Single-particle analysis by Cryo-electron microscopy (CryoEM) provides direct access to the conformations of macromolecules. Traditional methods assume discrete conformations, while newer algorithms estimate conformational landscapes representing the different structural states a biomolecule explores. This work presents HetSIREN, a deep learning-based method that can fully reconstruct or refine a CryoEM volume in real space based on the structural information summarized in a conformational latent space. HetSIREN is defined as an accurate space-based method that allows spatially focused analysis and the introduction of sinusoidal hypernetworks with proven high analytics capacities. Continuing with innovations, HetSIREN can also refine the images' pose while conditioning the network with additional constraints to yield cleaner high-quality volumes, as well as addressing one of the most confusing issues in heterogeneity analysis, as it is the fact that structural heterogeneity estimations are entangled with pose estimation (and to a lesser extent with CTF estimation) thanks to its decoupling architecture.

ProDy, an application programming interface (API) composed of multiple modules for studying the dynamics of proteins and their complexes under physiological conditions, has found wide usage in recent years, as a powerful tool for efficiently dissecting the mode spectra of biomolecular systems, including that of large multimers and assemblies. ProDy calculations are based on elastic network model representation of the structure. As such, they provide a rigorous account of the effects of contact topology, which in practice represents the contribution of entropic effects to the predicted conformational mechanics.

We introduce WatFinder, a tool designed to identify and visualize protein-water interactions (water bridges, water-mediated associations, or water channels, fluxes, and clusters) relevant to protein stability, dynamics, and function. WatFinder is integrated into ProDy, a Python API broadly used for structure-based prediction of protein dynamics. WatFinder provides a suite of functions for generating raw data as well as outputs from statistical analyses. The ProDy framework facilitates comprehensive automation and efficient analysis of the ensembles of structures resolved for a given protein or the time-evolved conformations from simulations in explicit water, as illustrated in five case studies presented in the Supplementary Material. Availability and implementation ProDy is open-source and freely available under MIT License from https://github.com/ProDy/ProDy.

There is a variety of experimental and computational techniques available to explore protein dynamics, each presenting advantages and limitations. One promising experimental technique that is driving the development of computational methods is cryo‐electron microscopy (cryo‐EM). Cryo‐EM provides molecular‐level structural data and first estimates of conformational landscape from single particle analysis but cannot track real‐time protein dynamics and may contain uncertainties in atomic positions especially at highly dynamic regions. Molecular simulations offer atomic‐level insights into protein dynamics; however, their computing time requirements limit the conformational sampling accuracy, and it is often hard, to assess by full‐atomic simulations the cooperative movements of biological interest for large assemblies such as those resolved by cryo‐EM. Coarse‐grained (CG) simulations permit us to explore such systems, but at the costs of lower resolution and potentially incomplete sampling of conformational space. On the other hand, analytical methods may circumvent sampling limitations. In particular, elastic network models‐based normal mode analyses (ENM‐NMA) provide unique solutions for the complete mode spectra near equilibrium states, even for systems of megadaltons, and may thus deliver information on mechanisms of motions relevant to biological function. Yet, they lack atomic resolution as well as temporal information for non‐equilibrium systems. Given the complementary nature of these methods, the integration of molecular simulations and ENM‐NMA into hybrid methodologies has gained traction. This review presents the current state‐of‐the‐art in structure‐based computations and how they are helping us gain a deeper understanding of biological mechanisms, with emphasis on the development of hybrid methods accompanying the advances in cryo‐EM.This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics

Scipion is an extensive workflow engine that combines many different software packages into shared pipelines, focusing on single particle cryo-electron microscopy (cryo-EM) image processing and structural biology more generally. Recently, cryo-EM has undergone a continuous heterogeneity revolution, with a host of new methods enabling conformational landscapes to be estimated from individual particle images. We have, therefore, extended Scipion to better apply these methods and connect to computational biophysics.

Macromolecular assemblies, such as protein complexes, undergo continuous structural dynamics, including global reconfigurations critical for their function. Two fast analytical methods are widely used to study these global dynamics, namely elastic network model normal mode analysis and principal component analysis of ensembles of structures. These approaches have found wide use in various computational studies, driving the development of complex pipelines in several software packages. One common theme has been conformational sampling through hybrid simulations incorporating all-atom molecular dynamics and global modes of motion. However, wide functionality is only available for experienced programmers with limited capabilities for other users. We have, therefore, integrated one popular and extensively developed software for such analyses, the ProDy Python application programming interface, into the Scipion workflow engine. This enables a wider range of users to access a complete range of macromolecular dynamics pipelines beyond the core functionalities available in its command-line applications and the normal mode wizard in VMD. The new protocols and pipelines can be further expanded and integrated into larger workflows, together with other software packages for cryo-electron microscopy image analysis and molecular simulations. We present the resulting plugin, Scipion-EM-ProDy, in detail, highlighting the rich functionality made available by its development.

