AI-based structure prediction empowers integrative structural analysis of human nuclear pores

Bonds and bytes: The odyssey of structural biology

Characterizing structural and dynamic properties of proteins and large macromolecular assemblies is crucial to understand the molecular mechanisms underlying biological functions. In the field of structural biology, no single method comprehensively reveals the behavior of biological systems across various spatiotemporal scales. Instead, we have a versatile toolkit of techniques, each contributing a piece to the overall puzzle. Integrative structural biology combines different techniques to create accurate and precise multi-scale models that expand our understanding of complex biological systems. This review outlines recent advancements in computational and experimental methods in structural biology, with special focus on recent Artificial Intelligence techniques, emphasizes integrative approaches that combine different types of data for precise spatiotemporal modeling, and provides an outlook into future directions of this field.

HIV-1 capsid shape, orientation, and entropic elasticity regulate translocation into the nuclear pore complex

Nuclear import and uncoating of the viral capsid are critical steps in the HIV-1 life cycle that serve to transport and release genomic material into the nucleus. Viral core import involves translocating the HIV-1 capsid at the nuclear pore complex (NPC). Notably, the central channel of the NPC appears to often accommodate and allow passage of intact HIV-1 capsid, though mechanistic details of the process remain to be fully understood. Here, we investigate the molecular interactions that operate in concert between the HIV-1 capsid and the NPC that regulate capsid translocation through the central channel. To this end, we develop a "bottom-up" coarse-grained (CG) model of the human NPC from recently released cryo-electron tomography structure and then construct composite membrane-embedded CG NPC models. We find that successful translocation from the cytoplasmic side to the NPC central channel is contingent on the compatibility of the capsid morphology and channel dimension and the proper orientation of the capsid approach to the channel from the cytoplasmic side. The translocation dynamics is driven by maximizing the contacts between phenylalanine-glycine nucleoporins at the central channel and the capsid. For the docked intact capsids, structural analysis reveals correlated striated patterns of lattice disorder likely related to the intrinsic capsid elasticity. Uncondensed genomic material inside the docked capsid augments the overall lattice disorder of the capsid. Our results suggest that the intrinsic "elasticity" can also aid the capsid to adapt to the stress and remain structurally intact during translocation.

Nuclear-import receptors as gatekeepers of pathological phase transitions in ALS/FTD

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are fatal neurodegenerative disorders on a disease spectrum that are characterized by the cytoplasmic mislocalization and aberrant phase transitions of prion-like RNA-binding proteins (RBPs). The common accumulation of TAR DNA-binding protein-43 (TDP-43), fused in sarcoma (FUS), and other nuclear RBPs in detergent-insoluble aggregates in the cytoplasm of degenerating neurons in ALS/FTD is connected to nuclear pore dysfunction and other defects in the nucleocytoplasmic transport machinery. Recent advances suggest that beyond their canonical role in the nuclear import of protein cargoes, nuclear-import receptors (NIRs) can prevent and reverse aberrant phase transitions of TDP-43, FUS, and related prion-like RBPs and restore their nuclear localization and function. Here, we showcase the NIR family and how they recognize cargo, drive nuclear import, and chaperone prion-like RBPs linked to ALS/FTD. We also discuss the promise of enhancing NIR levels and developing potentiated NIR variants as therapeutic strategies for ALS/FTD and related neurodegenerative proteinopathies.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterPro and MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

Integrative spatiotemporal map of nucleocytoplasmic transport

The Nuclear Pore Complex (NPC) facilitates rapid and selective nucleocytoplasmic transport of molecules as large as ribosomal subunits and viral capsids. It is not clear how key emergent properties of this transport arise from the system components and their interactions. To address this question, we constructed an integrative coarse-grained Brownian dynamics model of transport through a single NPC, followed by coupling it with a kinetic model of Ran-dependent transport in an entire cell. The microscopic model parameters were fitted to reflect experimental data and theoretical information regarding the transport, without making any assumptions about its emergent properties. The resulting reductionist model is validated by reproducing several features of transport not used for its construction, such as the morphology of the central transporter, rates of passive and facilitated diffusion as a function of size and valency, in situ radial distributions of pre-ribosomal subunits, and active transport rates for viral capsids. The model suggests that the NPC functions essentially as a virtual gate whose flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to diffusion through the pore. Importantly, this core functionality is greatly enhanced by several key design features, including 'fuzzy' and transient interactions, multivalency, redundancy in the copy number of FG nucleoporins, exponential coupling of transport kinetics and thermodynamics in accordance with the transition state theory, and coupling to the energy-reliant RanGTP concentration gradient. These design features result in the robust and resilient rate and selectivity of transport for a wide array of cargo ranging from a few kilodaltons to megadaltons in size. By dissecting these features, our model provides a quantitative starting point for rationally modulating the transport system and its artificial mimics.

Plant nuclear envelope as a hub connecting genome organization with regulation of gene expression

Eukaryotic cells organize their genome within the nucleus with a double-layered membrane structure termed the nuclear envelope (NE) as the physical barrier. The NE not only shields the nuclear genome but also spatially separates transcription from translation. Proteins of the NE including nucleoskeleton proteins, inner nuclear membrane proteins, and nuclear pore complexes have been implicated in interacting with underlying genome and chromatin regulators to establish a higher-order chromatin architecture. Here, I summarize recent advances in the knowledge of NE proteins that are involved in chromatin organization, gene regulation, and coordination of transcription and mRNA export. These studies support an emerging view of plant NE as a central hub that contributes to chromatin organization and gene expression in response to various cellular and environmental cues.

