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

Recent progress and future challenges in structure-based protein-protein interaction prediction

Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches. We further discuss the expanding biomedical applications of PPI research, including the elucidation of disease mechanisms, drug discovery, and therapeutic design. Despite these advances, significant challenges remain in predicting host-pathogen interactions, interactions between intrinsically disordered regions, and interactions related to immune responses. These challenges are worthwhile for future explorations and represent the frontier of research in this field.

Microtubule-mediated defence reaction of grapevine to Neofusicoccum parvum via the transcription factor VrWRKY22 promoting the kinesin-like protein VrKIN10C

Grapevine Trunk Diseases (GTDs) are among the most destructive diseases in viticulture due to global climate change. Some causal agents like Neofusicoccum parvum can be latent endophytic and become pathogenic under abiotic stress. Microtubules (MTs) have been found to play a role in mediating the pathogen-related signaling in grapevine. In this study, a novel transcription factor VrWRKY22 was identified and cloned from the native American grapevine Vitis rupestris. Leaves of the table grape variety 'Kyoho' (V. vinifera × V. labrusca L.) overexpressing VrWRKY22 showed less necroses after N. parvum Bt-67 inoculation and activated signaling pathways. VrWRKY22 interacted with VrMPK3 and then bounded to the TTGACC motif in the promoter of VrKIN10C, which was confirmed by Y2H and Y1H assays. Since VrKIN10C is one of the important kinesin-like proteins associated with MTs, a grapevine MT marker line overexpressing VrWRKY22 was generated to test the responses of grapevine cells to N. parvum Bt-67. An increased number of prompt movement proteins can be traced within the peri-nuclear MTs and along the cortical MTs. The skewness and thickness of both central and cortical MTs were significantly increased. Moreover, a prominent (resulting from both the number and the rate) accumulation of speckles appeared in the nucleus and cortical MTs. A significant reduction in cell mortality and a stronger antioxidant capacity were detected. This study demonstrates that VrWRKY22 plays positive roles during N. parvum Bt-67 invasion by rapidly increasing the concentration and dynamics of MTs in the peri-nuclear and cortical regions via VrKIN10, and will facilitate the interpretation of the results of further GTD mitigation studies.

Nuclear pores safeguard the integrity of the nuclear envelope

Nuclear pore complexes (NPCs) mediate nucleocytoplasmic exchange, which is essential for eukaryotes. Mutations in the central scaffolding components of NPCs are associated with genetic diseases, but how they manifest only in specific tissues remains unclear. This is exemplified in Nup133-deficient mouse embryonic stem cells, which grow normally during pluripotency, but differentiate poorly into neurons. Here, using an innovative in situ structural biology approach, we show that Nup133-/- mouse embryonic stem cells have heterogeneous NPCs with non-canonical symmetries and missing subunits. During neuronal differentiation, Nup133-deficient NPCs frequently disintegrate, resulting in abnormally large nuclear envelope openings. We propose that the elasticity of the NPC scaffold has a protective function for the nuclear envelope and that its perturbation becomes critical under conditions that impose an increased mechanical load onto nuclei.

Advancing Molecular Simulations: Merging Physical Models, Experiments, and AI to Tackle Multiscale Complexity

Proteins and protein complexes form adaptable networks that regulate essential biochemical pathways and define cell phenotypes through dynamic mechanisms and interactions. Advances in structural biology and molecular simulations have revealed how protein systems respond to changes in their environments, such as ligand binding, stress conditions, or perturbations like mutations and post-translational modifications, influencing signal transduction and cellular phenotypes. Here, we discuss how computational approaches, ranging from molecular dynamics (MD) simulations to AI-driven methods, are instrumental in studying protein dynamics from isolated molecules to large assemblies. These techniques elucidate conformational landscapes, ligand-binding mechanisms, and protein-protein interactions and are starting to support the construction of multiscale realistic representations of highly complex systems, ranging up to whole cell models. With cryo-electron microscopy, cryo-electron tomography, and AlphaFold accelerating the structural characterization of protein networks, we suggest that integrating AI and Machine Learning with multiscale MD methods will enhance fundamental understating for systems of ever-increasing complexity, usher in exciting possibilities for predictive modeling of the behavior of cell compartments or even whole cells. These advances are indeed transforming biophysics and chemical biology, offering new opportunities to study biomolecular mechanisms at atomic resolution.

