Single-Particle Cryo-EM at Crystallographic Resolution

Structural modeling of ion channels using AlphaFold2, RoseTTAFold2, and ESMFold

Ion channels play key roles in human physiology and are important targets in drug discovery. The atomic-scale structures of ion channels provide invaluable insights into a fundamental understanding of the molecular mechanisms of channel gating and modulation. Recent breakthroughs in deep learning-based computational methods, such as AlphaFold, RoseTTAFold, and ESMFold have transformed research in protein structure prediction and design. We review the application of AlphaFold, RoseTTAFold, and ESMFold to structural modeling of ion channels using representative voltage-gated ion channels, including human voltage-gated sodium (NaV) channel - NaV1.8, human voltage-gated calcium (CaV) channel - CaV1.1, and human voltage-gated potassium (KV) channel - KV1.3. We compared AlphaFold, RoseTTAFold, and ESMFold structural models of NaV1.8, CaV1.1, and KV1.3 with corresponding cryo-EM structures to assess details of their similarities and differences. Our findings shed light on the strengths and limitations of the current state-of-the-art deep learning-based computational methods for modeling ion channel structures, offering valuable insights to guide their future applications for ion channel research.

Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences

Cryo-electron microscopy (cryo-EM) technique is widely used for protein structure determination. Current automatic cryo-EM protein complex modeling methods mostly rely on prior chain separation. However, chain separation without sequence guidance often suffers from errors caused by cross-chain interaction or noise densities, which would accumulate and mislead the subsequent steps. Here, we present EModelX, a fully automated cryo-EM protein complex structure modeling method, which achieves sequence-guiding modeling through cross-modal alignments between cryo-EM maps and protein sequences. EModelX first employs multi-task deep learning to predict Cα atoms, backbone atoms, and amino acid types from cryo-EM maps, which is subsequently used to sample Cα traces with amino acid profiles. The profiles are then aligned with protein sequences to obtain initial structural models, which yielded an average RMSD of 1.17 Å in our test set, approaching atomic-level precision in recovering PDB-deposited structures. After filling unmodeled gaps through sequence-guiding Cα threading, the final models achieved an average TM-score of 0.808, outperforming the state-of-the-art method. The further combination with AlphaFold can improve the average TM-score to 0.911. Analyzes conducted by comparing some EModelX-built models and PDB structures highlight its potential to improve PDB structures. EModelX is accessible at https://bio-web1.nscc-gz.cn/app/EModelX .

NDT-C11 as a Viable Novel Detergent for Single Particle Cryo-EM

Single particle cryo electron microscopy (cryo-EM) is now the major method for the determination of integral membrane protein structure. For the success of a given project the type of membrane mimetic used for extraction from the native cell membrane, purification to homogeneity and finally cryo-grid vitrification is crucial. Although small molecule amphiphiles - detergents - are the most widely used membrane mimetic, specific tailoring of detergent structure for single particle cryo-EM is rare and the demand for effective detergents not satisfied. Here, we compare the popular detergent lauryl maltose-neopentyl glycol (LMNG) with the novel detergent neopentyl glycol-derived triglucoside-C11 (NDT-C11) in its behavior as free detergent and when bound to two types of multisubunit membrane protein complexes - cyanobacterial photosystem I (PSI) and mammalian F-ATP synthase. We conclude that NDT-C11 has high potential to become a very useful detergent for single particle cryo-EM of integral membrane proteins.

3dDNAscoreA: A scoring function for evaluation of DNA 3D structures

DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.

CheckMyMetal (CMM): validating metal-binding sites in X-ray and cryo-EM data

Identifying and characterizing metal-binding sites (MBS) within macromolecular structures is imperative for elucidating their biological functions. CheckMyMetal (CMM) is a web based tool that facilitates the interactive validation of MBS in structures determined through X-ray crystallography and cryo-electron microscopy (cryo-EM). Recent updates to CMM have significantly enhanced its capability to efficiently handle large datasets generated from cryo-EM structural analyses. In this study, we address various challenges inherent in validating MBS within both X-ray and cryo-EM structures. Specifically, we examine the difficulties associated with accurately identifying metals and modeling their coordination environments by considering the ongoing reproducibility challenges in structural biology and the critical importance of well annotated, high-quality experimental data. CMM employs a sophisticated framework of rules rooted in the valence bond theory for MBS validation. We explore how CMM validation parameters correlate with the resolution of experimentally derived structures of macromolecules and their complexes. Additionally, we showcase the practical utility of CMM by analyzing a representative cryo-EM structure. Through a comprehensive examination of experimental data, we demonstrate the capability of CMM to advance MBS characterization and identify potential instances of metal misassignment.

Mechanism of allosteric inhibition of human p97/VCP ATPase and its disease mutant by triazole inhibitors

Human p97 ATPase is crucial in various cellular processes, making it a target for inhibitors to treat cancers, neurological, and infectious diseases. Triazole allosteric p97 inhibitors have been demonstrated to match the efficacy of CB-5083, an ATP-competitive inhibitor, in cellular models. However, the mechanism is not well understood. This study systematically investigates the structures of new triazole inhibitors bound to  both wild-type and disease mutant forms of p97 and measures their effects on function. These inhibitors bind at the interface of the D1 and D2 domains of each p97 subunit, shifting surrounding helices and altering the loop structures near the C-terminal α2 G helix to modulate domain-domain communications. A key structural moiety of the inhibitor affects the rotameric conformations of interacting side chains, indirectly modulating the N-terminal domain conformation in p97 R155H mutant. The differential effects of inhibitor binding to wild-type and mutant p97 provide insights into drug design with enhanced specificity, particularly for oncology applications.

Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method

Cryogenic electron microscopy (cryo-EM) has emerged as a powerful method for the determination of structures of complex biological molecules. The accurate characterisation of the dynamics of such systems, however, remains a challenge. To address this problem, we introduce cryoENsemble, a method that applies Bayesian reweighting to conformational ensembles derived from molecular dynamics simulations to improve their agreement with cryo-EM data, thus enabling the extraction of dynamics information. We illustrate the use of cryoENsemble to determine the dynamics of the ribosome-bound state of the co-translational chaperone trigger factor (TF). We also show that cryoENsemble can assist with the interpretation of low-resolution, noisy or unaccounted regions of cryo-EM maps. Notably, we are able to link an unaccounted part of the cryo-EM map to the presence of another protein (methionine aminopeptidase, or MetAP), rather than to the dynamics of TF, and model its TF-bound state. Based on these results, we anticipate that cryoENsemble will find use for challenging heterogeneous cryo-EM maps for biomolecular systems encompassing dynamic components.

Correction of preferred orientation-induced distortion in cryo-electron microscopy maps

Reconstruction maps of cryo-electron microscopy (cryo-EM) exhibit distortion when the cryo-EM dataset is incomplete, usually caused by unevenly distributed orientations. Prior efforts had been attempted to address this preferred orientation problem using tilt-collection strategy and modifications to grids or to air-water interfaces. However, these approaches often require time-consuming experiments, and the effect was always protein dependent. Here, we developed a procedure containing removing misaligned particles and an iterative reconstruction method based on signal-to-noise ratio of Fourier component to correct this distortion by recovering missing data using a purely computational algorithm. This procedure called signal-to-noise ratio iterative reconstruction method (SIRM) was applied on incomplete datasets of various proteins to fix distortion in cryo-EM maps and to a more isotropic resolution. In addition, SIRM provides a better reference map for further reconstruction refinements, resulting in an improved alignment, which ultimately improves map quality and benefits model building.

Factors affecting macromolecule orientations in thin films formed in cryo-EM

The formation of a vitrified thin film embedded with randomly oriented macromolecules is an essential prerequisite for cryogenic sample electron microscopy. Most commonly, this is achieved using the plunge-freeze method first described nearly 40 years ago. Although this is a robust method, the behaviour of different macromolecules shows great variation upon freezing and often needs to be optimized to obtain an isotropic, high-resolution reconstruction. For a macromolecule in such a film, the probability of encountering the air-water interface in the time between blotting and freezing and adopting preferred orientations is very high. 3D reconstruction using preferentially oriented particles often leads to anisotropic and uninterpretable maps. Currently, there are no general solutions to this prevalent issue, but several approaches largely focusing on sample preparation with the use of additives and novel grid modifications have been attempted. In this study, the effect of physical and chemical factors on the orientations of macromolecules was investigated through an analysis of selected well studied macromolecules, and important parameters that determine the behaviour of proteins on cryo-EM grids were revealed. These insights highlight the nature of the interactions that cause preferred orientations and can be utilized to systematically address orientation bias for any given macromolecule and to provide a framework to design small-molecule additives to enhance sample stability and behaviour.

Using mass spectrometry-based methods to understand amyloid formation and inhibition of alpha-synuclein and amyloid beta

Amyloid fibrils, insoluble β-sheets structures that arise from protein misfolding, are associated with several neurodegenerative disorders. Many small molecules have been investigated to prevent amyloid fibrils from forming; however, there are currently no therapeutics to combat these diseases. Mass spectrometry (MS) is proving to be effective for studying the high order structure (HOS) of aggregating proteins and for determining structural changes accompanying protein-inhibitor interactions. When combined with native MS (nMS), gas-phase ion mobility, protein footprinting, and chemical cross-linking, MS can afford regional and sometimes amino acid spatial resolution of the aggregating protein. The spatial resolution is greater than typical low-resolution spectroscopic, calorimetric, and the traditional ThT fluorescence methods used in amyloid research today. High-resolution approaches can struggle when investigating protein aggregation, as the proteins exist as complex oligomeric mixtures of many sizes and several conformations or polymorphs. Thus, MS is positioned to complement both high- and low-resolution approaches to studying amyloid fibril formation and protein-inhibitor interactions. This review covers basics in MS paired with ion mobility, continuous hydrogen-deuterium exchange (continuous HDX), pulsed hydrogen-deuterium exchange (pulsed HDX), fast photochemical oxidation of proteins (FPOP) and other irreversible labeling methods, and chemical cross-linking. We then review the applications of these approaches to studying amyloid-prone proteins with a focus on amyloid beta and alpha-synuclein. Another focus is the determination of protein-inhibitor interactions. The expectation is that MS will bring new insights to amyloid formation and thereby play an important role to prevent their formation.

Automated multiconformer model building for X-ray crystallography and cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift toward modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior Rfree and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g., Coot) and fit can be further improved by refinement using standard pipelines (e.g., Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

An immobilized antibody-based affinity grid strategy for on-grid purification of target proteins enables high-resolution cryo-EM

In cryo-electron microscopy (cryo-EM), sample preparation poses a critical bottleneck, particularly for rare or fragile macromolecular assemblies and those suffering from denaturation and particle orientation distribution issues related to air-water interface. In this study, we develop and characterize an immobilized antibody-based affinity grid (IAAG) strategy based on the high-affinity PA tag/NZ-1 antibody epitope tag system. We employ Pyr-NHS as a linker to immobilize NZ-1 Fab on the graphene oxide or carbon-covered grid surface. Our results demonstrate that the IAAG grid effectively enriches PA-tagged target proteins and overcomes preferred orientation issues. Furthermore, we demonstrate the utility of our IAAG strategy for on-grid purification of low-abundance target complexes from cell lysates, enabling atomic resolution cryo-EM. This approach greatly streamlines the purification process, reduces the need for large quantities of biological samples, and addresses common challenges encountered in cryo-EM sample preparation. Collectively, our IAAG strategy provides an efficient and robust means for combined sample purification and vitrification, feasible for high-resolution cryo-EM. This approach holds potential for broader applicability in both cryo-EM and cryo-electron tomography (cryo-ET).

