Retrieving functional pathways of biomolecules from single-particle snapshots

Roodmus: a toolkit for benchmarking heterogeneous electron cryo-microscopy reconstructions

Conformational heterogeneity of biological macromolecules is a challenge in single-particle averaging (SPA). Current standard practice is to employ classification and filtering methods that may allow a discrete number of conformational states to be reconstructed. However, the conformation space accessible to these molecules is continuous and, therefore, explored incompletely by a small number of discrete classes. Recently developed heterogeneous reconstruction algorithms (HRAs) to analyse continuous heterogeneity rely on machine-learning methods that employ low-dimensional latent space representations. The non-linear nature of many of these methods poses a challenge to their validation and interpretation and to identifying functionally relevant conformational trajectories. These methods would benefit from in-depth benchmarking using high-quality synthetic data and concomitant ground truth information. We present a framework for the simulation and subsequent analysis with respect to the ground truth of cryo-EM micrographs containing particles whose conformational heterogeneity is sourced from molecular dynamics simulations. These synthetic data can be processed as if they were experimental data, allowing aspects of standard SPA workflows as well as heterogeneous reconstruction methods to be compared with known ground truth using available utilities. The simulation and analysis of several such datasets are demonstrated and an initial investigation into HRAs is presented.

DynaMight: estimating molecular motions with improved reconstruction from cryo-EM images

How to deal with continuously flexing molecules is one of the biggest outstanding challenges in single-particle analysis of proteins from cryogenic-electron microscopy (cryo-EM) images. Here, we present DynaMight, a software tool that estimates a continuous space of conformations in a cryo-EM dataset by learning three-dimensional deformations of a Gaussian pseudo-atomic model of a consensus structure for every particle image. Inversion of the learned deformations is then used to obtain an improved reconstruction of the consensus structure. We illustrate the performance of DynaMight for several experimental cryo-EM datasets. We also show how error estimates on the deformations may be obtained by independently training two variational autoencoders on half sets of the cryo-EM data, and how regularization of the three-dimensional deformations through the use of atomic models may lead to important artifacts due to model bias. DynaMight is distributed as free, open-source software, as part of RELION-5.

Structural basis for ryanodine receptor type 2 leak in heart failure and arrhythmogenic disorders

Heart failure, the leading cause of mortality and morbidity in the developed world, is characterized by cardiac ryanodine receptor 2 channels that are hyperphosphorylated, oxidized, and depleted of the stabilizing subunit calstabin-2. This results in a diastolic sarcoplasmic reticulum Ca2+ leak that impairs cardiac contractility and triggers arrhythmias. Genetic mutations in ryanodine receptor 2 can also cause Ca2+ leak, leading to arrhythmias and sudden cardiac death. Here, we solved the cryogenic electron microscopy structures of ryanodine receptor 2 variants linked either to heart failure or inherited sudden cardiac death. All are in the primed state, part way between closed and open. Binding of Rycal drugs to ryanodine receptor 2 channels reverts the primed state back towards the closed state, decreasing Ca2+ leak, improving cardiac function, and preventing arrhythmias. We propose a structural-physiological mechanism whereby the ryanodine receptor 2 channel primed state underlies the arrhythmias in heart failure and arrhythmogenic disorders.

Cryo-EM Heterogeneity Analysis using Regularized Covariance Estimation and Kernel Regression

Proteins and the complexes they form are central to nearly all cellular processes. Their flexibility, expressed through a continuum of states, provides a window into their biological functions. Cryogenic electron microscopy (cryo-EM) is an ideal tool to study these dynamic states as it captures specimens in non-crystalline conditions and enables high-resolution reconstructions. However, analyzing the heterogeneous distributions of conformations from cryo-EM data is challenging. We present RECOVAR, a method for analyzing these distributions based on principal component analysis (PCA) computed using a REgularized COVARiance estimator. RECOVAR is fast, robust, interpretable, expressive, and competitive with the state-of-art neural network methods on heterogeneous cryo-EM datasets. The regularized covariance method efficiently computes a large number of high-resolution principal components that can encode rich heterogeneous distributions of conformations and does so robustly thanks to an automatic regularization scheme. The novel reconstruction method based on adaptive kernel regression resolves conformational states to a higher resolution than all other tested methods on extensive independent benchmarks while remaining highly interpretable. Additionally, we exploit favorable properties of the PCA embedding to estimate the conformational density accurately. This density allows for better interpretability of the latent space by identifying stable states and low free-energy motions. Finally, we present a scheme to navigate the high-dimensional latent space by automatically identifying these low free-energy trajectories. We make the code freely available at https://github.com/ma-gilles/recovar.

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.

CLEAPA: a framework for exploring the conformational landscape of cryo-EM using energy-aware pathfinding algorithm

MOTIVATION

Cryo-electron microscopy (cryo-EM) is a powerful technique for studying macromolecules and holds the potential for identifying kinetically preferred transition sequences between conformational states. Typically, these sequences are explored within two-dimensional energy landscapes. However, due to the complexity of biomolecules, representing conformational changes in two dimensions can be challenging. Recent advancements in reconstruction models have successfully extracted structural heterogeneity from cryo-EM images using higher-dimension latent space. Nonetheless, creating high-dimensional conformational landscapes in the latent space and then searching for preferred paths continues to be a formidable task.

RESULTS

This study introduces an innovative framework for identifying preferred trajectories within high-dimensional conformational landscapes. Our method encompasses the search for the minimum energy path in the graph, where edge weights are determined based on the energy estimation at each node using local density. The effectiveness of this approach is demonstrated by identifying accurate transition states in both synthetic and real-world datasets featuring continuous conformational changes.

