The resolution revolution in X-ray diffraction, Cryo-EM and other Technologies

New insights into GATOR2-dependent interactions and its conformational changes in amino acid sensing

Eukaryotic cells coordinate growth under different environmental conditions via mechanistic target of rapamycin complex 1 (mTORC1). In the amino-acid-sensing signalling pathway, the GATOR2 complex, containing five evolutionarily conserved subunits (WDR59, Mios, WDR24, Seh1L and Sec13), is required to regulate mTORC1 activity by interacting with upstream CASTOR1 (arginine sensor) and Sestrin2 (leucine sensor and downstream GATOR1 complex). GATOR2 complex utilizes β-propellers to engage with CASTOR1, Sestrin2 and GATOR1, removal of these β-propellers results in substantial loss of mTORC1 capacity. However, structural information regarding the interface between amino acid sensors and GATOR2 remains elusive. With the recent progress of the AI-based tool AlphaFold2 (AF2) for protein structure prediction, structural models were predicted for Sentrin2-WDR24-Seh1L and CASTOR1-Mios β-propeller. Furthermore, the effectiveness of relevant residues within the interface was examined using biochemical experiments combined with molecular dynamics (MD) simulations. Notably, fluorescence resonance energy transfer (FRET) analysis detected the structural transition of GATOR2 in response to amino acid signals, and the deletion of Mios β-propeller severely impeded that change at distinct arginine levels. These findings provide structural perspectives on the association between GATOR2 and amino acid sensors and can facilitate future research on structure determination and function.

Structural biology of SARS-CoV-2

This chapter provides a comprehensive overview of the techniques employed to unravel the structural biology of SARS-CoV-2, facilitating a deeper understanding of the virus for developing future therapeutic strategies. Various techniques such as Electron microscopy (EM) for capturing high-resolution images of the virus and X-ray crystallography used for determining atomic-level structures of viral proteins are discussed. Cryo-electron microscopy (cryo-EM) imaging is also examined as a powerful tool for visualizing the virus's structure in its native state. Intracellular detection and tracking of SARS-CoV-2 are discussed, highlighting the techniques employed to study the virus's behavior within host cells. The chapter further explores how cryo-EM has been instrumental in delivering high-quality structural information on SARS-CoV-2, enabling researchers to better understand its mechanisms of infection and replication. The structural visualization of SARS-CoV-2 is then presented, focusing on key components such as the spike protein structure, RNA polymerase structure, and the visualization of intact and in-situ virions using cryo-electron tomography (cryo-ET). Lastly, the chapter touches upon the application of nuclear magnetic resonance (NMR) spectroscopy for studying the dynamics and interactions of viral proteins.

Accurate prediction of protein assembly structure by combining AlphaFold and symmetrical docking

AlphaFold can predict the structures of monomeric and multimeric proteins with high accuracy but has a limit on the number of chains and residues it can fold. Here we show that a combination of AlphaFold and all-atom symmetric docking simulations enables highly accurate prediction of the structure of complex symmetrical assemblies. We present a method to predict the structure of complexes with cubic - tetrahedral, octahedral and icosahedral - symmetry from sequence. Focusing on proteins where AlphaFold can make confident predictions on the subunit structure, 27 cubic systems were assembled with a median TM-score of 0.99 and a DockQ score of 0.72. 21 had TM-scores of above 0.9 and were categorized as acceptable- to high-quality according to DockQ. The resulting models are energetically optimized and can be used for detailed studies of intermolecular interactions in higher-order symmetrical assemblies. The results demonstrate how explicit treatment of structural symmetry can significantly expand the size and complexity of AlphaFold predictions.

Predicting virus Fitness: Towards a structure-based computational model

Predicting the impact of new emerging virus mutations is of major interest in surveillance and for understanding the evolutionary forces of the pathogens. The SARS-CoV-2 surface spike-protein (S-protein) binds to human ACE2 receptors as a critical step in host cell infection. At the same time, S-protein binding to human antibodies neutralizes the virus and prevents interaction with ACE2. Here we combine these two binding properties in a simple virus fitness model, using structure-based computation of all possible mutation effects averaged over 10 ACE2 complexes and 10 antibody complexes of the S-protein (∼380,000 computed mutations), and validated the approach against diverse experimental binding/escape data of ACE2 and antibodies. The ACE2-antibody selectivity change caused by mutation (i.e., the differential change in binding to ACE2 vs. immunity-inducing antibodies) is proposed to be a key metric of fitness model, enabling systematic error cancelation when evaluated. In this model, new mutations become fixated if they increase the selective binding to ACE2 relative to circulating antibodies, assuming that both are present in the host in a competitive binding situation. We use this model to categorize viral mutations that may best reach ACE2 before being captured by antibodies. Our model may aid the understanding of variant-specific vaccines and molecular mechanisms of viral evolution in the context of a human host.