Engineering G protein-coupled receptors for stabilization

G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.

Apprehensions and emerging solutions in ML-based protein structure prediction

The three-dimensional structure of proteins determines their function in vital biological processes. Thus, when the structure is known, the molecular mechanism of protein function can be understood in more detail and obtained information utilized in biotechnological, diagnostics, and therapeutic applications. Over the past five years, machine learning (ML)-based modeling has pushed protein structure prediction to the next level with AlphaFold in the front line, predicting the structure for hundreds of millions of proteins. Further advances recently report promising ML-based approaches for solving remaining challenges by incorporating functionally important metals, co-factors, post-translational modifications, structural dynamics, and interdomain and multimer interactions in the structure prediction process.

Graphene in cryo-EM specimen optimization

Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.

Direct Imaging of Radiation-Sensitive Organic Polymer-Based Nanocrystals at Sub-Ångström Resolution

Seeing the atomic configuration of single organic nanoparticles at a sub-Å spatial resolution by transmission electron microscopy has been so far prevented by the high sensitivity of soft matter to radiation damage. This difficulty is related to the need to irradiate the particle with a total dose of a few electrons/Å2, not compatible with the electron beam density necessary to search the low-contrast nanoparticle, to control its drift, finely adjust the electron-optical conditions and particle orientation, and finally acquire an effective atomic-resolution image. On the other hand, the capability to study individual pristine nanoparticles, such as proteins, active pharmaceutical ingredients, and polymers, with peculiar sensitivity to the variation in the local structure, defects, and strain, would provide advancements in many fields, including materials science, medicine, biology, and pharmacology. Here, we report the direct sub-ångström-resolution imaging at room temperature of pristine unstained crystalline polymer-based nanoparticles. This result is obtained by combining low-dose in-line electron holography and phase-contrast imaging on state-of-the-art equipment, providing an effective tool for the quantitative sub-ångström imaging of soft matter.

Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM

In their folded state, biomolecules exchange between multiple conformational states that are crucial for their function. Traditional structural biology methods, such as X-ray crystallography and cryogenic electron microscopy (cryo-EM), produce density maps that are ensemble averages, reflecting molecules in various conformations. Yet, most models derived from these maps explicitly represent only a single conformation, overlooking the complexity of biomolecular structures. To accurately reflect the diversity of biomolecular forms, there is a pressing need to shift towards modeling structural ensembles that mirror the experimental data. However, the challenge of distinguishing signal from noise complicates manual efforts to create these models. In response, we introduce the latest enhancements to qFit, an automated computational strategy designed to incorporate protein conformational heterogeneity into models built into density maps. These algorithmic improvements in qFit are substantiated by superior R f r e e and geometry metrics across a wide range of proteins. Importantly, unlike more complex multicopy ensemble models, the multiconformer models produced by qFit can be manually modified in most major model building software (e.g. Coot) and fit can be further improved by refinement using standard pipelines (e.g. Phenix, Refmac, Buster). By reducing the barrier of creating multiconformer models, qFit can foster the development of new hypotheses about the relationship between macromolecular conformational dynamics and function.

Apolipoprotein E secreted by astrocytes forms antiparallel dimers in discoidal lipoproteins

The Apolipoprotein E gene (APOE) is of great interest due to its role as a risk factor for late-onset Alzheimer's disease. ApoE is secreted by astrocytes in the central nervous system in high-density lipoprotein (HDL)-like lipoproteins. Structural models of lipidated ApoE of high resolution could aid in a mechanistic understanding of how ApoE functions in health and disease. Using monoclonal Fab and F(ab')2 fragments, we characterize the structure of lipidated ApoE on astrocyte-secreted lipoproteins. Our results provide support for the "double-belt" model of ApoE in nascent discoidal HDL-like lipoproteins, where two ApoE proteins wrap around the nanodisc in an antiparallel conformation. We further show that lipidated, recombinant ApoE accurately models astrocyte-secreted ApoE lipoproteins. Cryogenic electron microscopy of recombinant lipidated ApoE further supports ApoE adopting antiparallel dimers in nascent discoidal lipoproteins.

Cryo-electron microscopy-based drug design

Structure-based drug design (SBDD) has gained popularity owing to its ability to develop more potent drugs compared to conventional drug-discovery methods. The success of SBDD relies heavily on obtaining the three-dimensional structures of drug targets. X-ray crystallography is the primary method used for solving structures and aiding the SBDD workflow; however, it is not suitable for all targets. With the resolution revolution, enabling routine high-resolution reconstruction of structures, cryogenic electron microscopy (cryo-EM) has emerged as a promising alternative and has attracted increasing attention in SBDD. Cryo-EM offers various advantages over X-ray crystallography and can potentially replace X-ray crystallography in SBDD. To fully utilize cryo-EM in drug discovery, understanding the strengths and weaknesses of this technique and noting the key advancements in the field are crucial. This review provides an overview of the general workflow of cryo-EM in SBDD and highlights technical innovations that enable its application in drug design. Furthermore, the most recent achievements in the cryo-EM methodology for drug discovery are discussed, demonstrating the potential of this technique for advancing drug development. By understanding the capabilities and advancements of cryo-EM, researchers can leverage the benefits of designing more effective drugs. This review concludes with a discussion of the future perspectives of cryo-EM-based SBDD, emphasizing the role of this technique in driving innovations in drug discovery and development. The integration of cryo-EM into the drug design process holds great promise for accelerating the discovery of new and improved therapeutic agents to combat various diseases.