CryoEM has proven to be a very versatile tool to understand the macromolecule heterogeneity of a specimen.Classically, the extraction of this information was reduced to a discrete number of classes.However, due to the highly dynamic nature of macromolecules, the previous assumption limits our comprehension of the structural landscape explored by the macromolecule and captured at particle level.

Understanding how structure and function meet to drive biological processes is progressively shifting the cryoEM field towards a more advanced analysis of macromolecular flexibility. Thanks to techniques such as single-particle analysis and electron tomography, it is possible to image a macromolecule in different states, information that can subsequently be extracted through advanced image-processing methods to build a richer approximation of a conformational landscape. However, the interoperability of all of these algorithms remains a challenging task that is left to users, preventing them from defining a single flexible workflow in which conformational information can be addressed by different algorithms. Therefore, in this work, a new framework integrated into Scipion is proposed called the Flexibility Hub. This framework automatically handles intercommunication between different heterogeneity software, simplifying the task of combining the software into workflows in which the quality and the amount of information extracted from flexibility analysis is maximized.

Image-processing pipelines require the design of complex workflows combining many different steps that bring the raw acquired data to a final result with biological meaning. In the image-processing domain of cryo-electron microscopy single-particle analysis (cryo-EM SPA), hundreds of steps must be performed to obtain the three-dimensional structure of a biological macromolecule by integrating data spread over thousands of micrographs containing millions of copies of allegedly the same macromolecule. The execution of such complicated workflows demands a specific tool to keep track of all these steps performed. Additionally, due to the extremely low signal-to-noise ratio (SNR), the estimation of any image parameter is heavily affected by noise resulting in a significant fraction of incorrect estimates. Although low SNR and processing millions of images by hundreds of sequential steps requiring substantial computational resources are specific to cryo-EM, these characteristics may be shared by other biological imaging domains. Here, we present Scipion, a Python generic open-source workflow engine specifically adapted for image processing. Its main characteristics are: (a) interoperability, (b) smart object model, (c) gluing operations, (d) comparison operations, (e) wide set of domain-specific operations, (f) execution in streaming, (g) smooth integration in high-performance computing environments, (h) execution with and without graphical capabilities, (i) flexible visualization, (j) user authentication and private access to private data, (k) scripting capabilities, (l) high performance, (m) traceability, (n) reproducibility, (o) self-reporting, (p) reusability, (q) extensibility, (r) software updates, and (s) non-restrictive software licensing.

AMPA glutamate receptors (AMPARs), the primary mediators of excitatory neurotransmission in the brain, are either GluA2 subunit-containing and thus Ca 2+ -impermeable, or GluA2-lacking and Ca 2+ -permeable 1 . Despite their prominent expression throughout interneurons and glia, their role in long-term potentiation and their involvement in a range of neuropathologies 2 , structural information for GluA2-lacking receptors is currently absent. Here we determine and characterize cryo-electron microscopy structures of the GluA1 homotetramer, fully occupied with TARPγ3 auxiliary subunits (GluA1/γ3). The gating core of both resting and open-state GluA1/γ3 closely resembles GluA2-containing receptors. However, the sequence-diverse N-terminal domains (NTDs) give rise to a highly mobile assembly, enabling domain swapping and subunit re-alignments in the ligand-binding domain tier that are pronounced in desensitized states. These transitions underlie the unique kinetic properties of GluA1. A GluA2 mutant (F231A) increasing NTD dynamics phenocopies this behaviour, and exhibits reduced synaptic responses, reflecting the anchoring function of the AMPAR NTD at the synapse. Together, this work underscores how the subunit-diverse NTDs determine subunit arrangement, gating properties and ultimately synaptic signalling efficiency among AMPAR subtypes.

The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.

Cryo-electron microscopy (cryoEM) has become a well established technique with the potential to produce structures of large and dynamic supramolecular complexes that are not amenable to traditional approaches for studying structure and dynamics. The size and low resolution of such molecular systems often make structural modelling and molecular dynamics simulations challenging and computationally expensive. This, together with the growing wealth of structural data arising from cryoEM and other structural biology methods, has driven a trend in the computational biophysics community towards the development of new pipelines for analysing global dynamics using coarse-grained models and methods. At the centre of this trend has been a return to elastic network models, normal mode analysis (NMA) and ensemble analyses such as principal component analysis, and the growth of hybrid simulation methodologies that make use of them. Here, this field is reviewed with a focus on ProDy, the Python application programming interface for protein dynamics, which has been developed over the last decade. Two key developments in this area are highlighted: (i) ensemble NMA towards extracting and comparing the signature dynamics of homologous structures, aided by the recent SignDy pipeline, and (ii) pseudoatom fitting for more efficient global dynamics analyses of large and low-resolution supramolecular assemblies from cryoEM, revisited in the CryoDy pipeline. It is believed that such a renewal and extension of old models and methods in new pipelines will be critical for driving the field forward into the next cryoEM revolution.