Artificial intelligence in small molecule drug discovery from 2018 to 2023: Does it really work?

Utilizing artificial intelligence (AI) in drug design represents an advanced approach for identifying targets and developing new drugs. Integrating AI techniques significantly reduces the workload involved in drug development and enhances the efficiency of early-stage drug discovery. This review aims to present a comprehensive overview of the utilization of AI methods in the field of small drug design, with a specific focus on four key areas: protein structure prediction, molecular virtual screening, molecular design, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Additionally, the role and limitations of AI in drug development are explored, and the impact of AI on decision-making processes is studied. It is important to note that while AI can bring numerous benefits to the early stage of drug development, the direction and quality of decision-making should still be emphasized, as AI should be considered as a tool rather than a decisive factor.

Genetically encoded multimeric tags for subcellular protein localization in cryo-EM

Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling strategy for intracellular proteins based on fluorescent, 25-nm-sized, genetically encoded multimeric particles (GEMs). The particles exhibit recognizable structural signatures, enabling their automated detection in cryo-ET data by convolutional neural networks. The coupling of GEMs to green fluorescent protein-tagged macromolecules of interest is triggered by addition of a small-molecule ligand, allowing for time-controlled labeling to minimize disturbance to native protein function. We demonstrate the applicability of GEMs for subcellular-level localization of endogenous and overexpressed proteins across different organelles in human cells using cryo-correlative fluorescence and cryo-ET imaging. We describe means for quantifying labeling specificity and efficiency, and for systematic optimization for rare and abundant protein targets, with emphasis on assessing the potential effects of labeling on protein function.

Cryo-EM of a heterogeneous biochemical fraction elucidates multiple protein complexes from a multicellular thermophilic eukaryote

Biomolecular complexes and their interactions govern cellular structure and function. Understanding their architecture is a prerequisite for dissecting the cell's inner workings, but their higher-order assembly is often transient and challenging for structural analysis. Here, we performed cryo-EM on a single, highly heterogeneous biochemical fraction derived from Chaetomium thermophilum cell extracts to visualize the biomolecular content of the multicellular eukaryote. After cryo-EM single-particle image processing, results showed that a simultaneous three-dimensional structural characterization of multiple chemically diverse biomacromolecules is feasible. Namely, the thermophilic, eukaryotic complexes of (a) ATP citrate-lyase, (b) Hsp90, (c) 20S proteasome, (d) Hsp60 and (e) UDP-glucose pyrophosphorylase were characterized. In total, all five complexes have been structurally dissected in a thermophilic eukaryote in a total imaged sample area of 190.64 μm2, and two, in particular, 20S proteasome and Hsp60, exhibit side-chain resolution features. The C. thermophilum Hsp60 near-atomic model was resolved at 3.46 Å (FSC = 0.143) and shows a hinge-like conformational change of its equatorial domain, highly similar to the one previously shown for its bacterial orthologue, GroEL. This work demonstrates that cryo-EM of cell extracts will greatly accelerate the structural analysis of cellular complexes and provide unprecedented opportunities to annotate architectures of biomolecules in a holistic approach.

iU-ExM: nanoscopy of organelles and tissues with iterative ultrastructure expansion microscopy

Expansion microscopy (ExM) is a highly effective technique for super-resolution fluorescence microscopy that enables imaging of biological samples beyond the diffraction limit with conventional fluorescence microscopes. Despite the development of several enhanced protocols, ExM has not yet demonstrated the ability to achieve the precision of nanoscopy techniques such as Single Molecule Localization Microscopy (SMLM). Here, to address this limitation, we have developed an iterative ultrastructure expansion microscopy (iU-ExM) approach that achieves SMLM-level resolution. With iU-ExM, it is now possible to visualize the molecular architecture of gold-standard samples, such as the eight-fold symmetry of nuclear pores or the molecular organization of the conoid in Apicomplexa. With its wide-ranging applications, from isolated organelles to cells and tissue, iU-ExM opens new super-resolution avenues for scientists studying biological structures and functions.

Nuclear transport proteins: structure, function, and disease relevance

Proper subcellular localization is crucial for the functioning of biomacromolecules, including proteins and RNAs. Nuclear transport is a fundamental cellular process that regulates the localization of many macromolecules within the nuclear or cytoplasmic compartments. In humans, approximately 60 proteins are involved in nuclear transport, including nucleoporins that form membrane-embedded nuclear pore complexes, karyopherins that transport cargoes through these complexes, and Ran system proteins that ensure directed and rapid transport. Many of these nuclear transport proteins play additional and essential roles in mitosis, biomolecular condensation, and gene transcription. Dysregulation of nuclear transport is linked to major human diseases such as cancer, neurodegenerative diseases, and viral infections. Selinexor (KPT-330), an inhibitor targeting the nuclear export factor XPO1 (also known as CRM1), was approved in 2019 to treat two types of blood cancers, and dozens of clinical trials of are ongoing. This review summarizes approximately three decades of research data in this field but focuses on the structure and function of individual nuclear transport proteins from recent studies, providing a cutting-edge and holistic view on the role of nuclear transport proteins in health and disease. In-depth knowledge of this rapidly evolving field has the potential to bring new insights into fundamental biology, pathogenic mechanisms, and therapeutic approaches.