Illuminating the understudied GPCR-ome

G-protein-coupled receptors (GPCRs) are the target of >30% of approved drugs. Despite their popularity, many of the >800 human GPCRs remain understudied. The Illuminating the Druggable Genome (IDG) project has generated many tools leading to important insights into the function and druggability of these so-called 'dark' receptors. These tools include assays, such as PRESTO-TANGO and TRUPATH, billions of small molecules made available via the ZINC virtual library, solved orphan GPCR structures, GPCR knock-in mice, and more. Together, these tools are illuminating the remaining 'dark' GPCRs.

Recent Progress of Protein Tertiary Structure Prediction

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Electro-metabolic signaling

Precise matching of energy substrate delivery to local metabolic needs is essential for the health and function of all tissues. Here, we outline a mechanistic framework for understanding this critical process, which we refer to as electro-metabolic signaling (EMS). All tissues exhibit changes in metabolism over varying spatiotemporal scales and have widely varying energetic needs and reserves. We propose that across tissues, common signatures of elevated metabolism or increases in energy substrate usage that exceed key local thresholds rapidly engage mechanisms that generate hyperpolarizing electrical signals in capillaries that then relax contractile elements throughout the vasculature to quickly adjust blood flow to meet changing needs. The attendant increase in energy substrate delivery serves to meet local metabolic requirements and thus avoids a mismatch in supply and demand and prevents metabolic stress. We discuss in detail key examples of EMS that our laboratories have discovered in the brain and the heart, and we outline potential further EMS mechanisms operating in tissues such as skeletal muscle, pancreas, and kidney. We suggest that the energy imbalance evoked by EMS uncoupling may be central to cellular dysfunction from which the hallmarks of aging and metabolic diseases emerge and may lead to generalized organ failure states-such as diverse flavors of heart failure and dementia. Understanding and manipulating EMS may be key to preventing or reversing these dysfunctions.

Artificial Intelligence-based Amide-II Infrared Spectroscopy Simulation for Monitoring Protein Hydrogen Bonding Dynamics

The structurally sensitive amide II infrared (IR) bands of proteins provide valuable information about the hydrogen bonding of protein secondary structures, which is crucial for understanding protein dynamics and associated functions. However, deciphering protein structures from experimental amide II spectra relies on time-consuming quantum chemical calculations on tens of thousands of representative configurations in solvent water. Currently, the accurate simulation of amide II spectra for whole proteins remains a challenge. Here, we present a machine learning (ML)-based protocol designed to efficiently simulate the amide II IR spectra of various proteins with an accuracy comparable to experimental results. This protocol stands out as a cost-effective and efficient alternative for studying protein dynamics, including the identification of secondary structures and monitoring the dynamics of protein hydrogen bonding under different pH conditions and during protein folding process. Our method provides a valuable tool in the field of protein research, focusing on the study of dynamic properties of proteins, especially those related to hydrogen bonding, using amide II IR spectroscopy.

Graphene sandwich-based biological specimen preparation for cryo-EM analysis

High-quality specimen preparation plays a crucial role in cryo-electron microscopy (cryo-EM) structural analysis. In this study, we have developed a reliable and convenient technique called the graphene sandwich method for preparing cryo-EM specimens. This method involves using two layers of graphene films that enclose macromolecules on both sides, allowing for an appropriate ice thickness for cryo-EM analysis. The graphene sandwich helps to mitigate beam-induced charging effect and reduce particle motion compared to specimens prepared using the traditional method with graphene support on only one side, therefore improving the cryo-EM data quality. These advancements may open new opportunities to expand the use of graphene in the field of biological electron microscopy.

A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction

Cryo-Electron Microscopy (cryo-EM) is a widely used and effective method for determining the three-dimensional (3D) structure of biological molecules. For ab-initio Cryo-EM 3D reconstruction using single particle analysis (SPA), estimating the projection direction of the projection image is a crucial step. However, the existing SPA methods based on common lines are sensitive to noise. The error in common line detection will lead to a poor estimation of the projection directions and thus may greatly affect the final reconstruction results. To improve the reconstruction results, multiple candidate common lines are estimated for each pair of projection images. The key problem then becomes a combination optimization problem of selecting consistent common lines from multiple candidates. To solve the problem efficiently, a physics-inspired method based on a kinetic model is proposed in this work. More specifically, hypothetical attractive forces between each pair of candidate common lines are used to calculate a hypothetical torque exerted on each projection image in the 3D reconstruction space, and the rotation under the hypothetical torque is used to optimize the projection direction estimation of the projection image. This way, the consistent common lines along with the projection directions can be found directly without enumeration of all the combinations of the multiple candidate common lines. Compared with the traditional methods, the proposed method is shown to be able to produce more accurate 3D reconstruction results from high noise projection images. Besides the practical value, the proposed method also serves as a good reference for solving similar combinatorial optimization problems.

Recent Advances and Challenges in Protein Structure Prediction

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Exploring the Alternative Conformation of a Known Protein Structure Based on Contact Map Prediction

The rapid development of deep learning-based methods has considerably advanced the field of protein structure prediction. The accuracy of predicting the 3D structures of simple proteins is comparable to that of experimentally determined structures, providing broad possibilities for structure-based biological studies. Another critical question is whether and how multistate structures can be predicted from a given protein sequence. In this study, analysis of tens of two-state proteins demonstrated that deep learning-based contact map predictions contain structural information on both states, which suggests that it is probably appropriate to change the target of deep learning-based protein structure prediction from one specific structure to multiple likely structures. Furthermore, by combining deep learning- and physics-based computational methods, we developed a protocol for exploring alternative conformations from a known structure of a given protein, by which we successfully approached the holo-state conformations of multiple representative proteins from their apo-state structures.

