflicting effects on hERG function. This study investigates how activating PKC with phorbol 12-myristate 13-acetate (PMA) affects hERG channels expressed in human embryonic kidney (HEK) 293 cells. We show that chronic (24 hour) PKC activation increases hERG protein expression intracellularly and in the plasma membrane. However, the increased channel abundance is not accompanied by an increase in hERG current (I hERG). Our data reveal that acute (30 minute) PKC activation inhibits I hERG, and this effect is dependent on the N-terminus of the channel. Upon truncation of hERG's N-terminus, chronic activation of PKC increases both hERG protein expression and current. The PKC-mediated increase in hERG expression is partially due to inhibition of the E3 ubiquitin ligase Nedd4-2, which mediates degradation of hERG channels. Our findings demonstrate that PKC activation regulates hERG in a balanced manner, altering both hERG current and expression.
Cryo-EM represents a unique and powerful opportunity to structurally characterize biomolecules at the singleparticle level, and to draw biological insights from the heterogeneity observed within structural ensembles. Doing so, however, represents a significant computational challenge, and necessitates improved methods for studying extremely heterogeneous datasets. Here, we present an approach that combines our recently-published cryoDRGN method to reconstruct highly heterogeneous structural ensembles with a high-throughput compositional analysis that allows us to quantify the presence and absence of individual domains or whole proteins across hundreds-tothousands of cryo-EM density maps. This analysis produces a highly interpretable representation of the compositional heterogeneity present within a dataset. Using this representation, we can identify cooperative and mutually-exclusive occupancy relationships between various subunits, extract subsets of particles for traditional high-resolution refinement, and define pathways of structural change including complex assembly. We have applied this approach to understand the role of a universally-conserved methyltransferase in biogenesis of the 30S ribosomal subunit. By comparing the structural ensembles observed in the presence and absence of this factor, we have uncovered that this factor performs a novel proof-reading role in ribosome assembly. In sum, this work establishes a framework for systematically interrogating compositionally heterogeneous structural ensembles produced by tools such as cryoDRGN, and it highlights the value of this framework in illuminating underlying biological mechanisms.
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