Changing the guard-nuclear pore complex quality control

The integrity of the nuclear envelope depends on the function of nuclear pore complexes (NPCs), transport channels that control macromolecular traffic between the nucleus and cytosol. The central importance of NPCs suggests the existence of quality control (QC) mechanisms that oversee their assembly and function. In this perspective, we emphasize the challenges associated with NPC assembly and the need for QC mechanisms that operate at various stages of an NPC's life. This includes cytosolic preassembly QC that helps enforce key nucleoporin-nucleoporin interactions and their ultimate stoichiometry in the NPC in addition to mechanisms that monitor aberrant fusion of the inner and outer nuclear membranes. Furthermore, we discuss whether and how these QC mechanisms may operate to sense faulty mature NPCs to facilitate their repair or removal. The so far uncovered mechanisms for NPC QC provide fertile ground for future research that not only benefits a better understanding of the vital role that NPCs play in cellular physiology but also how loss of NPC function and/or these QC mechanisms might be an input to aging and disease.

Autophagy as a caretaker of nuclear integrity

Due to their essential functions, dysregulation of nuclear pore complexes (NPCs) is strongly associated with numerous human diseases, including neurodegeneration and cancer. On a cellular level, longevity of scaffold nucleoporins in postmitotic cells of both C. elegans and mammals renders them vulnerable to age-related damage, which is associated with an increase in pore leakiness and accumulation of intranuclear aggregates in rat brain cells. Thus, understanding the mechanisms which underpin the homeostasis of this complex, as well as other nuclear proteins, is essential. In this review, autophagy-mediated degradation pathways governing nuclear components in yeast will be discussed, with a particular focus on NPCs. Furthermore, the various nuclear degradation mechanisms identified thus far in diverse eukaryotes will also be highlighted.

Pragmatic Coarse-Graining of Proteins: Models and Applications

The molecular details involved in the folding, dynamics, organization, and interaction of proteins with other molecules are often difficult to assess by experimental techniques. Consequently, computational models play an ever-increasing role in the field. However, biological processes involving large-scale protein assemblies or long time scale dynamics are still computationally expensive to study in atomistic detail. For these applications, employing coarse-grained (CG) modeling approaches has become a key strategy. In this Review, we provide an overview of what we call pragmatic CG protein models, which are strategies combining, at least in part, a physics-based implementation and a top-down experimental approach to their parametrization. In particular, we focus on CG models in which most protein residues are represented by at least two beads, allowing these models to retain some degree of chemical specificity. A description of the main modern pragmatic protein CG models is provided, including a review of the most recent applications and an outlook on future perspectives in the field.

Nucleoporin downregulation modulates progenitor differentiation independent of nuclear pore numbers

Nucleoporins (NUPs) comprise nuclear pore complexes, gateways for nucleocytoplasmic transport. As primary human keratinocytes switch from the progenitor state towards differentiation, most NUPs are strongly downregulated, with NUP93 being the most downregulated NUP in this process. To determine if this NUP downregulation is accompanied by a reduction in nuclear pore numbers, we leveraged Stochastic Optical Reconstruction Microscopy. No significant changes in nuclear pore numbers were detected using three independent NUP antibodies; however, NUP reduction in other subcellular compartments such as the cytoplasm was identified. To investigate how NUP reduction influences keratinocyte differentiation, we knocked down NUP93 in keratinocytes in the progenitor-state culture condition. NUP93 knockdown diminished keratinocytes' clonogenicity and epidermal regenerative capacity, without drastically affecting nuclear pore numbers or permeability. Using transcriptome profiling, we identified that NUP93 knockdown induces differentiation genes related to both mechanical and immune barrier functions, including the activation of known NF-κB target genes. Consistently, keratinocytes with NUP93 knockdown exhibited increased nuclear localization of the NF-κB p65/p50 transcription factors, and increased NF-κB reporter activity. Taken together, these findings highlight the gene regulatory roles contributed by differential NUP expression levels in keratinocyte differentiation, independent of nuclear pore numbers.

Challenges in bridging the gap between protein structure prediction and functional interpretation

The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is essential to note that they are not validated and cannot be considered the ground truth. Thus, challenges persist, particularly in capturing protein dynamics, predicting multi-chain structures, interpreting protein function, and assessing model quality. Interdisciplinary collaborations are crucial to overcoming these obstacles. Databases like the AlphaFold Protein Structure Database, the ESM Metagenomic Atlas, and initiatives like the 3D-Beacons Network provide FAIR access to these data, enabling their interpretation and application across a broader scientific community. Whilst substantial advancements have been made in protein structure prediction, further progress is required to address the remaining challenges. Developing training materials, nurturing collaborations, and ensuring open data sharing will be paramount in this pursuit. The continued evolution of these tools and methodologies will deepen our understanding of protein function and accelerate disease pathogenesis and drug development discoveries.

The Protein Structure Prediction Revolution and Its Implications for Medicine: 2023 Albert Lasker Basic Medical Research Award

In this Viewpoint, 2023 Lasker award winners John Jumper and Demis Hassabis describe their invention, the artificial intelligence–based system AlphaFold, which is able to predict protein structure with great accuracy.