One bead per residue can describe all-atom protein structures

Atomistic resolution is the standard for high-resolution biomolecular structures, but experimental structural data are often at lower resolution. Coarse-grained models are also used extensively in computational studies to reach biologically relevant spatial and temporal scales. This study explores the use of advanced machine learning networks for reconstructing atomistic models from reduced representations. The main finding is that a single bead per amino acid residue allows construction of accurate and stereochemically realistic all-atom structures with minimal loss of information. This suggests that lower resolution representations of proteins may be sufficient for many applications when combined with a machine learning framework that encodes knowledge from known structures. Practical applications include the rapid addition of atomistic detail to low-resolution structures from experiment or computational coarse-grained models. The application of rapid, deterministic all-atom reconstruction within multi-scale frameworks is further demonstrated with a rapid protocol for the generation of accurate models from cryo-EM densities close to experimental structures.

Sample Preparation for Electron Cryo-Microscopy of Macromolecular Machines

High-resolution structure determination by electron cryo-microscopy underwent a step change in recent years. This now allows study of challenging samples which previously were inaccessible for structure determination, including membrane proteins. These developments shift the focus in the field to the next bottlenecks which are high-quality sample preparations. While the amounts of sample required for cryo-EM are relatively small, sample quality is the key challenge. Sample quality is influenced by the stability of complexes which depends on buffer composition, inherent flexibility of the sample, and the method of solubilization from the membrane for membrane proteins. It further depends on the choice of sample support, grid pre-treatment and cryo-grid freezing protocol. Here, we discuss various widely applicable approaches to improve sample quality for structural analysis by cryo-EM.

A method for structure determination of GPCRs in various states

G-protein-coupled receptors (GPCRs) are a class of integral membrane proteins that detect environmental cues and trigger cellular responses. Deciphering the functional states of GPCRs induced by various ligands has been one of the primary goals in the field. Here we developed an effective universal method for GPCR cryo-electron microscopy structure determination without the need to prepare GPCR-signaling protein complexes. Using this method, we successfully solved the structures of the β2-adrenergic receptor (β2AR) bound to antagonistic and agonistic ligands and the adhesion GPCR ADGRL3 in the apo state. For β2AR, an intermediate state stabilized by the partial agonist was captured. For ADGRL3, the structure revealed that inactive ADGRL3 adopts a compact fold and that large unusual conformational changes on both the extracellular and intracellular sides are required for activation of adhesion GPCRs. We anticipate that this method will open a new avenue for understanding GPCR structure‒function relationships and drug development.

Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2

Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.

Insights into protein structure using cryogenic light microscopy

Fluorescence microscopy has witnessed many clever innovations in the last two decades, leading to new methods such as structured illumination and super-resolution microscopies. The attainable resolution in biological samples is, however, ultimately limited by residual motion within the sample or in the microscope setup. Thus, such experiments are typically performed on chemically fixed samples. Cryogenic light microscopy (Cryo-LM) has been investigated as an alternative, drawing on various preservation techniques developed for cryogenic electron microscopy (Cryo-EM). Moreover, this approach offers a powerful platform for correlative microscopy. Another key advantage of Cryo-LM is the strong reduction in photobleaching at low temperatures, facilitating the collection of orders of magnitude more photons from a single fluorophore. This results in much higher localization precision, leading to Angstrom resolution. In this review, we discuss the general development and progress of Cryo-LM with an emphasis on its application in harnessing structural information on proteins and protein complexes.

Emerging Trends in Cryo-EM-based Structural Studies of Neuropathological Amyloids

Tauopathies, synucleinopathies, Aβ amyloidosis, TDP-43 proteinopathies, and prion diseases- these neurodegenerative diseases have in common the formation of amyloid filaments rich in cross-β sheets. Cryo-electron microscopy now permits the visualization of amyloid assemblies at atomic resolution, ushering a wide range of structural studies on several of these poorly understood amyloidogenic proteins. Amyloids are polymorphic with minor modulations in reaction environment affecting the overall architecture of their assembly, making amyloids an extremely challenging venture for structure-based therapeutic intervention. In 2017, the first cryo-EM structure of tau filaments from an Alzheimer's disease-affected brain established that in vitro assemblies might not necessarily reflect the native amyloid fold. Since then, brain-derived amyloid structures for several proteins across many neurodegenerative diseases have uncovered the disease-relevant amyloid folds. It has now been shown for tauopathies, synucleinopathies and TDP-43 proteinopathies, that distinct amyloid folds of the same protein might be related to different diseases. Salient features of each of these brain-derived folds are discussed in detail. It was also recently observed that seeded aggregation does not necessarily replicate the brain-derived structural fold. Owing to high throughput structure determination, some of these native amyloid folds have also been successfully replicated in vitro. In vitro replication of disease-relevant filaments will aid development of imaging ligands and defibrillating drugs. Towards this direction, recent high-resolution structures of tau filaments with positron emission tomography tracers and a defibrillating drug are also discussed. This review summarizes and celebrates the recent advancements in structural understanding of neuropathological amyloid filaments using cryo-EM.