AVAILABILITY AND IMPLEMENTATION

The CLEAPA package is available at https://github.com/tengyulin/energy_aware_pathfinding/.

Single-particle Cryo-EM and molecular dynamics simulations: A perfect match

Knowledge of the structure and dynamics of biomolecules is key to understanding the mechanisms underlying their biological functions. Single-particle cryo-electron microscopy (cryo-EM) is a powerful structural biology technique to characterize complex biomolecular systems. Here, we review recent advances of how Molecular Dynamics (MD) simulations are being used to increase and enhance the information extracted from cryo-EM experiments. We will particularly focus on the physics underlying these experiments, how MD facilitates structure refinement, in particular for heterogeneous and non-isotropic resolution, and how thermodynamic and kinetic information can be extracted from cryo-EM data.

Deep generative priors for biomolecular 3D heterogeneous reconstruction from cryo-EM projections

Cryo-electron microscopy has become a powerful tool to determine three-dimensional (3D) structures of rigid biological macromolecules from noisy micrographs with single-particle reconstruction. Recently, deep neural networks, e.g., CryoDRGN, have demonstrated conformational and compositional heterogeneity of complexes. However, the lack of ground-truth conformations poses a challenge to assess the performance of heterogeneity analysis methods. In this work, variational autoencoders (VAE) with three types of deep generative priors were learned for latent variable inference and heterogeneous 3D reconstruction via Bayesian inference. More specifically, VAEs with "Variational Mixture of Posteriors" priors (VampPrior-SPR), non-parametric exemplar-based priors (ExemplarPrior-SPR) and priors from latent score-based generative models (LSGM-SPR) were quantitatively compared with CryoDRGN. We built four simulated datasets composed of hypothetical continuous conformation or discrete states of the hERG K + channel. Empirical and quantitative comparisons of inferred latent representations were performed with affine-transformation-based metrics. These models with more informative priors gave better regularized, interpretable factorized latent representations with better conserved pairwise distances, less deformed latent distributions and lower within-cluster variances. They were also tested on experimental datasets to resolve compositional and conformational heterogeneity (50S ribosome assembly, cowpea chlorotic mottle virus, and pre-catalytic spliceosome) with comparable high resolution. Codes and data are available: https://github.com/benjamin3344/DGP-SPR.

Time-resolved cryo-EM of G-protein activation by a GPCR

G-protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by stimulating guanine nucleotide exchange in the Gα subunit1. To visualize this mechanism, we developed a time-resolved cryo-EM approach that examines the progression of ensembles of pre-steady-state intermediates of a GPCR-G-protein complex. By monitoring the transitions of the stimulatory Gs protein in complex with the β2-adrenergic receptor at short sequential time points after GTP addition, we identified the conformational trajectory underlying G-protein activation and functional dissociation from the receptor. Twenty structures generated from sequential overlapping particle subsets along this trajectory, compared to control structures, provide a high-resolution description of the order of main events driving G-protein activation in response to GTP binding. Structural changes propagate from the nucleotide-binding pocket and extend through the GTPase domain, enacting alterations to Gα switch regions and the α5 helix that weaken the G-protein-receptor interface. Molecular dynamics simulations with late structures in the cryo-EM trajectory support that enhanced ordering of GTP on closure of the α-helical domain against the nucleotide-bound Ras-homology domain correlates with α5 helix destabilization and eventual dissociation of the G protein from the GPCR. These findings also highlight the potential of time-resolved cryo-EM as a tool for mechanistic dissection of GPCR signalling events.

Four-dimensional imaging for cryo-electron microscopy experiments using molecular simulations and manifold learning

Elucidating protein conformational changes is essential because conformational changes are closely related to the functions of proteins. Cryo-electron microscopy (cryo-EM) experiment can be used to reconstruct protein conformational changes via a method that involves using the experimental data (two-dimensional protein images). In this study, a reconstruction method, referred to as the "four-dimensional imaging," was proposed. In our four-dimensional imaging technique, the protein conformational change was obtained using the two-dimensional protein images (the three-dimensional electron density maps used in previously proposed techniques were not used). The protein conformation for each two-dimensional protein image was obtained using our original protocol with molecular dynamics simulations. Using a manifold-learning technique and two-dimensional protein images, the protein conformations were arranged according to the conformational change of the protein. By arranging the protein conformations according to the arrangement of the protein images, four-dimensional imaging is constructed. A simulation for a cryo-EM experiment demonstrated the validity of our four-dimensional imaging technique.

Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features

Advances in cryo-electron microscopy (cryo-EM) imaging technologies have led to a rapidly increasing number of cryo-EM density maps. Alignment and comparison of density maps play a crucial role in interpreting structural information, such as conformational heterogeneity analysis using global alignment and atomic model assembly through local alignment. Here, we present a fast and accurate global and local cryo-EM density map alignment method called CryoAlign, that leverages local density feature descriptors to capture spatial structure similarities. CryoAlign is a feature-based cryo-EM map alignment tool, in which the employment of feature-based architecture enables the rapid establishment of point pair correspondences and robust estimation of alignment parameters. Extensive experimental evaluations demonstrate the superiority of CryoAlign over the existing methods in terms of both alignment accuracy and speed.

