Rapid structural analysis of bacterial ribosomes in situ.

Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe the development of a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days, and we expect this workflow will be widely applicable to related bacterial samples.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

Cryo-electron tomography (cryo-ET) enables observation of macromolecular complexes in their native, spatially contextualized cellular environment. Cryo-ET processing software to visualize such complexes at nanometer resolution via iterative alignment and averaging are well developed but rely upon assumptions of structural homogeneity among the complexes of interest. Recently developed tools allow for some assessment of structural diversity but have limited capacity to represent highly heterogeneous structures, including those undergoing continuous conformational changes. Here we extend the highly expressive cryoDRGN (Deep Reconstructing Generative Networks) deep learning architecture, originally created for single-particle cryo-electron microscopy analysis, to cryo-ET. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct heterogeneous structural ensembles supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET. We additionally illustrate tomoDRGN's efficacy in analyzing diverse datasets, using it to reveal high-level organization of human immunodeficiency virus (HIV) capsid complexes assembled in virus-like particles and to resolve extensive structural heterogeneity among ribosomes imaged in situ.

Imaging structurally dynamic ribosomes with cryogenic electron microscopy.

Throughout the history of electron microscopy, ribosomes have served as an ideal subject for imaging and technological development, which in turn has driven our understanding of ribosomal biology. Here, we provide a historical perspective at the intersection of electron microscopy technology development and ribosome biology and reflect on how this technique has shed light on each stage of the life cycle of this dynamic macromolecular machine. With an emphasis on prokaryotic systems, we specifically describe how pairing cryo-EM with clever experimental design, time-resolved techniques, and next-generation heterogeneous structural analysis has afforded insights into the modular nature of assembly, the roles of the many transient biogenesis and translation co-factors, and the subtle variations in structure and function between strains and species. The work concludes with a prospective outlook on the field, highlighting the pivotal role cryogenic electron tomography is playing in adding cellular context to our understanding of ribosomal life cycles, and noting how this exciting technology promises to bridge the gap between cellular and structural biology.

Learning structural heterogeneity from cryo-electron sub-tomograms with tomoDRGN.

Cryo-electron tomography (cryo-ET) allows one to observe macromolecular complexes in their native, spatially contextualized environment. Tools to visualize such complexes at nanometer resolution via iterative alignment and averaging are well-developed but rely on assumptions of structural homogeneity among the complexes under consideration. Recently developed downstream analysis tools allow for some assessment of macromolecular diversity but have limited capacity to represent highly heterogeneous macromolecules, including those undergoing continuous conformational changes. Here, we extend the highly expressive cryoDRGN deep learning architecture, originally created for cryo-electron microscopy single particle analysis, to sub-tomograms. Our new tool, tomoDRGN, learns a continuous low-dimensional representation of structural heterogeneity in cryo-ET datasets while also learning to reconstruct a large, heterogeneous ensemble of structures supported by the underlying data. Using simulated and experimental data, we describe and benchmark architectural choices within tomoDRGN that are uniquely necessitated and enabled by cryo-ET data. We additionally illustrate tomoDRGN's efficacy in analyzing an exemplar dataset, using it to reveal extensive structural heterogeneity among ribosomes imaged in situ.

Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN.

Single-particle cryogenic electron microscopy (cryo-EM) has emerged as a powerful technique to visualize the structural landscape sampled by a protein complex. However, algorithmic and computational bottlenecks in analyzing heterogeneous cryo-EM datasets have prevented the full realization of this potential. 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 is effective in modeling 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 the analysis of an existing assembling 50S ribosome dataset, 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 navigating Python and Jupyter notebooks. It requires 3-4 days to complete. CryoDRGN is open source software that is freely available.

Biophysical and X-ray structural studies of the (GGGTT)3GGG G-quadruplex in complex with N-methyl mesoporphyrin IX.

The G-quadruplex (GQ) is a well-studied non-canonical DNA structure formed by G-rich sequences found at telomeres and gene promoters. Biological studies suggest that GQs may play roles in regulating gene expression, DNA replication, and DNA repair. Small molecule ligands were shown to alter GQ structure and stability and thereby serve as novel therapies, particularly against cancer. In this work, we investigate the interaction of a G-rich sequence, 5'-GGGTTGGGTTGGGTTGGG-3' (T1), with a water-soluble porphyrin, N-methyl mesoporphyrin IX (NMM) via biophysical and X-ray crystallographic studies. UV-vis and fluorescence titrations, as well as a Job plot, revealed a 1:1 binding stoichiometry with an impressively tight binding constant of 30-50 µM-1 and ΔG298 of -10.3 kcal/mol. Eight extended variants of T1 (named T2 -T9) were fully characterized and T7 was identified as a suitable candidate for crystallographic studies. We solved the crystal structures of the T1- and T7-NMM complexes at 2.39 and 2.34 Å resolution, respectively. Both complexes form a 5'-5' dimer of parallel GQs capped by NMM at the 3' G-quartet, supporting the 1:1 binding stoichiometry. Our work provides invaluable details about GQ-ligand binding interactions and informs the design of novel anticancer drugs that selectively recognize specific GQs and modulate their stability for therapeutic purposes.

Lowering the overall charge on TMPyP4 improves its selectivity for G-quadruplex DNA.

Ligands that stabilize non-canonical DNA structures called G-quadruplexes (GQs) might have applications in medicine as anti-cancer agents, due to the involvement of GQ DNA in a variety of cancer-related biological processes. Five derivatives of 5,10,15,20-tetrakis(N-methyl-4-pyridyl)porphyrin (TMPyP4), where a N-methylpyridyl group was replaced with phenyl (4P3), 4-aminophenyl (PN3M), 4-phenylamidoproline (PL3M), or 4-carboxyphenyl (PC3M and P2C2M) were investigated for their interactions with human telomeric DNA (Tel22) using fluorescence resonance energy transfer (FRET) assay, and UV-visible and circular dichroism spectroscopies in K+ buffer. The molecules are cationic or zwitterionic with an overall charge of 3+ (4P3, PN3M, and PL3M), 2+ (PC3M) or neutral (P2C2M). All porphyrins except P2C2M stabilize human telomeric DNA in FRET assays by ∼20 °C at 5 eq CD melting experiments suggest that 4P3 is the most stabilizing ligand with a stabilization temperature of 16.8 °C at 4 eq. Importantly, 4P3, PC3M and PL3M demonstrate excellent selectivity for quadruplexes, far superior to that of TMPyP4. Binding constants, determined using UV-vis titrations, correlate with charge: triply cationic 4P3, PN3M and PL3M display Ka of 5-9 µM-1, doubly cationic PC3M displays Ka of 1 µM-1, and neutral P2C2M displays weak-to-no binding. UV-vis data suggest that binding interactions are driven by electrostatic attractions and that the binding mode may be base-stacking (or end-stacking) judging by the high values of red shift (15-20 nm) and hypochromicity (40-50%). We conclude that lowering the charge on TMPyP4 to 3+ can achieve the desired balance between stabilizing ability, affinity, and high selectivity required for an excellent quadruplex ligand.