Unveiling the complexity: assessing models describing the structure and function of the nuclear pore complex

The nuclear pore complex (NPC) serves as a pivotal subcellular structure, acting as a gateway that orchestrates nucleocytoplasmic transport through a selectively permeable barrier. Nucleoporins (Nups), particularly those containing phenylalanine-glycine (FG) motifs, play indispensable roles within this barrier. Recent advancements in technology have significantly deepened our understanding of the NPC's architecture and operational intricacies, owing to comprehensive investigations. Nevertheless, the conspicuous presence of intrinsically disordered regions within FG-Nups continues to present a formidable challenge to conventional static characterization techniques. Historically, a multitude of strategies have been employed to unravel the intricate organization and behavior of FG-Nups within the NPC. These endeavors have given rise to multiple models that strive to elucidate the structural layout and functional significance of FG-Nups. Within this exhaustive review, we present a comprehensive overview of these prominent models, underscoring their proposed dynamic and structural attributes, supported by pertinent research. Through a comparative analysis, we endeavor to shed light on the distinct characteristics and contributions inherent in each model. Simultaneously, it remains crucial to acknowledge the scarcity of unequivocal validation for any of these models, as substantiated by empirical evidence.

Simulating structurally variable nuclear pore complexes for microscopy

MOTIVATION

The nuclear pore complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and an important reference standard in microscopy: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudoatomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single-molecule localization microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However, the true picture of this variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure.

RESULTS

We introduce CIR4MICS ('ceramics', Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. Further, we provide annotated reference datasets of simulated human NPCs, which facilitate a side-by-side comparison with real data. To demonstrate, we synthetically replicate a geometric analysis of real NPC radii and reveal that a range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to test the capabilities of image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies.

AVAILABILITY AND IMPLEMENTATION

Code: https://github.com/uhlmanngroup/cir4mics. Simulated data: BioStudies S-BSST1058.

RANBP2 evolution and human disease

Ran-binding protein 2 (RANBP2)/Nup358 is a nucleoporin and a key component of the nuclear pore complex. Through its multiple functions (e.g., SUMOylation, regulation of nucleocytoplasmic transport) and subcellular localizations (e.g., at the nuclear envelope, kinetochores, annulate lamellae), it is involved in many cellular processes. RANBP2 dysregulation or mutation leads to the development of human pathologies, such as acute necrotizing encephalopathy 1, cancer, neurodegenerative diseases, and it is also involved in viral infections. The chromosomal region containing the RANBP2 gene is highly dynamic, with high structural variation and recombination events that led to the appearance of a gene family called RANBP2 and GCC2 Protein Domains (RGPD), with multiple gene loss/duplication events during ape evolution. Although RGPD homoplasy and maintenance during evolution suggest they might confer an advantage to their hosts, their functions are still unknown and understudied. In this review, we discuss the appearance and importance of RANBP2 in metazoans and its function-related pathologies, caused by an alteration of its expression levels (through promotor activity, post-transcriptional, or post-translational modifications), its localization, or genetic mutations.

The magnified view: from ancient trinkets to single nuclear pore complexes

A journey from the earliest known use of lenses and magnifying glasses in ancient times, through the development of microscopes and towards modern electron microscopy techniques. The evolving technology and improved microscopes enabled the discovery of intracellular organelles, the nucleus and nuclear pore complexes (NPCs). Current advances have led to composite three-dimensional models showing NPC structure in unprecedented detail but relying on the averaging of many images. A complementary approach is field emission scanning electron microscopy providing topographic surface images that are easily and intuitively interpreted by our brain. Recent advances in this technique have made it possible to expose nuclei from human cells and to focus on individual NPCs and their architectural features.

Coordinating nucleoporin condensation and nuclear pore complex assembly

The nuclear pore complex (NPC) is among the most elaborate protein complexes in eukaryotes. While ribosomes and proteasomes are known to require dedicated assembly machinery, our understanding of NPC assembly is at a relatively early stage. Defects in NPC assembly or homeostasis are tied to movement disorders, including dystonia and amyotrophic lateral sclerosis (ALS), as well as aging, requiring a better understanding of these processes to enable therapeutic intervention. Here, we discuss recent progress in the understanding of NPC assembly and highlight how related defects in human disorders can shed light on NPC biogenesis. We propose that the condensation of phenylalanine-glycine repeat nucleoporins needs to be carefully controlled during NPC assembly to prevent aberrant condensation, aggregation, or amyloid formation.

A Structural Model for the Core Nup358-BicD2 Interface

Dynein motors facilitate the majority of minus-end-directed transport events on microtubules. The dynein adaptor Bicaudal D2 (BicD2) recruits the dynein machinery to several cellular cargo for transport, including Nup358, which facilitates a nuclear positioning pathway that is essential for the differentiation of distinct brain progenitor cells. Previously, we showed that Nup358 forms a "cargo recognition α-helix" upon binding to BicD2; however, the specifics of the BicD2-Nup358 interface are still not well understood. Here, we used AlphaFold2, complemented by two additional docking programs (HADDOCK and ClusPro) as well as mutagenesis, to show that the Nup358 cargo-recognition α-helix binds to BicD2 between residues 747 and 774 in an anti-parallel manner, forming a helical bundle. We identified two intermolecular salt bridges that are important to stabilize the interface. In addition, we uncovered a secondary interface mediated by an intrinsically disordered region of Nup358 that is directly N-terminal to the cargo-recognition α-helix and binds to BicD2 between residues 774 and 800. This is the same BicD2 domain that binds to the competing cargo adapter Rab6, which is important for the transport of Golgi-derived and secretory vesicles. Our results establish a structural basis for cargo recognition and selection by the dynein adapter BicD2, which facilitates transport pathways that are important for brain development.