Understanding IP3R channels: From structural underpinnings to ligand-dependent conformational landscape

Inositol 1,4,5-trisphosphate receptors (IP3Rs) are ubiquitously expressed large-conductance Ca2+-permeable channels predominantly localized to the endoplasmic reticulum (ER) membranes of virtually all eukaryotic cell types. IP3Rs work as Ca2+ signaling hubs through which diverse extracellular stimuli and intracellular inputs are processed and then integrated to result in delivery of Ca2+ from the ER lumen to generate cytosolic Ca2+ signals with precise temporal and spatial properties. IP3R-mediated Ca2+ signals control a vast repertoire of cellular functions ranging from gene transcription and secretion to the more enigmatic brain activities such as learning and memory. IP3Rs open and release Ca2+ when they bind both IP3 and Ca2+, the primary channel agonists. Despite overwhelming evidence supporting functional interplay between IP3 and Ca2+ in activation and inhibition of IP3Rs, the mechanistic understanding of how IP3R channels convey their gating through the interplay of two primary agonists remains one of the major puzzles in the field. The last decade has seen much progress in the use of cryogenic electron microscopy to elucidate the molecular mechanisms of ligand binding, ion permeation, ion selectivity and gating of the IP3R channels. The results of these studies, summarized in this review, provide a prospective view of what the future holds in structural and functional research of IP3Rs.

Application of monolayer graphene to cryo-electron microscopy grids for high-resolution structure determination

In cryogenic electron microscopy (cryo-EM), purified macromolecules are typically applied to a grid bearing a holey carbon foil, blotted to remove excess liquid and rapidly frozen in a roughly 20-100 nm thick layer of vitreous ice that is suspended across roughly 1 μm-wide foil holes. The resulting sample is then imaged using cryogenic transmission electron microscopy and, after substantial image processing, near-atomic resolution structures can be determined. Despite cryo-EM's widespread adoption, sample preparation remains a severe bottleneck in cryo-EM workflows, with users often encountering challenges related to samples behaving poorly in the suspended vitreous ice. Recently, methods have been developed to modify cryo-EM grids with a single continuous layer of graphene, which acts as a support surface that often increases particle density in the imaged area and can reduce interactions between particles and the air-water interface. Here, we provide detailed protocols for the application of graphene to cryo-EM grids, and for rapidly assessing the relative hydrophilicity of the resulting grids. Additionally, we describe an EM-based method to confirm the presence of graphene by visualizing its characteristic diffraction pattern. Finally, we demonstrate the utility of these graphene supports by rapidly reconstructing a 2.7 Å resolution density map of an exemplar Cas9 complex using a highly pure sample at a relatively low concentration.

Guide for determination of protein structural ensembles by combining cryo-EM data with metadynamics

Metadynamics electron microscopy metaInference (MEMMI) is an integrative structural biology method that enables a rapid and accurate characterization of protein structural dynamics at the atomic level and the error in the cryo-EM experimental data, even in cases where conformations are separated by high energy barriers. It achieves this by incorporating (a) cryo-electron microscopy electron density maps with (b) metadynamic-enhanced-sampling molecular dynamics. Here, I showcase the setup and analysis protocol of MEMMI, used to discover the atomistic structural ensemble and error in the cryo-EM electron density map of the fuzzy coat of IAPP, a fibril implicated in type II diabetes.

Beyond ManifoldEM: geometric relationships between manifold embeddings of a continuum of 3D molecular structures and their 2D projections

ManifoldEM is an established method of geometric machine learning developed to extract information on conformational motions of molecules from their projections obtained by cryogenic electron microscopy (cryo-EM). In a previous work, in-depth analysis of the properties of manifolds obtained for simulated ground-truth data from molecules exhibiting domain motions has led to improvements of this method, as demonstrated in selected applications of single-particle cryo-EM. In the present work this analysis has been extended to investigate the properties of manifolds constructed by embedding data from synthetic models represented by atomic coordinates in motion, or three-dimensional density maps from biophysical experiments other than single-particle cryo-EM, with extensions to cryo-electron tomography and single-particle imaging with a X-ray free-electron laser. Our theoretical analysis revealed interesting relationships between all these manifolds, which can be exploited in future work.

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.

3DFlex: determining structure and motion of flexible proteins from cryo-EM

Modeling flexible macromolecules is one of the foremost challenges in single-particle cryogenic-electron microscopy (cryo-EM), with the potential to illuminate fundamental questions in structural biology. We introduce Three-Dimensional Flexible Refinement (3DFlex), a motion-based neural network model for continuous molecular heterogeneity for cryo-EM data. 3DFlex exploits knowledge that conformational variability of a protein is often the result of physical processes that transport density over space and tend to preserve local geometry. From two-dimensional image data, 3DFlex enables the determination of high-resolution 3D density, and provides an explicit model of a flexible protein's motion over its conformational landscape. Experimentally, for large molecular machines (tri-snRNP spliceosome complex, translocating ribosome) and small flexible proteins (TRPV1 ion channel, αVβ8 integrin, SARS-CoV-2 spike), 3DFlex learns nonrigid molecular motions while resolving details of moving secondary structure elements. 3DFlex can improve 3D density resolution beyond the limits of existing methods because particle images contribute coherent signal over the conformational landscape.

Methods for Cryo-EM Single Particle Reconstruction of Macromolecules Having Continuous Heterogeneity

Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already allow the recovery of a few distinct conformations from heterogeneous single-particle samples, but the treatment of complex forms of heterogeneity such as the continuum of possible transitory states and flexible regions remains largely an open problem. In recent years there has been a surge of new approaches for treating the more general problem of continuous heterogeneity. This paper surveys the current state of the art in this area.