Emerging frontiers in protein structure prediction following the AlphaFold revolution

Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated in a very short space of time through a revolution in protein structure prediction driven by deep learning, led by AlphaFold. This has provided a wealth of new structural information. Interpreting these predictions is critical to determining where and when this information is useful. But proteins are not static nor do they act alone, and structures of proteins interacting with other proteins and other biomolecules are critical to a complete understanding of their biological function at the molecular level. This review focuses on the application of state-of-the-art protein structure prediction to these advanced applications. We also suggest a set of guidelines for reporting AlphaFold predictions.

Targeting colorectal cancer at the level of nuclear pore complex

BACKGROUND

Nuclear pore complexes (NPCs) are the architectures entrenched in nuclear envelop of a cell that regulate the nucleo-cytoplasmic transportation of materials, such as proteins and RNAs for proper functioning of a cell. The appropriate localization of proteins and RNAs within the cell is essential for its normal functionality. For such a complex transportation of materials across the NPC, around 60 proteins are involved comprising nucleoporins, karyopherins and RAN system proteins that play a vital role in NPC's structure formation, cargo translocation across NPC, and cargoes' rapid directed transportation respectively. In various cancers, the structure and function of NPC is often exaggerated, following altered expressions of its nucleoporins and karyopherins, affecting other proteins of associated signaling pathways. Some inhibitors of karyopherins at present, have potential to regulate the altered level/expression of these karyopherin molecules.

AIM OF REVIEW

This review summarizes the data from 1990 to 2023, mainly focusing on recent studies that illustrate the structure and function of NPC, the relationship and mechanisms of nucleoporins and karyopherins with colorectal cancer, as well as therapeutic values, in order to understand the pathology and underlying basis of colorectal cancer associated with NPC. This is the first review to our knowledge elucidating the detailed updated studies targeting colorectal cancer at NPC. The review also aims to target certain karyopherins, Nups and their possible inhibitors and activators molecules as a therapeutic strategy.

KEY SCIENTIFIC CONCEPTS OF REVIEW

NPC structure provides understanding, how nucleoporins and karyopherins as key molecules are responsible for appropriate nucleocytoplasmic transportation. Many studies provide evidences, describing the role of disrupted nucleoporins and karyopherins not only in CRC but also in other non-hematological and hematological malignancies. At present, some inhibitors of karyopherins have therapeutic potential for CRC, however development of more potent inhibitors may provide more effective therapeutic strategies for CRC in near future.

IgStrand: A universal residue numbering scheme for the immunoglobulin-fold (Ig-fold) to study Ig-proteomes and Ig-interactomes

The Immunoglobulin fold (Ig-fold) is found in proteins from all domains of life and represents the most populous fold in the human genome, with current estimates ranging from 2 to 3% of protein coding regions. That proportion is much higher in the surfaceome where Ig and Ig-like domains orchestrate cell-cell recognition, adhesion and signaling. The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. We define a universal residue numbering scheme, common to all domains sharing the Ig-fold in order to study the wide spectrum of Ig-domain variants constituting the Ig-proteome and Ig-Ig interactomes at the heart of these systems. The "IgStrand numbering scheme" enables the identification of Ig structural proteomes and interactomes in and between any species, and comparative structural, functional, and evolutionary analyses. We review how Ig-domains are classified today as topological and structural variants and highlight the "Ig-fold irreducible structural signature" shared by all of them. The IgStrand numbering scheme lays the foundation for the systematic annotation of structural proteomes by detecting and accurately labeling Ig-, Ig-like and Ig-extended domains in proteins, which are poorly annotated in current databases and opens the door to accurate machine learning. Importantly, it sheds light on the robust Ig protein folding algorithm used by nature to form beta sandwich supersecondary structures. The numbering scheme powers an algorithm implemented in the interactive structural analysis software iCn3D to systematically recognize Ig-domains, annotate them and perform detailed analyses comparing any domain sharing the Ig-fold in sequence, topology and structure, regardless of their diverse topologies or origin. The scheme provides a robust fold detection and labeling mechanism that reveals unsuspected structural homologies among protein structures beyond currently identified Ig- and Ig-like domain variants. Indeed, multiple folds classified independently contain a common structural signature, in particular jelly-rolls. Examples of folds that harbor an "Ig-extended" architecture are given. Applications in protein engineering around the Ig-architecture are straightforward based on the universal numbering.