Aptamer-Protein Interactions: From Regulation to Biomolecular Detection

Serving as the basis of cell life, interactions between nucleic acids and proteins play essential roles in fundamental cellular processes. Aptamers are unique single-stranded oligonucleotides generated by in vitro evolution methods, possessing the ability to interact with proteins specifically. Altering the structure of aptamers will largely modulate their interactions with proteins and further affect related cellular behaviors. Recently, with the in-depth research of aptamer-protein interactions, the analytical assays based on their interactions have been widely developed and become a powerful tool for biomolecular detection. There are some insightful reviews on aptamers applied in protein detection, while few systematic discussions are from the perspective of regulating aptamer-protein interactions. Herein, we comprehensively introduce the methods for regulating aptamer-protein interactions and elaborate on the detection techniques for analyzing aptamer-protein interactions. Additionally, this review provides a broad summary of analytical assays based on the regulation of aptamer-protein interactions for detecting biomolecules. Finally, we present our perspectives regarding the opportunities and challenges of analytical assays for biological analysis, aiming to provide guidance for disease mechanism research and drug discovery.

Structural insights of the p97/VCP AAA+ ATPase: How adapter interactions coordinate diverse cellular functionality

p97/valosin-containing protein is an essential eukaryotic AAA+ ATPase with diverse functions including protein homeostasis, membrane remodeling, and chromatin regulation. Dysregulation of p97 function causes severe neurodegenerative disease and is associated with cancer, making this protein a significant therapeutic target. p97 extracts polypeptide substrates from macromolecular assemblies by hydrolysis-driven translocation through its central pore. Growing evidence indicates that this activity is highly coordinated by "adapter" partner proteins, of which more than 30 have been identified and are commonly described to facilitate translocation through substrate recruitment or modification. In so doing, these adapters enable critical p97-dependent functions such as extraction of misfolded proteins from the endoplasmic reticulum or mitochondria, and are likely the reason for the extreme functional diversity of p97 relative to other AAA+ translocases. Here, we review the known functions of adapter proteins and highlight recent structural and biochemical advances that have begun to reveal the diverse molecular bases for adapter-mediated regulation of p97 function. These studies suggest that the range of mechanisms by which p97 activity is controlled is vastly underexplored with significant advances possible for understanding p97 regulation by the most known adapters.

OPUS-DSD: deep structural disentanglement for cryo-EM single-particle analysis

Cryo-electron microscopy (cryo-EM) captures snapshots of dynamic macromolecules, collectively illustrating the involved structural landscapes. This provides an exciting opportunity to explore the structural variations of macromolecules under study. However, traditional cryo-EM single-particle analysis often yields static structures. Here we describe OPUS-DSD, an algorithm capable of efficiently reconstructing the structural landscape embedded in cryo-EM data. OPUS-DSD uses a three-dimensional convolutional encoder-decoder architecture trained with cryo-EM images, thereby encoding structural variations into a smooth and easily analyzable low-dimension space. This space can be traversed to reconstruct continuous dynamics or clustered to identify distinct conformations. OPUS-DSD can offer meaningful insights into the structural variations of macromolecules, filling in the gaps left by traditional cryo-EM structural determination, and potentially improves the reconstruction resolution by reliably clustering similar particles within the dataset. These functionalities are especially relevant to the study of highly dynamic biological systems. OPUS-DSD is available at https://github.com/alncat/opusDSD .

Structure and Dynamics Guiding Design of Antibody Therapeutics and Vaccines

Antibodies and other new antibody-like formats have emerged as one of the most rapidly growing classes of biotherapeutic proteins. Understanding the structural features that drive antibody function and, consequently, their molecular recognition is critical for engineering antibodies. Here, we present the structural architecture of conventional IgG antibodies alongside other formats. We emphasize the importance of considering antibodies as conformational ensembles in solution instead of focusing on single-static structures because their functions and properties are strongly governed by their dynamic nature. Thus, in this review, we provide an overview of the unique structural and dynamic characteristics of antibodies with respect to their antigen recognition, biophysical properties, and effector functions. We highlight the numerous technical advances in antibody structure prediction and design, enabled by the vast number of experimentally determined high-quality structures recorded with cryo-EM, NMR, and X-ray crystallography. Lastly, we assess antibody and vaccine design strategies in the context of structure and dynamics.

Structural biology and inhibition of the Mtb cell wall glycoconjugates biosynthesis on the membrane

Glycoconjugates are the dominant components of the Mycobacterium tuberculosis cell wall. These glycoconjugates are essential for the viability of Mtb and attribute to drug resistance and virulence during infection. The assembly and maturation of the cell wall largely relies on the Mtb plasma membrane. A significant number of membrane-bound glycosyltransferases (GTs) and transporters play pivotal roles in forming the complex glycoconjugates and are targeted by the first-line anti-TB drug and potent drug candidates. Here we summarize the latest structural biology of mycobacterial GTs and transporters, and describe the modes of action of drug and drug candidates that are of substantial clinical value in anti-TB chemotherapeutics.

Molecular dynamics simulations support a multistep pathway for activation of branched actin filament nucleation by Arp2/3 complex

Actin-related protein 2/3 complex (Arp2/3 complex) catalyzes the nucleation of branched actin filaments that push against membranes in processes like cellular motility and endocytosis. During activation by WASP proteins, the complex must bind WASP and engage the side of a pre-existing (mother) filament before a branched filament is nucleated. Recent high-resolution structures of activated Arp2/3 complex revealed two major sets of activating conformational changes. How these activating conformational changes are triggered by interactions of Arp2/3 complex with actin filaments and WASP remains unclear. Here we use a recent high-resolution structure of Arp2/3 complex at a branch junction to design all-atom molecular dynamics simulations that elucidate the pathway between the active and inactive states. We ran a total of ∼4.6 microseconds of both unbiased and steered all-atom molecular dynamics simulations starting from three different binding states, including Arp2/3 complex within a branch junction, bound only to a mother filament, and alone in solution. These simulations indicate that the contacts with the mother filament are mostly insensitive to the massive rigid body motion that moves Arp2 and Arp3 into a short pitch helical (filament-like) arrangement, suggesting actin filaments alone do not stimulate the short pitch conformational change. In contrast, contacts with the mother filament stabilize subunit flattening in Arp3, an intrasubunit change that converts Arp3 from a conformation that mimics an actin monomer to one that mimics a filamentous actin subunit. Our results support a multistep activation pathway that has important implications for understanding how WASP-mediated activation allows Arp2/3 complex to assemble force-producing actin networks.