Time-resolved cryo-EM of G protein activation by a GPCR

G protein-coupled receptors (GPCRs) activate heterotrimeric G proteins by stimulating the exchange of guanine nucleotide in the Gα subunit. To visualize this mechanism, we developed a time-resolved cryo-EM approach that examines the progression of ensembles of pre-steady-state intermediates of a GPCR-G protein complex. Using variability analysis to monitor the transitions of the stimulatory Gs protein in complex with the β 2 -adrenergic receptor (β 2 AR) at short sequential time points after GTP addition, we identified the conformational trajectory underlying G protein activation and functional dissociation from the receptor. Twenty transition structures generated from sequential overlapping particle subsets along this trajectory, compared to control structures, provide a high-resolution description of the order of events driving G protein activation upon GTP binding. Structural changes propagate from the nucleotide-binding pocket and extend through the GTPase domain, enacting alterations to Gα Switch regions and the α5 helix that weaken the G protein-receptor interface. Molecular dynamics (MD) simulations with late structures in the cryo-EM trajectory support that enhanced ordering of GTP upon closure of the alpha-helical domain (AHD) against the nucleotide-bound Ras-homology domain (RHD) correlates with irreversible α5 helix destabilization and eventual dissociation of the G protein from the GPCR. These findings also highlight the potential of time-resolved cryo-EM as a tool for mechanistic dissection of GPCR signaling events.

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.

Energy landscapes from cryo-EM snapshots: a benchmarking study

Biomolecules undergo continuous conformational motions, a subset of which are functionally relevant. Understanding, and ultimately controlling biomolecular function are predicated on the ability to map continuous conformational motions, and identify the functionally relevant conformational trajectories. For equilibrium and near-equilibrium processes, function proceeds along minimum-energy pathways on one or more energy landscapes, because higher-energy conformations are only weakly occupied. With the growing interest in identifying functional trajectories, the need for reliable mapping of energy landscapes has become paramount. In response, various data-analytical tools for determining structural variability are emerging. A key question concerns the veracity with which each data-analytical tool can extract functionally relevant conformational trajectories from a collection of single-particle cryo-EM snapshots. Using synthetic data as an independently known ground truth, we benchmark the ability of four leading algorithms to determine biomolecular energy landscapes and identify the functionally relevant conformational paths on these landscapes. Such benchmarking is essential for systematic progress toward atomic-level movies of continuous biomolecular function.

MDSPACE: Extracting Continuous Conformational Landscapes from Cryo-EM Single Particle Datasets Using 3D-to-2D Flexible Fitting based on Molecular Dynamics Simulation

This article presents an original approach for extracting atomic-resolution landscapes of continuous conformational variability of biomolecular complexes from cryo electron microscopy (cryo-EM) single particle images. This approach is based on a new 3D-to-2D flexible fitting method, which uses molecular dynamics (MD) simulation and is embedded in an iterative conformational-landscape refinement scheme. This new approach is referred to as MDSPACE, which stands for Molecular Dynamics simulation for Single Particle Analysis of Continuous Conformational hEterogeneity. The article describes the MDSPACE approach and shows its performance using synthetic and experimental datasets.

Biological function investigated by time-resolved structure determination

Inspired by recent progress in time-resolved x-ray crystallography and the adoption of time-resolution by cryo-electronmicroscopy, this article enumerates several approaches developed to become bigger/smaller, faster, and better to gain new insight into the molecular mechanisms of life. This is illustrated by examples where chemical and physical stimuli spawn biological responses on various length and time-scales, from fractions of Ångströms to micro-meters and from femtoseconds to hours.

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

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.

ContinuousFlex: Software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy and tomography data

ContinuousFlex is a user-friendly open-source software package for analyzing continuous conformational variability of macromolecules in cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET) data. In 2019, ContinuousFlex became available as a plugin for Scipion, an image processing software package extensively used in the cryo-EM field. Currently, ContinuousFlex contains software for running (1) recently published methods HEMNMA-3D, TomoFlow, and NMMD; (2) earlier published methods HEMNMA and StructMap; and (3) methods for simulating cryo-EM and cryo-ET data with conformational variability and methods for data preprocessing. It also includes external software for molecular dynamics simulation (GENESIS) and normal mode analysis (ElNemo), used in some of the mentioned methods. The HEMNMA software has been presented in the past, but not the software of other methods. Besides, ContinuousFlex currently also offers a deep learning extension of HEMNMA, named DeepHEMNMA. In this article, we review these methods in the context of the ContinuousFlex package, developed to facilitate their use by the community.

DeepHEMNMA: ResNet-based hybrid analysis of continuous conformational heterogeneity in cryo-EM single particle images

Single-particle cryo-electron microscopy (cryo-EM) is a technique for biomolecular structure reconstruction from vitrified samples containing many copies of a biomolecular complex (known as single particles) at random unknown 3D orientations and positions. Cryo-EM allows reconstructing multiple conformations of the complexes from images of the same sample, which usually requires many rounds of 2D and 3D classifications to disentangle and interpret the combined conformational, orientational, and translational heterogeneity. The elucidation of different conformations is the key to understand molecular mechanisms behind the biological functions of the complexes and the key to novel drug discovery. Continuous conformational heterogeneity, due to gradual conformational transitions giving raise to many intermediate conformational states of the complexes, is both an obstacle for high-resolution 3D reconstruction of the conformational states and an opportunity to obtain information about multiple coexisting conformational states at once. HEMNMA method, specifically developed for analyzing continuous conformational heterogeneity in cryo-EM, determines the conformation, orientation, and position of the complex in each single particle image by image analysis using normal modes (the motion directions simulated for a given atomic structure or EM map), which in turn allows determining the full conformational space of the complex but at the price of high computational cost. In this article, we present a new method, referred to as DeepHEMNMA, which speeds up HEMNMA by combining it with a residual neural network (ResNet) based deep learning approach. The performance of DeepHEMNMA is shown using synthetic and experimental single particle images.

Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning

The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving the conformational continuum of important biomolecular complexes at the atomic level is essential to understanding their functional mechanisms and guiding structure-based drug discovery. Here, we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions that approximately visualize the conformational space of biomolecular complexes of interest. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of pseudo-energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous datasets, AlphaCryo4D achieved 3D classification accuracy three times those of alternative methods and reconstructed continuous conformational changes of a 130-kDa protein at sub-3 Å resolution. By applying this approach to analyze several experimental datasets of the proteasome, ribosome and spliceosome, we demonstrate its potential generality in exploring hidden conformational space or transient states of macromolecular complexes that remain hitherto invisible. Integration of this approach with time-resolved cryo-EM further allows visualization of conformational continuum in a nonequilibrium regime at the atomic level, thus potentially enabling therapeutic discovery against highly dynamic biomolecular targets.

Point mutations in SARS-CoV-2 variants induce long-range dynamical perturbations in neutralizing antibodies

Monoclonal antibodies are emerging as a viable treatment for the coronavirus disease 19 (COVID-19). However, newly evolved variants of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can reduce the efficacy of currently available antibodies and can diminish vaccine-induced immunity. Here, we demonstrate that the microscopic dynamics of neutralizing monoclonal antibodies can be profoundly modified by the mutations present in the spike proteins of the SARS-COV-2 variants currently circulating in the world population. The dynamical perturbations within the antibody structure, which alter the thermodynamics of antigen recognition, are diverse and can depend both on the nature of the antibody and on the spatial location of the spike mutation. The correlation between the motion of the antibody and that of the spike receptor binding domain (RBD) can also be changed, modulating binding affinity. Using protein-graph-connectivity networks, we delineated the mutant-induced modifications in the information-flow along allosteric pathway throughout the antibody. Changes in the collective dynamics were spatially distributed both locally and across long-range distances within the antibody. On the receptor side, we identified an anchor-like structural element that prevents the detachment of the antibodies; individual mutations there can significantly affect the antibody binding propensity. Our study provides insight into how virus neutralization by monoclonal antibodies can be impacted by local mutations in the epitope via a change in dynamics. This realization adds a new layer of sophistication to the efforts for rational design of monoclonal antibodies against new variants of SARS-CoV2, taking the allostery in the antibody into consideration.

Mutations in the new variants of SARS-CoV-2 spike protein modulates the dynamics of the neutralizing antibodies. Capturing such modulations from MD simulations and graph network model identifies the role of mutations in facilitating immune evasion.

Data-driven determination of number of discrete conformations in single-particle cryo-EM

BACKGROUND AND OBJECTIVE

One of the strengths of single-particle cryo-EM compared to other structural determination techniques is its ability to image heterogeneous samples containing multiple molecular species, different oligomeric states or distinct conformations. This is achieved using routines for in-silico 3D classification that are now well established in the field and have successfully been used to characterize the structural heterogeneity of important biomolecules. These techniques, however, rely on expert-user knowledge and trial-and-error experimentation to determine the correct number of conformations, making it a labor intensive, subjective, and difficult to reproduce procedure.

METHODS

We propose an approach to address the problem of automatically determining the number of discrete conformations present in heterogeneous single-particle cryo-EM datasets. We do this by systematically evaluating all possible partitions of the data and selecting the result that maximizes the average variance of similarities measured between particle images and the corresponding 3D reconstructions.

RESULTS

Using this strategy, we successfully analyzed datasets of heterogeneous protein complexes, including: 1) in-silico mixtures obtained by combining closely related antibody-bound HIV-1 Env trimers and other important membrane channels, and 2) naturally occurring mixtures from diverse and dynamic protein complexes representing varying degrees of structural heterogeneity and conformational plasticity.

CONCLUSIONS

The availability of unsupervised strategies for 3D classification combined with existing approaches for fully automatic pre-processing and 3D refinement, represents an important step towards converting single-particle cryo-EM into a high-throughput technique.

Recovery of Conformational Continuum From Single-Particle Cryo-EM Images: Optimization of ManifoldEM Informed by Ground Truth

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.

Probing Structural Perturbation of Biomolecules by Extracting Cryo-EM Data Heterogeneity

Single-particle cryogenic electron microscopy (cryo-EM) has become an indispensable tool to probe high-resolution structural detail of biomolecules. It enables direct visualization of the biomolecules and opens a possibility for averaging molecular images to reconstruct a three-dimensional Coulomb potential density map. Newly developed algorithms for data analysis allow for the extraction of structural heterogeneity from a massive and low signal-to-noise-ratio (SNR) cryo-EM dataset, expanding our understanding of multiple conformational states, or further implications in dynamics, of the target biomolecule. This review provides an overview that briefly describes the workflow of single-particle cryo-EM, including imaging and data processing, and new methods developed for analyzing the data heterogeneity to understand the structural variability of biomolecules.

The case for data science in experimental chemistry: examples and recommendations

The physical sciences community is increasingly taking advantage of the possibilities offered by modern data science to solve problems in experimental chemistry and potentially to change the way we design, conduct and understand results from experiments. Successfully exploiting these opportunities involves considerable challenges. In this Expert Recommendation, we focus on experimental co-design and its importance to experimental chemistry. We provide examples of how data science is changing the way we conduct experiments, and we outline opportunities for further integration of data science and experimental chemistry to advance these fields. Our recommendations include establishing stronger links between chemists and data scientists; developing chemistry-specific data science methods; integrating algorithms, software and hardware to 'co-design' chemistry experiments from inception; and combining diverse and disparate data sources into a data network for chemistry research.