Structural Biology of RNA and Protein-RNA Complexes after AlphaFold3

Recent breakthroughs in AI-mediated protein structure prediction have significantly accelerated research and generated valuable hypotheses within the field of structural biology and beyond. Notably, AlphaFold2 has facilitated the determination of larger protein complexes for which only limited experimental data are available. De novo predictions can now be experimentally validated with relative ease compared to the pre-AlphaFold2 era. In May 2024, AlphaFold3 was launched with high expectations, promising the capability to accurately predict RNA structures and protein-RNA complexes - features that were absent in AlphaFold2. This review evaluates the extent to which AlphaFold3 fulfills this promise through specific examples. At present, AlphaFold3 falls short in reliably predicting RNA and protein-RNA complex structures, particularly for non-canonical interactions where training data remain scarce. As a result, users should exercise caution when using AlphaFold3 predictions as hypotheses generators for RNA and protein-RNA complex structures. In the interim, integrating AI-based predictors with data-driven docking tools is recommended to address these limitations. This approach can help bridge the gap until sufficient training data are available to enable the development of more reliable predictive algorithms.

Integrative spatiotemporal modeling of biomolecular processes: Application to the assembly of the nuclear pore complex

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. First, for each time point, a set of coarse models of compositional and structural heterogeneity is computed (heterogeneity models). Second, for each heterogeneity model, a set of static integrative structure models is computed (a snapshot model). Finally, these snapshot models are selected and connected into a series of trajectories that optimize the likelihood of both the snapshot models and transitions between them (a trajectory model). The method is demonstrated by application to the assembly process of the human nuclear pore complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully assembled nuclear pore complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform (IMP) software.

Has AlphaFold 3 Solved the Protein Folding Problem for D-Peptides?

Due to the favorable chemical properties of mirrored chiral centers (such as improved stability, bioavailability, and membrane permeability) the computational design of D-peptides targeting biological L-proteins is a valuable area of research. To design these structures in silico , a computational workflow should correctly dock and fold a peptide while maintaining chiral centers. The latest AlphaFold 3 (AF3) from Abramson et al. (2024) enforces a strict chiral violation penalty to maintain chiral centers from model inputs and is reported to have a low chiral violation rate of only 4.4% on a PoseBusters benchmark containing diverse chiral molecules. Herein, we report the results of 3,255 experiments with AF3 to evaluate its ability to predict the fold, chirality, and binding pose of D-peptides in heterochiral complexes. Despite our inputs specifying explicit D-stereocenters, we report that the AF3 chiral violation for D-peptide binders is much higher at 51% across all evaluated predictions; on average the model is as accurate as chance (random chirality choice, L or D, for each peptide residue). Increasing the number of seeds failed to improve this violation rate. The AF3 predictions exhibit incorrect folds and binding poses, with D-peptides commonly oriented incorrectly in the L-protein binding pocket. Confidence metrics returned by AF3 also fail to distinguish predictions with low chirality violation and correct docking vs. predictions with high chirality violation and incorrect docking. We conclude that AF3 is a poor predictor of D-peptide chirality, fold, and binding pose.

