Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy

Abstract

Biological macromolecules and assemblies precisely rearrange their atomic 3D structures to execute cellular functions. Understanding the mechanisms by which these molecular machines operate requires insight into the ensemble of structural states they occupy during the functional cycle. Single-particle cryo-electron microscopy (cryo-EM) has become the preferred method to provide near-atomic resolution, structural information about dynamic biological macromolecules elusive to other structure determination methods. Recent advances in cryo-EM methodology have allowed structural biologists not only to probe the structural intermediates of biochemical reactions, but also to resolve different compositional and conformational states present within the same dataset. This article reviews newly developed sample preparation and single-particle analysis (SPA) techniques for high-resolution structure determination of intrinsically dynamic and heterogeneous samples, shedding light upon the intricate mechanisms employed by molecular machines and helping to guide drug discovery efforts.

Cryo-EM reveals the molecular basis of laminin polymerization and LN-lamininopathies

“…3c). R234W and R234Q are frequent mutations producing severe phenotypes 16 . R234W leads to a significant decrease of Lm expression, while R234Q reduces extracellular Lm secretion.…”

Novel Artificial Intelligence-Based Approaches for Ab Initio Structure Determination and Atomic Model Building for Cryo-Electron Microscopy

“…Particle assignment, however, requires the calculation of the particle pose parameters (three Eulerian angles and two in-plane translations) that are not provided by the experimental images. This process is further complicated by several inherent factors of cryo-EM data: (1) artifacts are often present in samples and can be introduced during multiple stages of the EM workflow; (2) particle images have a very low SNR; (3) samples often have molecules of varying compositional and conformational states, and thus datasets contain 2D projections that correspond to multiple 3D structures (reviewed in [ 1 ]). Many existing 3D reconstruction algorithms use a low-resolution, initial map representing the best estimate of the target protein to assign projection directions to the experimental projections [ 73 ].…”

“…Signal contributed by noise may manifest as random fluctuations or distortions in the map that can interfere with the optimization of the measured fit between the model and the Coulomb map [ 62 ]. Additionally, there is currently no infrastructure to adjust models of flexible macromolecules that undergo continuous conformational changes [ 1 ]. The recent integration of AI-based approaches to the de novo model building toolbox has the potential to significantly alleviate these limitations.…”

“…Cryo-EM and single particle analysis (SPA) have become the preferred method for structure determination of biological complexes at near-atomic resolution [ 1 , 2 , 3 , 4 , 5 , 6 ]. Such level of detail not only provides crucial insight into molecular mechanisms employed by biological macromolecules, but also may facilitate the design and development of new drugs and therapeutics.…”

Machine learning approaches to cryoEM density modification differentially affect biomacromolecule and ligand density quality

“…Over the past decade, cryogenic electron microscopy (cryoEM) has matured into a leading method for determining the three-dimensional (3-D) structures of dynamic macromolecular complexes ( DiIorio and Kulczyk, 2022 ). Reductions in the cost of GPU-accelerated computing coupled with improvements in both microscope and detector hardware have facilitated the acquisition of high-quality, high-resolution cryoEM data ( Nakane et al, 2020 ; Yip et al, 2020 ; Fréchin et al, 2023 ).…”