Energy landscape of the SARS-CoV-2 reveals extensive conformational heterogeneity

Cryo-electron microscopy (cryo-EM) has produced a number of structural models of the SARS-CoV-2 spike, already prompting biomedical outcomes. However, these reported models and their associated electrostatic potential maps represent an unknown admixture of conformations stemming from the underlying energy landscape of the spike protein. As with any protein, some of the spike's conformational motions are expected to be biophysically relevant, but cannot be interpreted only by static models. Using experimental cryo-EM images, we present the energy landscape of the glycosylated spike protein, and identify the diversity of low-energy conformations in the vicinity of its open (so called 1RBD-up) state. The resulting atomic refinement reveal global and local molecular rearrangements that cannot be inferred from an average 1RBD-up cryo-EM model. Here we report varied degrees of "openness" in global conformations of the 1RBD-up state, not revealed in the single-model interpretations of the density maps, together with conformations that overlap with the reported models. We discover how the glycan shield contributes to the stability of these low-energy conformations. Five out of six binding sites we analyzed, including those for engaging ACE2, therapeutic mini-proteins, linoleic acid, two different kinds of antibodies, switch conformations between their known apo- and holo-conformations, even when the global spike conformation is 1RBD-up. This apo-to-holo switching is reminiscent of a conformational preequilibrium. We found only one binding site, namely that of AB-C135 remains in apo state within all the sampled free energy-minimizing models, suggesting an induced fit mechanism for the docking of this antibody to the spike.

The ugly, bad, and good stories of large-scale biomolecular simulations

Molecular modeling of large biomolecular assemblies exemplifies a disruptive area holding both promises and contentions. Propelled by peta and exascale computing, several simulation methodologies have now matured into user-friendly tools that are successfully employed for modeling viruses, membranous nano-constructs, and key pieces of the genetic machinery. We present three unifying biophysical themes that emanate from some of the most recent multi-million atom simulation endeavors. Despite connecting molecular changes with phenotypic outcomes, the quality measures of these simulations remain questionable. We discuss the existing and upcoming strategies for constructing representative ensembles of large systems, how new computing technologies will boost this area, and make a point that integrative modeling guided by experimental data is the future of biomolecular computations.

Annealing synchronizes the 70S ribosome into a minimum-energy conformation

Researchers commonly anneal metals, alloys, and semiconductors to repair defects and improve microstructures via recrystallization. Theoretical studies indicate that simulated annealing on biological macromolecules helps predict the final structures with minimum free energy. Experimental validation of this homogenizing effect and further exploration of its applications are fascinating scientific questions that remain elusive. Here, we chose the apo-state 70S ribosome from Escherichia coli as a model, wherein the 30S subunit undergoes a thermally driven intersubunit rotation and exhibits substantial structural flexibility as well as distinct free energy. We experimentally demonstrate that annealing at a fast cooling rate enhances the 70S ribosome homogeneity and improves local resolution on the 30S subunit. After annealing, the 70S ribosome is in a nonrotated state with respect to corresponding intermediate structures in unannealed or heated ribosomes. Manifold-based analysis further indicates that the annealed 70S ribosome takes a narrow conformational distribution and exhibits a minimum-energy state in the free-energy landscape. Our experimental results offer a facile yet robust approach to enhance protein stability, which is ideal for high-resolution cryogenic electron microscopy. Beyond structure determination, annealing shows great potential for synchronizing proteins on a single-molecule level and can be extended to study protein folding and explore conformational and energy landscapes.

Machine Learning for Structure Determination in Single-Particle Cryo-Electron Microscopy: A Systematic Review

Recently, single-particle cryo-electron microscopy (cryo-EM) has become an indispensable method for determining macromolecular structures at high resolution to deeply explore the relevant molecular mechanism. Its recent breakthrough is mainly because of the rapid advances in hardware and image processing algorithms, especially machine learning. As an essential support of single-particle cryo-EM, machine learning has powered many aspects of structure determination and greatly promoted its development. In this article, we provide a systematic review of the applications of machine learning in this field. Our review begins with a brief introduction of single-particle cryo-EM, followed by the specific tasks and challenges of its image processing. Then, focusing on the workflow of structure determination, we describe relevant machine learning algorithms and applications at different steps, including particle picking, 2-D clustering, 3-D reconstruction, and other steps. As different tasks exhibit distinct characteristics, we introduce the evaluation metrics for each task and summarize their dynamics of technology development. Finally, we discuss the open issues and potential trends in this promising field.

Mapping co-regulatory interactions among ligand-binding sites in ryanodine receptor 1

Ryanodine receptor 1 (RyR1) is an intracellular calcium ion (Ca²⁺ ) release channel required for skeletal muscle contraction. Although cryo-electron microscopy identified binding sites of three coactivators Ca²⁺ , ATP, and caffeine (CFF), the mechanism of co-regulation and synergy of these activators is unknown. Here, we report allosteric connections among the three ligand-binding sites and pore region in (i) Ca²⁺ bound-closed, (ii) ATP/CFF bound-closed, (iii) Ca²⁺ /ATP/CFF bound-closed, and (iv) Ca²⁺ /ATP/CFF bound-open RyR1 states. We identified two dominant networks of interactions that mediate communication between the Ca²⁺ -binding site and pore region in Ca²⁺ bound-closed state, which partially overlapped with the pore communications in ATP/CFF bound-closed RyR1 state. In Ca²⁺ /ATP/CFF bound-closed and -open RyR1 states, co-regulatory interactions were analogous to communications in the Ca²⁺ bound-closed and ATP/CFF bound-closed states. Both ATP- and CFF-binding sites mediate communication between the Ca²⁺ -binding site and the pore region in Ca²⁺ /ATP/CFF bound-open RyR1 structure. We conclude that Ca²⁺ , ATP, and CFF propagate their effects to the pore region through a network of overlapping interactions that mediate allosteric control and molecular synergy in channel regulation.