Machine learning for cross-scale microscopy of viruses

Despite advances in virological sciences and antiviral research, viruses continue to emerge, circulate, and threaten public health. We still lack a comprehensive understanding of how cells and individuals remain susceptible to infectious agents. This deficiency is in part due to the complexity of viruses, including the cell states controlling virus-host interactions. Microscopy samples distinct cellular infection stages in a multi-parametric, time-resolved manner at molecular resolution and is increasingly enhanced by machine learning and deep learning. Here we discuss how state-of-the-art artificial intelligence (AI) augments light and electron microscopy and advances virological research of cells. We describe current procedures for image denoising, object segmentation, tracking, classification, and super-resolution and showcase examples of how AI has improved the acquisition and analyses of microscopy data. The power of AI-enhanced microscopy will continue to help unravel virus infection mechanisms, develop antiviral agents, and improve viral vectors.

Preparing Arabidopsis thaliana root protoplasts for cryo electron tomography

The use of protoplasts in plant biology has become a convenient tool for the application of transient gene expression. This model system has allowed the study of plant responses to biotic and abiotic stresses, protein location and trafficking, cell wall dynamics, and single-cell transcriptomics, among others. Although well-established protocols for isolating protoplasts from different plant tissues are available, they have never been used for studying plant cells using cryo electron microscopy (cryo-EM) and cryo electron tomography (cryo-ET). Here we describe a workflow to prepare root protoplasts from Arabidopsis thaliana plants for cryo-ET. The process includes protoplast isolation and vitrification on EM grids, and cryo-focused ion beam milling (cryo-FIB), with the aim of tilt series acquisition. The whole workflow, from growing the plants to the acquisition of the tilt series, may take a few months. Our protocol provides a novel application to use plant protoplasts as a tool for cryo-ET.

Fellowship of two rings: Unprecedented insights into the structure of the yeast nuclear pore complex

Akey et al.1 use complementary experimental approaches and AI-based structure prediction to reveal new details of the structure of the yeast nuclear pore complex, providing key insights into evolution, assembly, and nucleocytoplasmic transport mechanisms.

Implications of a multiscale structure of the yeast nuclear pore complex

Nuclear pore complexes (NPCs) direct the nucleocytoplasmic transport of macromolecules. Here, we provide a composite multiscale structure of the yeast NPC, based on improved 3D density maps from cryogenic electron microscopy and AlphaFold2 models. Key features of the inner and outer rings were integrated into a comprehensive model. We resolved flexible connectors that tie together the core scaffold, along with equatorial transmembrane complexes and a lumenal ring that anchor this channel within the pore membrane. The organization of the nuclear double outer ring reveals an architecture that may be shared with ancestral NPCs. Additional connections between the core scaffold and the central transporter suggest that under certain conditions, a degree of local organization is present at the periphery of the transport machinery. These connectors may couple conformational changes in the scaffold to the central transporter to modulate transport. Collectively, this analysis provides insights into assembly, transport, and NPC evolution.

Super-resolution microscopy reveals focal organization of ER-associated Y-complexes in mitosis

During mitotic entry of vertebrate cells, nuclear pore complexes (NPCs) are rapidly disintegrated. NPC disassembly is initiated by hyperphosphorylation of linker nucleoporins (Nups), which leads to the dissociation of FG repeat Nups and relaxation of the nuclear permeability barrier. However, less is known about disintegration of the huge nuclear and cytoplasmic rings, which are formed by annular assemblies of Y-complexes that are dissociated from NPCs as intact units. Surprisingly, we observe that Y-complex Nups display slower dissociation kinetics compared with other Nups during in vitro NPC disassembly, indicating a mechanistic difference in the disintegration of Y-based rings. Intriguingly, biochemical experiments reveal that a fraction of Y-complexes remains associated with mitotic ER membranes, supporting recent microscopic observations. Visualization of mitotic Y-complexes by super-resolution microscopy demonstrates that they form two classes of higher order assemblies: large clusters at kinetochores and small, focal ER-associated assemblies. These, however, lack features qualifying them as persisting ring-shaped subassemblies previously proposed to serve as structural templates for NPC reassembly during mitotic exit, which helps to refine current models of nuclear reassembly.

The impact of AlphaFold Protein Structure Database on the fields of life sciences

Arguably, 2020 was the year of high-accuracy protein structure predictions, with AlphaFold 2.0 achieving previously unseen accuracy in the Critical Assessment of Protein Structure Prediction (CASP). In 2021, DeepMind and EMBL-EBI developed the AlphaFold Protein Structure Database to make an unprecedented number of reliable protein structure predictions easily accessible to the broad scientific community. We provide a brief overview and describe the latest developments in the AlphaFold database. We highlight how the fields of data services, bioinformatics, structural biology, and drug discovery are directly affected by the influx of protein structure data. We also show examples of cutting-edge research that took advantage of the AlphaFold database. It is apparent that connections between various fields through protein structures are now possible, but the amount of data poses new challenges. Finally, we give an outlook regarding the future direction of the database, both in terms of data sets and new functionalities.