Emerging evidence suggests that G protein-coupled receptor (GPCR) kinases (GRKs) are associated with the pathophysiology of Alzheimer's disease (AD). However, GRKs have not been directly implicated in regulation of the amyloid-β (Aβ) pathogenic cascade in AD. Here, we determine that GRKs phosphorylate a non-canonical substrate, anterior pharynx-defective 1A (APH1A), an integral component of the γ-secretase complex. Significantly, we show that GRKs generate distinct phosphorylation barcodes in intracellular loop 2 (ICL2) and the C terminus of APH1A, which differentially regulate recruitment of the scaffolding protein β-arrestin 2 (βarr2) to APH1A and γ-secretase-mediated Aβ generation. Further molecular dynamics simulation studies reveal an interaction between the βarr2 finger loop domain and ICL2 and ICL3 of APH1A, similar to a GPCR-β-arrestin complex, which regulates γ-secretase activity. Collectively, these studies provide insight into the molecular and structural determinants of the APH1A-βarr2 interaction that critically regulate Aβ generation.

AMPA-type glutamate receptors (AMPARs) mediate rapid signal transmission at excitatory synapses in the brain. Glutamate binding to the receptor’s ligand-binding domains (LBDs) leads to ion channel activation and desensitization. Gating kinetics shape synaptic transmission and are strongly modulated by transmembrane AMPAR regulatory proteins (TARPs) through currently incompletely resolved mechanisms. Here, electron cryo-microscopy structures of the GluA1/2 TARP-γ8 complex, in both open and desensitized states (at 3.5 Å), reveal state-selective engagement of the LBDs by the large TARP-γ8 loop (‘β1’), elucidating how this TARP stabilizes specific gating states. We further show how TARPs alter channel rectification, by interacting with the pore helix of the selectivity filter. Lastly, we reveal that the Q/R-editing site couples the channel constriction at the filter entrance to the gate, and forms the major cation binding site in the conduction path. Our results provide a mechanistic framework of how TARPs modulate AMPAR gating and conductance.

Cryo-EM is now able to reveal continuous heterogeneity and dynamics with many groups developing methods to analysis this, including ours, which we are making available in Scipion [1], which integrates multiple software packages into pipelines. Simple computational biophysics models and methods such as elastic network models (ENMs) and normal mode analysis (NMA) can efficiently identify key motions of biological relevance, even for large system studied by Cryo-EM [2]. These methods have been demonstrated to be beneficial for Cryo-EM continuous heterogeneity analysis some years ago with the hybrid electron microscopy normal mode analysis (HEMNMA) [3], which has recently been integrated into Scipion through the ContinuousFlex plugin [4], and its extension HEMNMA-3D for tomography [5]. We now extend the capabilities for using such approaches further by integrating the ProDy Python package for protein dynamics [6] into a Scipion plugin with new protocols for atom selection and alignment, analysis of deformation vectors associated with pairwise conformational changes, ENM NMA, construction of structural ensembles and principal component analysis (PCA) of them, and comparison of these different motions derived from theory and experiments. Another approach that is proving successful in our lab, which is being implemented with Xmipp [7] and associated Scipion plugins, makes use of 3D Zernike polynomials to describe conformational changes [8]. This approach can combine information from images, volumes and atomic structures into a common framework, optimising the coefficients of the polynomials to describe transitions between them and create a rough conformational space. Applying these polynomials and coefficients creates new volumes and structures corresponding to particular regions of the space. We can also convert Zernike bases and coefficients to corresponding ones for normal modes and vice versa, providing a more direct connection between Cryo-EM data and physically realistic

The S:A222V point mutation, within the G clade, was characteristic of the 20E (EU1) SARS-CoV-2 variant identified in Spain in early summer 2020. This mutation has since reappeared in the Delta subvariant AY.4.2, raising questions about its specific effect on viral infection. We report combined serological, functional, structural and computational studies characterizing the impact of this mutation. Our results reveal that S:A222V promotes an increased RBD opening and slightly increases ACE2 binding as compared to the parent S:D614G clade. Finally, S:A222V does not reduce sera neutralization capacity, suggesting it does not affect vaccine effectiveness.