Magnesium Ions Moderate Calcium-Induced Calcium Release in Cardiac Calcium Release Sites by Binding to Ryanodine Receptor Activation and Inhibition Sites

Ryanodine receptor channels at calcium release sites of cardiac myocytes operate on the principle of calcium-induced calcium release. In vitro experiments revealed competition of Ca2+ and Mg2+ in the activation of ryanodine receptors (RyRs) as well as inhibition of RyRs by Mg2+. The impact of RyR modulation by Mg2+ on calcium release is not well understood due to the technical limitations of in situ experiments. We turned instead to an in silico model of a calcium release site (CRS), based on a homotetrameric model of RyR gating with kinetic parameters determined from in vitro measurements. We inspected changes in the activity of the CRS model in response to a random opening of one of 20 realistically distributed RyRs, arising from Ca2+/Mg2+ interactions at RyR channels. Calcium release events (CREs) were simulated at a range of Mg2+-binding parameters at near-physiological Mg2+ and ATP concentrations. Facilitation of Mg2+ binding to the RyR activation site inhibited the formation of sparks and slowed down their activation. Impeding Mg-binding to the RyR activation site enhanced spark formation and speeded up their activation. Varying Mg2+ binding to the RyR inhibition site also dramatically affected calcium release events. Facilitation of Mg2+ binding to the RyR inhibition site reduced the amplitude, relative occurrence, and the time-to-end of sparks, and vice versa. The characteristics of CREs correlated dose-dependently with the effective coupling strength between RyRs, defined as a function of RyR vicinity, single-channel calcium current, and Mg-binding parameters of the RyR channels. These findings postulate the role of Mg2+ in calcium release as a negative modulator of the coupling strength among RyRs in a CRS, translating to damping of the positive feedback of the calcium-induced calcium-release mechanism.

Challenges and approaches to studying pore-forming proteins

Pore-forming proteins (PFPs) are a broad class of molecules that comprise various families, structural folds, and assembly pathways. In nature, PFPs are most often deployed by their host organisms to defend against other organisms. In humans, this is apparent in the immune system, where several immune effectors possess pore-forming activity. Furthermore, applications of PFPs are found in next-generation low-cost DNA sequencing, agricultural crop protection, pest control, and biosensing. The advent of cryoEM has propelled the field forward. Nevertheless, significant challenges and knowledge-gaps remain. Overcoming these challenges is particularly important for the development of custom, purpose-engineered PFPs with novel or desired properties. Emerging single-molecule techniques and methods are helping to address these unanswered questions. Here we review the current challenges, problems, and approaches to studying PFPs.

Cryo-EM Analyses Permit Visualization of Structural Polymorphism of Biological Macromolecules

The functions of biological macromolecules are often associated with conformational malleability of the structures. This phenomenon of chemically identical molecules with different structures is coined structural polymorphism. Conventionally, structural polymorphism is observed directly by structural determination at the density map level from X-ray crystal diffraction. Although crystallography approach can report the conformation of a macromolecule with the position of each atom accurately defined in it, the exploration of structural polymorphism and interpreting biological function in terms of crystal structures is largely constrained by the crystal packing. An alternative approach to studying the macromolecule of interest in solution is thus desirable. With the advancement of instrumentation and computational methods for image analysis and reconstruction, cryo-electron microscope (cryo-EM) has been transformed to be able to produce “in solution” structures of macromolecules routinely with resolutions comparable to crystallography but without the need of crystals. Since the sample preparation of single-particle cryo-EM allows for all forms co-existing in solution to be simultaneously frozen, the image data contain rich information as to structural polymorphism. The ensemble of structure information can be subsequently disentangled through three-dimensional (3D) classification analyses. In this review, we highlight important examples of protein structural polymorphism in relation to allostery, subunit cooperativity and function plasticity recently revealed by cryo-EM analyses, and review recent developments in 3D classification algorithms including neural network/deep learning approaches that would enable cryo-EM analyese in this regard. Finally, we brief the frontier of cryo-EM structure determination of RNA molecules where resolving the structural polymorphism is at dawn.

Molecular modeling in cardiovascular pharmacology: Current state of the art and perspectives

Molecular modeling in pharmacology is a promising emerging tool for exploring drug interactions with cellular components. Recent advances in molecular simulations, big data analysis, and artificial intelligence (AI) have opened new opportunities for rationalizing drug interactions with their pharmacological targets. Despite the obvious utility and increasing impact of computational approaches, their development is not progressing at the same speed in different fields of pharmacology. Here, we review current in silico techniques used in cardiovascular diseases (CVDs), cardiological drug discovery, and assessment of cardiotoxicity. In silico techniques are paving the way to a new era in cardiovascular medicine, but their use somewhat lags behind that in other fields.

TomoFlow: Analysis of Continuous Conformational Variability of Macromolecules in Cryogenic Subtomograms based on 3D Dense Optical Flow

Cryogenic Electron Tomography (cryo-ET) allows structural and dynamics studies of macromolecules in situ. Averaging different copies of imaged macromolecules is commonly used to obtain their structure at higher resolution and discrete classification to analyze their dynamics. Instrumental and data processing developments are progressively equipping cryo-ET studies with the ability to escape the trap of classification into a complete continuous conformational variability analysis. In this work, we propose TomoFlow, a method for analyzing macromolecular continuous conformational variability in cryo-ET subtomograms based on a three-dimensional dense optical flow (OF) approach. The resultant lower-dimensional conformational space allows generating movies of macromolecular motion and obtaining subtomogram averages by grouping conformationally similar subtomograms. The animations and the subtomogram group averages reveal accurate trajectories of macromolecular motion based on a novel mathematical model that makes use of OF properties. This paper describes TomoFlow with tests on simulated datasets generated using different techniques, namely Normal Mode Analysis and Molecular Dynamics Simulation. It also shows an application of TomoFlow on a dataset of nucleosomes in situ, which provided promising results coherent with previous findings using the same dataset but without imposing any prior knowledge on the analysis of the conformational variability. The method is discussed with its potential uses and limitations.