Geometric phase correction: A direct phase correction method to register low contrast noisy TEM images

A major challenge in the emerging field of low-dose electron microscopy lies in the development of drift correction algorithms against beam-induced specimen motion and compatible with highly noisy transmission electron microscopy (TEM) images. We report here a new drift correction method, namely geometric phase correlation (GPC), to correlate the specimen motion in real space by directly measuring the unwrapped geometric phase shift in the spatial frequency domain of the TEM image (especially from the intensive Bragg spots for crystalline materials) with sub-pixel precision. The GPC method outperforms cross-correlation-based methods in both accuracy of specimen motion prediction from highly noisy TEM movies and computational efficiency of drift calculation from abundant image frames, which holds great promise for diverse applications in low-dose TEM imaging of beam-sensitive materials, such as metal-organic frameworks (MOFs) and covalent organic frameworks (COFs).

Electrostatic complementarity at the interface drives transient protein-protein interactions

Understanding the mechanisms driving bio-molecules binding and determining the resulting complexes' stability is fundamental for the prediction of binding regions, which is the starting point for drug-ability and design. Characteristics like the preferentially hydrophobic composition of the binding interfaces, the role of van der Waals interactions, and the consequent shape complementarity between the interacting molecular surfaces are well established. However, no consensus has yet been reached on the role of electrostatic. Here, we perform extensive analyses on a large dataset of protein complexes for which both experimental binding affinity and pH data were available. Probing the amino acid composition, the disposition of the charges, and the electrostatic potential they generated on the protein molecular surfaces, we found that (i) although different classes of dimers do not present marked differences in the amino acid composition and charges disposition in the binding region, (ii) homodimers with identical binding region show higher electrostatic compatibility with respect to both homodimers with non-identical binding region and heterodimers. Interestingly, (iii) shape and electrostatic complementarity, for patches defined on short-range interactions, behave oppositely when one stratifies the complexes by their binding affinity: complexes with higher binding affinity present high values of shape complementarity (the role of the Lennard-Jones potential predominates) while electrostatic tends to be randomly distributed. Conversely, complexes with low values of binding affinity exploit Coulombic complementarity to acquire specificity, suggesting that electrostatic complementarity may play a greater role in transient (or less stable) complexes. In light of these results, (iv) we provide a novel, fast, and efficient method, based on the 2D Zernike polynomial formalism, to measure electrostatic complementarity without the need of knowing the complex structure. Expanding the electrostatic potential on a basis of 2D orthogonal polynomials, we can discriminate between transient and permanent protein complexes with an AUC of the ROC of [Formula: see text] 0.8. Ultimately, our work helps shedding light on the non-trivial relationship between the hydrophobic and electrostatic contributions in the binding interfaces, thus favoring the development of new predictive methods for binding affinity characterization.

Quantitative analysis of protein dynamics using a deep learning technique combined with experimental cryo-EM density data and MD simulations

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.

Prospects and Limitations of High-Resolution Single-Particle Cryo-Electron Microscopy

Single particle cryo-electron microscopy (cryo-EM) has matured into a robust method for the determination of biological macromolecule structures in the past decade, complementing X-ray crystallography and nuclear magnetic resonance. Constant methodological improvements in both cryo-EM hardware and image processing software continue to contribute to an exponential growth in the number of structures solved annually. In this review, we provide a historical view of the many steps that were required to make cryo-EM a successful method for the determination of high-resolution protein complex structures. We further discuss aspects of cryo-EM methodology that are the greatest pitfalls challenging successful structure determination to date. Lastly, we highlight and propose potential future developments that would improve the method even further in the near future.

Unsupervised Cryo-EM Images Denoising and Clustering Based on Deep Convolutional Autoencoder and K-Means+

Cryo-electron microscopy (cryo-EM) is a widely used structural determination technique. Because of the extremely low signal-to-noise ratio (SNR) of images captured by cryo-EM, clustering single-particle cryo-EM images with high accuracy is challenging. To address this, we proposed an iterative denoising and clustering method based on a deep convolutional variational autoencoder and K-means++. The proposed method contains two modules: a denoising ResNet variational autoencoder (DRVAE), and Balance size K-means++ (BSK-means++). First, the DRVAE is trained in a fully unsupervised manner to initialize the neural network and obtain preliminary denoised images. Second, BSK-means++ is built for clustering denoised images, and images closer to class centers are divided into reliable samples. Third, the training of DRVAE is continued, while the class-average images are used as pseudo supervision of reliable samples to reserve more detailed information of denoised images. Finally, the second and third steps mentioned above can be performed jointly and iteratively until convergence occurs. The experimental results showed that the proposed method can generate reliable class average images and achieve better clustering accuracy and normalized mutual information than current methods. This study confirmed that DRVAE with BSK-means++ could achieve a good denoise performance on single-particle cryo-EM images, which can help researchers obtain information such as symmetry and heterogeneity of the target particles. In addition, the proposed method avoids the extreme imbalance of class size, which improves the reliability of the clustering result.