Enabling cryo-EM density interpretation from yeast native cell extracts by proteomics data and AlphaFold structures

In the cellular context, proteins participate in communities to perform their function. The detection and identification of these communities as well as in-community interactions has long been the subject of investigation, mainly through proteomics analysis with mass spectrometry. With the advent of cryogenic electron microscopy and the "resolution revolution," their visualization has recently been made possible, even in complex, native samples. The advances in both fields have resulted in the generation of large amounts of data, whose analysis requires advanced computation, often employing machine learning approaches to reach the desired outcome. In this work, we first performed a robust proteomics analysis of mass spectrometry (MS) data derived from a yeast native cell extract and used this information to identify protein communities and inter-protein interactions. Cryo-EM analysis of the cell extract provided a reconstruction of a biomolecule at medium resolution (∼8 Å (FSC = 0.143)). Utilizing MS-derived proteomics data and systematic fitting of AlphaFold-predicted atomic models, this density was assigned to the 2.6 MDa complex of yeast fatty acid synthase. Our proposed workflow identifies protein complexes in native cell extracts from Saccharomyces cerevisiae by combining proteomics, cryo-EM, and AI-guided protein structure prediction.

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

Single particle cryo-electron microscopy (cryo-EM) has emerged as the prevailing method for near-atomic structure determination, shedding light on the important molecular mechanisms of biological macromolecules. However, the inherent dynamics and structural variability of biological complexes coupled with the large number of experimental images generated by a cryo-EM experiment make data processing nontrivial. In particular, ab initio reconstruction and atomic model building remain major bottlenecks that demand substantial computational resources and manual intervention. Approaches utilizing recent innovations in artificial intelligence (AI) technology, particularly deep learning, have the potential to overcome the limitations that cannot be adequately addressed by traditional image processing approaches. Here, we review newly proposed AI-based methods for ab initio volume generation, heterogeneous 3D reconstruction, and atomic model building. We highlight the advancements made by the implementation of AI methods, as well as discuss remaining limitations and areas for future development.

DoubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models

Sequence assignment is a key step of the model building process in both cryogenic electron microscopy (cryo-EM) and macromolecular crystallography (MX). If the assignment fails, it can result in difficult to identify errors affecting the interpretation of a model. There are many model validation strategies that help experimentalists in this step of protein model building, but they are virtually non-existent for nucleic acids. Here, I present doubleHelix-a comprehensive method for assignment, identification, and validation of nucleic acid sequences in structures determined using cryo-EM and MX. The method combines a neural network classifier of nucleobase identities and a sequence-independent secondary structure assignment approach. I show that the presented method can successfully assist sequence-assignment step in nucleic-acid model building at lower resolutions, where visual map interpretation is very difficult. Moreover, I present examples of sequence assignment errors detected using doubleHelix in cryo-EM and MX structures of ribosomes deposited in the Protein Data Bank, which escaped the scrutiny of available model-validation approaches. The doubleHelix program source code is available under BSD-3 license at https://gitlab.com/gchojnowski/doublehelix.

The enigmatic nucleus of the marine dinoflagellate Prorocentrum cordatum

The marine, bloom-forming dinoflagellate Prorocentrum cordatum CCMP 1329 (formerly P. minimum) has a genome atypical of eukaryotes, with a large size of ~4.15 Gbp, organized in plentiful, highly condensed chromosomes and packed in a dinoflagellate-specific nucleus (dinokaryon). Here, we apply microscopic and proteogenomic approaches to obtain new insights into this enigmatic nucleus of axenic P. cordatum. High-resolution focused ion beam/scanning electron microscopy analysis of the flattened nucleus revealed highest density of nuclear pores in the vicinity of the nucleolus, a total of 62 tightly packed chromosomes (~0.4-6.7 µm3), and interaction of several chromosomes with the nucleolus and other nuclear structures. A specific procedure for enriching intact nuclei was developed to enable proteomic analyses of soluble and membrane protein-enriched fractions. These were analyzed with geLC and shotgun approaches employing ion-trap and timsTOF (trapped-ion-mobility-spectrometry time-of-flight) mass spectrometers, respectively. This allowed identification of 4,052 proteins (39% of unknown function), out of which 418 were predicted to serve specific nuclear functions; additional 531 proteins of unknown function could be allocated to the nucleus. Compaction of DNA despite very low histone abundance could be accomplished by highly abundant major basic nuclear proteins (HCc2-like). Several nuclear processes including DNA replication/repair and RNA processing/splicing can be fairly well explained on the proteogenomic level. By contrast, transcription and composition of the nuclear pore complex remain largely elusive. One may speculate that the large group of potential nuclear proteins with currently unknown functions may serve yet to be explored functions in nuclear processes differing from those of typical eukaryotic cells. IMPORTANCE Dinoflagellates form a highly diverse group of unicellular microalgae. They provide keystone species for the marine ecosystem and stand out among others by their very large, unusually organized genomes embedded in the nuclei markedly different from other eukaryotic cells. Functional insights into nuclear and other cell biological structures and processes of dinoflagellates have long been hampered by the paucity of available genomic sequences. The here studied cosmopolitan P. cordatum belongs to the harmful algal bloom-forming, marine dinoflagellates and has a recently de novo assembled genome. We present a detailed 3D reconstruction of the P. cordatum nucleus together with comprehensive proteogenomic insights into the protein equipment mastering the broad spectrum of nuclear processes. This study significantly advances our understanding of mechanisms and evolution of the conspicuous dinoflagellate cell biology.