Few-fs resolution of a photoactive protein traversing a conical intersection

The structural dynamics of a molecule are determined by the underlying potential energy landscape. Conical intersections are funnels connecting otherwise separate potential energy surfaces. Posited almost a century ago¹, conical intersections remain the subject of intense scientific interest^2-5. In biology, they have a pivotal role in vision, photosynthesis and DNA stability⁶. Accurate theoretical methods for examining conical intersections are at present limited to small molecules. Experimental investigations are challenged by the required time resolution and sensitivity. Current structure-dynamical understanding of conical intersections is thus limited to simple molecules with around ten atoms, on timescales of about 100 fs or longer⁷. Spectroscopy can achieve better time resolutions⁸, but provides indirect structural information. Here we present few-femtosecond, atomic-resolution videos of photoactive yellow protein, a 2,000-atom protein, passing through a conical intersection. These videos, extracted from experimental data by machine learning, reveal the dynamical trajectories of de-excitation via a conical intersection, yield the key parameters of the conical intersection controlling the de-excitation process and elucidate the topography of the electronic potential energy surfaces involved.

Conquer by cryo-EM without physically dividing

This mini-review provides an update regarding the substantial progress that has been made in using single-particle cryo-EM to obtain high-resolution structures for proteins and other macromolecules whose particle sizes are smaller than 100 kDa. We point out that establishing the limits of what can be accomplished, both in terms of particle size and attainable resolution, serves as a guide for what might be expected when attempting to improve the resolution of small flexible portions of a larger structure using focused refinement approaches. These approaches, which involve computationally ignoring all but a specific, targeted region of interest on the macromolecules, is known as 'masking and refining,' and it thus is the computational equivalent of the 'divide and conquer' approach that has been used so successfully in X-ray crystallography. The benefit of masked refinement, however, is that one is able to determine structures in their native architectural context, without physically separating them from the biological connections that they require for their function. This mini-review also compares where experimental achievements currently stand relative to various theoretical estimates for the smallest particle size that can be successfully reconstructed to high resolution. Since it is clear that a substantial gap still remains between the two, we briefly recap the areas in which further improvement seems possible, both in equipment and in methods.

Observation of substrate diffusion and ligand binding in enzyme crystals using high-repetition-rate mix-and-inject serial crystallography

Here, we illustrate what happens inside the catalytic cleft of an enzyme when substrate or ligand binds on single-millisecond timescales. The initial phase of the enzymatic cycle is observed with near-atomic resolution using the most advanced X-ray source currently available: the European XFEL (EuXFEL). The high repetition rate of the EuXFEL combined with our mix-and-inject technology enables the initial phase of ceftriaxone binding to the Mycobacterium tuberculosis β-lactamase to be followed using time-resolved crystallography in real time. It is shown how a diffusion coefficient in enzyme crystals can be derived directly from the X-ray data, enabling the determination of ligand and enzyme-ligand concentrations at any position in the crystal volume as a function of time. In addition, the structure of the irreversible inhibitor sulbactam bound to the enzyme at a 66 ms time delay after mixing is described. This demonstrates that the EuXFEL can be used as an important tool for biomedically relevant research.

CryoFold: determining protein structures and data-guided ensembles from cryo-EM density maps

Cryo-electron microscopy (EM) requires molecular modeling to refine structural details from data. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins that cannot be resolved by cryo-EM. Here, we introduce CryoFold - a pipeline of molecular dynamics simulations that determines ensembles of protein structures directly from sequence by integrating density data of varying sparsity at 3-5 Å resolution with coarse-grained topological knowledge of the protein folds. We present six examples showing its broad applicability for folding proteins between 72 to 2000 residues, including large membrane and multi-domain systems, and results from two EMDB competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations, unseen by traditional methods, are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python GUI.

Out-of-Equilibrium Biophysical Chemistry: The Case for Multidimensional, Integrated Single-Molecule Approaches

Out-of-equilibrium processes are ubiquitous across living organisms and all structural hierarchies of life. At the molecular scale, out-of-equilibrium processes (for example, enzyme catalysis, gene regulation, and motor protein functions) cause biological macromolecules to sample an ensemble of conformations over a wide range of time scales. Quantifying and conceptualizing the structure–dynamics to function relationship is challenging because continuously evolving multidimensional energy landscapes are necessary to describe nonequilibrium biological processes in biological macromolecules. In this perspective, we explore the challenges associated with state-of-the-art experimental techniques to understanding biological macromolecular function. We argue that it is time to revisit how we probe and model functional out-of-equilibrium biomolecular dynamics. We suggest that developing integrated single-molecule multiparametric force–fluorescence instruments and using advanced molecular dynamics simulations to study out-of-equilibrium biomolecules will provide a path towards understanding the principles of and mechanisms behind the structure–dynamics to function paradigm in biological macromolecules.

The many faces of RNA-based RNase P, an RNA-world relic

RNase P is an essential enzyme that catalyzes removal of the 5' leader from precursor transfer RNAs. The ribonucleoprotein (RNP) form of RNase P is present in all domains of life and comprises a single catalytic RNA (ribozyme) and a variable number of protein cofactors. Recent cryo-electron microscopy structures of representative archaeal and eukaryotic (nuclear) RNase P holoenzymes bound to tRNA substrate/product provide high-resolution detail on subunit organization, topology, and substrate recognition in these large, multisubunit catalytic RNPs. These structures point to the challenges in understanding how proteins modulate the RNA functional repertoire and how the structure of an ancient RNA-based catalyst was reshaped during evolution by new macromolecular associations that were likely necessitated by functional/regulatory coupling.