Evolution of Sequence and Structure of SARS-CoV-2 Spike Protein: A Dynamic Perspective

Novel coronavirus (SARS-CoV-2) enters its host cell through a surface spike protein. The viral spike protein has undergone several modifications/mutations at the genomic level, through which it modulated its structure-function and passed through several variants of concern. Recent advances in high-resolution structure determination and multiscale imaging techniques, cost-effective next-generation sequencing, and development of new computational methods (including information theory, statistical methods, machine learning, and many other artificial intelligence-based techniques) have hugely contributed to the characterization of sequence, structure, function of spike proteins, and its different variants to understand viral pathogenesis, evolutions, and transmission. Laying on the foundation of the sequence-structure-function paradigm, this review summarizes not only the important findings on structure/function but also the structural dynamics of different spike components, highlighting the effects of mutations on them. As dynamic fluctuations of three-dimensional spike structure often provide important clues for functional modulation, quantifying time-dependent fluctuations of mutational events over spike structure and its genetic/amino acidic sequence helps identify alarming functional transitions having implications for enhanced fusogenicity and pathogenicity of the virus. Although these dynamic events are more difficult to capture than quantifying a static, average property, this review encompasses those challenging aspects of characterizing the evolutionary dynamics of spike sequence and structure and their implications for functions.

Auto-DRRAFTER: Automated RNA Modeling Based on Cryo-EM Density

RNA three-dimensional structures provide rich and vital information for understanding their functions. Recent advances in cryogenic electron microscopy (cryo-EM) allow structure determination of RNAs and ribonucleoprotein (RNP) complexes. However, limited global and local resolutions of RNA cryo-EM maps pose great challenges in tracing RNA coordinates. The Rosetta-based "auto-DRRAFTER" method builds RNA models into moderate-resolution RNA cryo-EM density as part of the Ribosolve pipeline. Here, we describe a step-by-step protocol for auto-DRRAFTER using a glycine riboswitch from Fusobacterium nucleatum as an example. Successful implementation of this protocol allows automated RNA modeling into RNA cryo-EM density, accelerating our understanding of RNA structure-function relationships. Input and output files are being made available at https://github.com/auto-DRRAFTER/springer-chapter .

Stability and expression of SARS-CoV-2 spike-protein mutations

Protein fold stability likely plays a role in SARS-CoV-2 S-protein evolution, together with ACE2 binding and antibody evasion. While few thermodynamic stability data are available for S-protein mutants, many systematic experimental data exist for their expression. In this paper, we explore whether such expression levels relate to the thermodynamic stability of the mutants. We studied mutation-induced SARS-CoV-2 S-protein fold stability, as computed by three very distinct methods and eight different protein structures to account for method- and structure-dependencies. For all methods and structures used (24 comparisons), computed stability changes correlate significantly (99% confidence level) with experimental yeast expression from the literature, such that higher expression is associated with relatively higher fold stability. Also significant, albeit weaker, correlations were seen between stability and ACE2 binding effects. The effect of thermodynamic fold stability may be direct or a correlate of amino acid or site properties, notably the solvent exposure of the site. Correlation between computed stability and experimental expression and ACE2 binding suggests that functional properties of the SARS-CoV-2 S-protein mutant space are largely determined by a few simple features, due to underlying correlations. Our study lends promise to the development of computational tools that may ideally aid in understanding and predicting SARS-CoV-2 S-protein evolution.