Particle fusion of super-resolution data reveals the unit structure of Nup96 in Nuclear Pore Complex

Single molecule localization microscopy offers resolution nearly down to the molecular level with specific molecular labelling, and is thereby a promising tool for structural biology. In practice, however, the actual value to this field is limited primarily by incomplete fluorescent labelling of the structure. This missing information can be completed by merging information from many structurally identical particles in a particle fusion approach similar to cryo-EM single-particle analysis. In this paper, we present a data analysis of particle fusion results of fluorescently labelled Nup96 nucleoporins in the Nuclear Pore Complex to show that Nup96 occurs in a spatial arrangement of two rings of 8 units with two Nup96 copies per unit giving a total of 32 Nup96 copies per pore. We use Artificial Intelligence assisted modeling in Alphafold to extend the existing cryo-EM model of Nup96 to accurately pinpoint the positions of the fluorescent labels and show the accuracy of the match between fluorescent and cryo-EM data to be better than 3 nm in-plane and 5 nm out-of-plane.

Bayesian methods in integrative structure modeling

There is a growing interest in characterizing the structure and dynamics of large biomolecular assemblies and their interactions within the cellular environment. A diverse array of experimental techniques allows us to study biomolecular systems on a variety of length and time scales. These techniques range from imaging with light, X-rays or electrons, to spectroscopic methods, cross-linking mass spectrometry and functional genomics approaches, and are complemented by AI-assisted protein structure prediction methods. A challenge is to integrate all of these data into a model of the system and its functional dynamics. This review focuses on Bayesian approaches to integrative structure modeling. We sketch the principles of Bayesian inference, highlight recent applications to integrative modeling and conclude with a discussion of current challenges and future perspectives.

Interaction of nucleoporins with nuclear transport receptors: a structural perspective

Soluble nuclear transport receptors and stationary nucleoporins are at the heart of the nucleocytoplasmic transport machinery. A subset of nucleoporins contains characteristic and repetitive FG (phenylalanine-glycine) motifs, which are the basis for the permeability barrier of the nuclear pore complex (NPC) that controls transport of macromolecules between the nucleus and the cytoplasm. FG-motifs can interact with each other and/or with transport receptors, mediating their translocation across the NPC. The molecular details of homotypic and heterotypic FG-interactions have been analyzed at the structural level. In this review, we focus on the interactions of nucleoporins with nuclear transport receptors. Besides the conventional FG-motifs as interaction spots, a thorough structural analysis led us to identify additional similar motifs at the binding interface between nucleoporins and transport receptors. A detailed analysis of all known human nucleoporins revealed a large number of such phenylalanine-containing motifs that are not buried in the predicted 3D-structure of the respective protein but constitute part of the solvent-accessible surface area. Only nucleoporins that are rich in conventional FG-repeats are also enriched for these motifs. This additional layer of potential low-affinity binding sites on nucleoporins for transport receptors may have a strong impact on the interaction of transport complexes with the nuclear pore and, thus, the efficiency of nucleocytoplasmic transport.

Application of Super-resolution SPEED Microscopy in the Study of Cellular Dynamics

Super-resolution imaging techniques have broken the diffraction-limited resolution of light microscopy. However, acquiring three-dimensional (3D) super-resolution information about structures and dynamic processes in live cells at high speed remains challenging. Recently, the development of high-speed single-point edge-excitation subdiffraction (SPEED) microscopy, along with its 2D-to-3D transformation algorithm, provides a practical and effective approach to achieving 3D subdiffraction-limit information in subcellular structures and organelles with rotational symmetry. One of the major benefits of SPEED microscopy is that it does not rely on complex optical components and can be implemented on a standard, inverted epifluorescence microscope, simplifying the process of sample preparation and the expertise requirement. SPEED microscopy is specifically designed to obtain 2D spatial locations of individual immobile or moving fluorescent molecules inside submicrometer biological channels or cavities at high spatiotemporal resolution. The collected data are then subjected to postlocalization 2D-to-3D transformation to obtain 3D super-resolution structural and dynamic information. In recent years, SPEED microscopy has provided significant insights into nucleocytoplasmic transport across the nuclear pore complex (NPC) and cytoplasm-cilium trafficking through the ciliary transition zone. This Review focuses on the applications of SPEED microscopy in studying the structure and function of nuclear pores.

Translation dynamics in human cells visualized at high resolution reveal cancer drug action

Ribosomes catalyze protein synthesis by cycling through various functional states. These states have been extensively characterized in vitro, but their distribution in actively translating human cells remains elusive. We used a cryo-electron tomography-based approach and resolved ribosome structures inside human cells with high resolution. These structures revealed the distribution of functional states of the elongation cycle, a Z transfer RNA binding site, and the dynamics of ribosome expansion segments. Ribosome structures from cells treated with Homoharringtonine, a drug used against chronic myeloid leukemia, revealed how translation dynamics were altered in situ and resolve the small molecules within the active site of the ribosome. Thus, structural dynamics and drug effects can be assessed at high resolution within human cells.