Structural analysis of cancer-relevant TCR-CD3 and peptide-MHC complexes by cryoEM

The recognition of antigenic peptide-MHC (pMHC) molecules by T-cell receptors (TCR) initiates the T-cell mediated immune response. Structural characterization is key for understanding the specificity of TCR-pMHC interactions and informing the development of therapeutics. Despite the rapid rise of single particle cryoelectron microscopy (cryoEM), x-ray crystallography has remained the preferred method for structure determination of TCR-pMHC complexes. Here, we report cryoEM structures of two distinct full-length α/β TCR-CD3 complexes bound to their pMHC ligand, the cancer-testis antigen HLA-A2/MAGEA4 (230-239). We also determined cryoEM structures of pMHCs containing MAGEA4 (230-239) peptide and the closely related MAGEA8 (232-241) peptide in the absence of TCR, which provided a structural explanation for the MAGEA4 preference displayed by the TCRs. These findings provide insights into the TCR recognition of a clinically relevant cancer antigen and demonstrate the utility of cryoEM for high-resolution structural analysis of TCR-pMHC interactions.

Accurate real space iterative reconstruction (RESIRE) algorithm for tomography

Tomography has made a revolutionary impact on the physical, biological and medical sciences. The mathematical foundation of tomography is to reconstruct a three-dimensional (3D) object from a set of two-dimensional (2D) projections. As the number of projections that can be measured from a sample is usually limited by the tolerable radiation dose and/or the geometric constraint on the tilt range, a main challenge in tomography is to achieve the best possible 3D reconstruction from a limited number of projections with noise. Over the years, a number of tomographic reconstruction methods have been developed including direct inversion, real-space, and Fourier-based iterative algorithms. Here, we report the development of a real-space iterative reconstruction (RESIRE) algorithm for accurate tomographic reconstruction. RESIRE iterates between the update of a reconstructed 3D object and the measured projections using a forward and back projection step. The forward projection step is implemented by the Fourier slice theorem or the Radon transform, and the back projection step by a linear transformation. Our numerical and experimental results demonstrate that RESIRE performs more accurate 3D reconstructions than other existing tomographic algorithms, when there are a limited number of projections with noise. Furthermore, RESIRE can be used to reconstruct the 3D structure of extended objects as demonstrated by the determination of the 3D atomic structure of an amorphous Ta thin film. We expect that RESIRE can be widely employed in the tomography applications in different fields. Finally, to make the method accessible to the general user community, the MATLAB source code of RESIRE and all the simulated and experimental data are available at  https://zenodo.org/record/7273314 .

Atomic crystal structure and sugar specificity of a β-trefoil lectin domain from the ectomycorrhizal basidiomycete Laccaria bicolor

Lectins from fruiting bodies are a diverse group of sugar-binding proteins from mushrooms that face the biologically relevant challenge of discriminating self- from non-self carbohydrate structures, therefore providing a basis for an innate defence system. Such a system entails both detection and destruction of invaders and/or feeders, and in contrast to more complex organisms with immense immune systems, these two functions normally rely on multitasking lectins, namely, lectins with different functional modules. Here, we present a novel fungal lectin, LBL, from the basidiomycete Laccaria bicolor. Using a diverse set of biophysical techniques, we unveil the fine details of the sugar-binding specificity of the N-terminal β-trefoil of LBL (LBL152), whose structure has been determined at the highest resolution so far reported for such a fold. LBL152 binds complex poly-N-Acetyllactosamine polysaccharides and also robust LBL152 binding to Caenorhabditis elegans and Drosophila melanogaster cellular extracts was detected in microarray assays, with a seeming preference for the fruit fly adult and pupa stages over the larva stage. Prediction of the structure of the C-terminal part of LBL with AlphaFold reveals a tandem repeat of two structurally almost identical domains of around 110 amino acids each, despite sharing low sequence conservation.

Cryo-EM reveals the structure and dynamics of a 723-residue malate synthase G

Determination of sub-100 kilodalton (kDa) structures by cryo-electron microscopy (EM) is a longstanding but not straightforward goal. Here, we present a 2.9-Å cryo-EM structure of a 723-amino acid apo-form malate synthase G (MSG) from Escherichia coli. The cryo-EM structure of the 82-kDa MSG exhibits the same global folding as structures resolved by crystallography and nuclear magnetic resonance (NMR) spectroscopy, and the crystal and cryo-EM structures are indistinguishable. Analyses of MSG dynamics reveal consistent conformational flexibilities among the three experimental approaches, most notably that the α/β domain exhibits structural heterogeneity. We observed that sidechains of F453, L454, M629, and E630 residues involved in hosting the cofactor acetyl-CoA and substrate rotate differently between the cryo-EM apo-form and complex crystal structures. Our work demonstrates that the cryo-EM technique can be used to determine structures and conformational heterogeneity of sub-100 kDa biomolecules to a quality as high as that obtained from X-ray crystallography and NMR spectroscopy.

Adeno-associated virus receptor complexes and implications for adeno-associated virus immune neutralization

Adeno-associated viruses (AAV) are among the foremost vectors for in vivo gene therapy. A number of monoclonal antibodies against several serotypes of AAV have previously been prepared. Many are neutralizing, and the predominant mechanisms have been reported as the inhibition of binding to extracellular glycan receptors or interference with some post-entry step. The identification of a protein receptor and recent structural characterization of its interactions with AAV compel reconsideration of this tenet. AAVs can be divided into two families based on which domain of the receptor is strongly bound. Neighboring domains, unseen in the high-resolution electron microscopy structures have now been located by electron tomography, pointing away from the virus. The epitopes of neutralizing antibodies, previously characterized, are now compared to the distinct protein receptor footprints of the two families of AAV. Comparative structural analysis suggests that antibody interference with protein receptor binding might be the more prevalent mechanism than interference with glycan attachment. Limited competitive binding assays give some support to the hypothesis that inhibition of binding to the protein receptor has been an overlooked mechanism of neutralization. More extensive testing is warranted.