Structural heterogeneity and precision of implications drawn from cryo-electron microscopy structures: SARS-CoV-2 spike-protein mutations as a test case

Protein structures may be used to draw functional implications at the residue level, but how sensitive are these implications to the exact structure used? Calculation of the effects of SARS-CoV-2 S-protein mutations based on experimental cryo-electron microscopy structures have been abundant during the pandemic. To understand the precision of such estimates, we studied three distinct methods to estimate stability changes for all possible mutations in 23 different S-protein structures (3.69 million ΔΔG values in total) and explored how random and systematic errors can be remedied by structure-averaged mutation group comparisons. We show that computational estimates have low precision, due to method and structure heterogeneity making results for single mutations uninformative. However, structure-averaged differences in mean effects for groups of substitutions can yield significant results. Illustrating this protocol, functionally important natural mutations, despite individual variations, average to a smaller stability impact compared to other possible mutations, independent of conformational state (open, closed). In summary, we document substantial issues with precision in structure-based protein modeling and recommend sensitivity tests to quantify these effects, but also suggest partial solutions to the problem in the form of structure-averaged “ensemble” estimates for groups of residues when multiple structures are available.

Genomics-based strategies toward the identification of a Z-ISO carotenoid biosynthetic enzyme suitable for structural studies

Over the past 20years, structural genomics efforts have proven enormously successful for the determination of integral membrane protein structures, particularly for those of prokaryotic origin. However, traditional genomic expansion screens have included up to hundreds of targets, necessitating the use of robotics and other automation not available to most laboratories. Moreover, such large-scale screens of eukaryotic targets are not easily performed at such a scale. To have broader appeal, traditional structural genomic approaches need to be modified and improved such that they are feasible for most laboratories and especially so for proteins from eukaryotic organisms. One such refinement, termed "microgenomic expansion," has been recently described. This approach improves the process of target selection by making target screening a two-step process, with a minimal number of targets tested at each step. Microgenomic expansion methods are applied here theoretically to a project that has the objective of acquiring a structure for the plant 15-cis-ζ-carotene isomerase, Z-ISO.

Structural validation and assessment of AlphaFold2 predictions for centrosomal and centriolar proteins and their complexes

Obtaining the high-resolution structures of proteins and their complexes is a crucial aspect of understanding the mechanisms of life. Experimental structure determination methods are time-consuming, expensive and cannot keep pace with the growing number of protein sequences available through genomic DNA sequencing. Thus, the ability to accurately predict the structure of proteins from their sequence is a holy grail of structural and computational biology that would remove a bottleneck in our efforts to understand as well as rationally engineer living systems. Recent advances in protein structure prediction, in particular the breakthrough with the AI-based tool AlphaFold2 (AF2), hold promise for achieving this goal, but the practical utility of AF2 remains to be explored. Focusing on proteins with essential roles in centrosome and centriole biogenesis, we demonstrate the quality and usability of the AF2 prediction models and we show that they can provide important insights into the modular organization of two key players in this process, CEP192 and CEP44. Furthermore, we used the AF2 algorithm to elucidate and then experimentally validate previously unknown prime features in the structure of TTBK2 bound to CEP164, as well as the Chibby1-FAM92A complex for which no structural information was available to date. These findings have important implications in understanding the regulation and function of these complexes. Finally, we also discuss some practical limitations of AF2 and anticipate the implications for future research approaches in the centriole/centrosome field.

Using experimental data, the authors assess the quality and discuss the limitations of AlphaFold2 predictions of protein structures and protein-protein interactions essential for centrosome and centriole biogenesis.

Structure and Mutations of SARS-CoV-2 Spike Protein: A Focused Overview

The spike protein (S-protein) of SARS-CoV-2, the protein that enables the virus to infect human cells, is the basis for many vaccines and a hotspot of concerning virus evolution. Here, we discuss the outstanding progress in structural characterization of the S-protein and how these structures facilitate analysis of virus function and evolution. We emphasize the differences in reported structures and that analysis of structure-function relationships is sensitive to the structure used. We show that the average residue solvent exposure in nearly complete structures is a good descriptor of open vs closed conformation states. Because of structural heterogeneity of functionally important surface-exposed residues, we recommend using averages of a group of high-quality protein structures rather than a single structure before reaching conclusions on specific structure-function relationships. To illustrate these points, we analyze some significant chemical tendencies of prominent S-protein mutations in the context of the available structures. In the discussion of new variants, we emphasize the selectivity of binding to ACE2 vs prominent antibodies rather than simply the antibody escape or ACE2 affinity separately. We note that larger chemical changes, in particular increased electrostatic charge or side-chain volume of exposed surface residues, are recurring in mutations of concern, plausibly related to adaptation to the negative surface potential of human ACE2. We also find indications that the fixated mutations of the S-protein in the main variants are less destabilizing than would be expected on average, possibly pointing toward a selection pressure on the S-protein. The richness of available structures for all of these situations provides an enormously valuable basis for future research into these structure-function relationships.