Automated identification of chalcogen bonds in AlphaFold protein structure database files: is it possible?

Protein structure prediction and structural biology have entered a new era with an artificial intelligence-based approach encoded in the AlphaFold2 and the analogous RoseTTAfold methods. More than 200 million structures have been predicted by AlphaFold2 from their primary sequences and the models as well as the approach itself have naturally been examined from different points of view by experimentalists and bioinformaticians. Here, we assessed the degree to which these computational models can provide information on subtle structural details with potential implications for diverse applications in protein engineering and chemical biology and focused the attention on chalcogen bonds formed by disulphide bridges. We found that only 43% of the chalcogen bonds observed in the experimental structures are present in the computational models, suggesting that the accuracy of the computational models is, in the majority of the cases, insufficient to allow the detection of chalcogen bonds, according to the usual stereochemical criteria. High-resolution experimentally derived structures are therefore still necessary when the structure must be investigated in depth based on fine structural aspects.

AlphaFold and the future of structural biology

This editorial acknowledges the transformative impact of new machine-learning methods, such as the use of AlphaFold, but also makes the case for the continuing need for experimental structural biology.

Structural biology: A golden era

In the past 2 decades, structural biology has transformed from a single technique used on single proteins to a multimodal integrative approach. Recently, protein structure prediction algorithms have opened new avenues to address challenging biological questions.

Distinct domains in Ndc1 mediate its interaction with the Nup84 complex and the nuclear membrane

Nuclear pore complexes (NPCs) are embedded in the nuclear envelope and built from ∼30 different nucleoporins (Nups) in multiple copies, few are integral membrane proteins. One of these transmembrane nucleoporins, Ndc1, is thought to function in NPC assembly at the fused inner and outer nuclear membranes. Here, we show a direct interaction of Ndc1's transmembrane domain with Nup120 and Nup133, members of the pore membrane coating Y-complex. We identify an amphipathic helix in Ndc1's C-terminal domain binding highly curved liposomes. Upon overexpression, this amphipathic motif is toxic and dramatically alters the intracellular membrane organization in yeast. Ndc1's amphipathic motif functionally interacts with related motifs in the C-terminus of the nucleoporins Nup53 and Nup59, important for pore membrane binding and interconnecting NPC modules. The essential function of Ndc1 can be suppressed by deleting the amphipathic helix from Nup53. Our data indicate that nuclear membrane and presumably NPC biogenesis depends on a balanced ratio between amphipathic motifs in diverse nucleoporins.

Nuclear export of pre-60S particles through the nuclear pore complex

The nuclear pore complex (NPC) is the bidirectional gate that mediates the exchange of macromolecules or their assemblies between nucleus and cytoplasm^1-3. The assembly intermediates of the ribosomal subunits, pre-60S and pre-40S particles, are among the largest cargoes of the NPC and the export of these gigantic ribonucleoproteins requires numerous export factors^4,5. Here we report the cryo-electron microscopy structure of native pre-60S particles trapped in the channel of yeast NPCs. In addition to known assembly factors, multiple factors with export functions are also included in the structure. These factors in general bind to either the flexible regions or subunit interface of the pre-60S particle, and virtually form many anchor sites for NPC binding. Through interactions with phenylalanine-glycine (FG) repeats from various nucleoporins of NPC, these factors collectively facilitate the passage of the pre-60S particle through the central FG repeat network of the NPC. Moreover, in silico analysis of the axial and radial distribution of pre-60S particles within the NPC shows that a single NPC can take up to four pre-60S particles simultaneously, and pre-60S particles are enriched in the inner ring regions close to the wall of the NPC with the solvent-exposed surface facing the centre of the nuclear pore. Our data suggest a translocation model for the export of pre-60S particles through the NPC.

TomoTwin: generalized 3D localization of macromolecules in cryo-electron tomograms with structural data mining

Cryogenic-electron tomography enables the visualization of cellular environments in extreme detail, however, tools to analyze the full amount of information contained within these densely packed volumes are still needed. Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ratio and crowding of the cellular space. Available methods for this task suffer either from being error prone or requiring manual annotation of training data. To assist in this crucial particle picking step, we present TomoTwin: an open source general picking model for cryogenic-electron tomograms based on deep metric learning. By embedding tomograms in an information-rich, high-dimensional space that separates macromolecules according to their three-dimensional structure, TomoTwin allows users to identify proteins in tomograms de novo without manually creating training data or retraining the network to locate new proteins.

Recent breakthroughs in computational structural biology harnessing the power of sequences and structures

Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.

Bringing Structure to Cell Biology with Cryo-Electron Tomography

Recent advances in cryo-electron microscopy have marked only the beginning of the potential of this technique. To bring structure into cell biology, the modality of cryo-electron tomography has fast developed into a bona fide in situ structural biology technique where structures are determined in their native environment, the cell. Nearly every step of the cryo-focused ion beam-assisted electron tomography (cryo-FIB-ET) workflow has been improved upon in the past decade, since the first windows were carved into cells, unveiling macromolecular networks in near-native conditions. By bridging structural and cell biology, cryo-FIB-ET is advancing our understanding of structure-function relationships in their native environment and becoming a tool for discovering new biology.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.