On the Regulation of Mitosis by the Kinetochore, a Macromolecular Complex and Organising Hub of Eukaryotic Organisms

The kinetochore is the multiprotein complex of eukaryotic organisms that is assembled on mitotic or meiotic centromeres to connect centromeric DNA with microtubules. Its function involves the coordinated action of more than 100 different proteins. The kinetochore acts as an organiser hub that establishes physical connections with microtubules and centromere-associated proteins and recruits central protein components of the spindle assembly checkpoint (SAC), an evolutionarily conserved surveillance mechanism of eukaryotic organisms that detects unattached kinetochores and destabilises incorrect kinetochore-microtubule attachments. The molecular communication between the kinetochore and the SAC is highly dynamic and tightly regulated to ensure that cells can progress towards anaphase until each chromosome is properly bi-oriented on the mitotic spindle. This is achieved through an interplay of highly cooperative interactions and concerted phosphorylation/dephosphorylation events that are organised in time and space.This contribution discusses our current understanding of the function, structure and regulation of the kinetochore, in particular, how its communication with the SAC results in the amplification of specific signals to exquisitely control the eukaryotic cell cycle. This contribution also addresses recent advances in machine learning approaches, cell imaging and proteomics techniques that have enhanced our understanding of the molecular mechanisms that ensure the high fidelity and timely segregation of the genetic material every time a cell divides as well as the current challenges in the study of this fascinating molecular machine.

Imaging and visualizing SARS-CoV-2 in a new era for structural biology

The SARS-CoV-2 pandemic has had a global impact and has put scientific endeavour in the spotlight, perhaps more than any previous viral outbreak. Fortuitously, the pandemic came at a time when decades of research in multiple scientific fields could be rapidly brought to bear, and a new generation of vaccine platforms was on the cusp of clinical maturity. SARS-CoV-2 also emerged at the inflection point of a technological revolution in macromolecular imaging by cryo-electron microscopy, fuelled by a confluence of major technological advances in sample preparation, optics, detectors and image processing software, that complemented pre-existing techniques. Together, these advances enabled us to visualize SARS-CoV-2 and its components more rapidly, in greater detail, and in a wider variety of biologically relevant contexts than would have been possible even a few years earlier. The resulting ultrastructural information on SARS-CoV-2 and how it interacts with the host cell has played a critical role in the much-needed accelerated development of COVID-19 vaccines and therapeutics. Here, we review key imaging modalities used to visualize SARS-CoV-2 and present select example data, which have provided us with an exceptionally detailed picture of this virus.

The first resolution revolution in protein structure analysis: X-ray diffraction of polypeptide conformations and globular protein folds in 1950s and 1960s

Although determination of structures of biological molecules became a real possibility after the first X-ray analyses of crystals by the William Henry Bragg and his son Lawrence in 1913, the crystal structure determination of globular proteins became a possibility only in 1934 with the demonstration of X-ray diffraction from pepsin by J D Bernal and Dorothy Crowfoot, later Hodgkin, who had realised the importance of maintaining an aqueous environment for proteins in crystals. After a further 20 years of hard work by Max Perutz, John Kendrew and others the structures of haemoglobin and myoglobin emerged. Further innovation resulted in a revolution in X-ray diffraction studies in the 1960s, which focused first on polypeptides with alpha helix, beta strand and collagen polyproline helix structures, described in a review by David Davies in 1965 in the journal Progress in Biophysics, later to become Progress in Biophysics and Molecular Biology. It was followed in 1969 by a further detailed review by Tony North and David Phillips in the same journal on crystal structure a nalyses of globular proteins that successfully emerged soon after that of myoglobin. These included the structure of the first enzyme, lysozyme, followed by structures of chymotrypsin, trypsin, carboxypeptidase and many others. This first resolution revolution in X-ray analysis described in the two reviews is the subject of this retrospective analysis just over five decades later.