Summary

A crucial task in computational protein design is predicting fold, as this property determines the structure and function of a protein. Abramson et al. 1 published in Nature on AlphaFold 3 (AF3), a powerful deep learning framework for predicting chemical structures in both bound and unbound states. This architecture is tuned to respect chiral centers, which are atoms (in proteins, backbone α -carbons) covalently bound to four different chemical species 2 . These centers adopt two non-superposable forms, often called "handedness," termed L (all biological proteins adopt this form) and D (the mirror image of L). L and D chiral centers exert significant influence on chemical function; changing the chirality of even a single residue can dramatically alter chemical properties such as enantioselective binding (e.g., antifolate resistance 3 ) and stability 4 . Additionally, D-peptides (small proteins containing exclusively D chiral centers) exhibit many advantages compared to their L-peptide counterparts, such as protease evasion 5 , and are therefore therapeutically relevant modalities. Due to vastly differing chemical properties, an algorithm should respect chiral center inputs and exhibit an error rate of 0%. Although Abramson et al. 1 reports a low 4.4% chirality violation across diverse chiral centers, we have found that the chiral violation rate for D-peptides with D chiral center inputs explicitly specified is much higher at 51%. Increasing the number of seeds fails to improve this rate. Our data highlights a crucial structural prediction error in AF3 and demonstrates the model is as accurate on average as chance (random chirality choice, L or D, for each peptide residue). Compared to empirical structures, AF3 is also highly inaccurate when folding and docking D-peptide:L-protein complexes. The failure of AF3 to accurately predict these chemical interactions indicates more work is need for high-quality prediction of D-peptides.

AlphaFold2-Guided Functional Screens Reveal a Conserved Antioxidant Protein at ER Membranes

Oxidative protein folding in the endoplasmic reticulum (ER) is essential for all eukaryotic cells yet generates hydrogen peroxide (H2O2), a reactive oxygen species (ROS). The ER-transmembrane protein that provides reducing equivalents to ER and guards the cytosol for antioxidant defense remains unidentified. Here we combine AlphaFold2-based and functional reporter screens in C. elegans to discover a previously uncharacterized and evolutionarily conserved protein ERGU-1 that fulfills these roles. Deleting C. elegans ERGU-1 causes excessive H2O2 and transcriptional gene up-regulation through SKN-1, homolog of mammalian antioxidant master regulator NRF2. ERGU-1 deficiency also impairs organismal reproduction and behavioral responses to H2O2. Both C. elegans and human ERGU-1 proteins localize to ER membranes and form network reticulum structures. Human and Drosophila homologs of ERGU-1 can rescue C. elegans mutant phenotypes, demonstrating evolutionarily ancient and conserved functions. In addition, purified ERGU-1 and human homolog TMEM161B exhibit redox-modulated oligomeric states. Together, our results reveal an ER-membrane-specific protein machinery for peroxide detoxification and suggest a previously unknown and conserved mechanisms for antioxidant defense in animal cells.

Correlative In Situ Cryo-ET Reveals Cellular and Viral Remodeling Associated with Selective HIV-1 Core Nuclear Import

Lentiviruses like HIV-1 infect non-dividing cells by traversing the nuclear pore, but studying this process has been challenging due to its scarcity and dynamic nature in infected cells. Here, we developed a robust cell-permeabilization system that recapitulates HIV-1 nuclear import and established an integrated cryo-correlative workflow combining cryo-CLEM, cryo-FIB, and cryo-ET for targeted imaging of this process. These advancements enabled the successful capture of 1,899 HIV-1 cores at various stages of nuclear import. Statistical and structural analyses of native wild-type and mutant cores revealed that HIV-1 nuclear import depends on both capsid elasticity and nuclear pore adaptability, as well as nuclear factors such as CPSF6. Brittle cores fail to enter the nuclear pore complex (NPC), while CPSF6-binding-deficient cores stall inside the NPC, resulting in impaired nuclear import. Intriguingly, nuclear pores function as selective filters favoring the import of smaller, tube-shaped cores. Our study opens new avenues for dissecting the biochemistry and structural biology of HIV-1 nuclear import as well as downstream events including core uncoating and potentially integration, with unprecedented detail.

Easy and accurate protein structure prediction using ColabFold

Since its public release in 2021, AlphaFold2 (AF2) has made investigating biological questions, by using predicted protein structures of single monomers or full complexes, a common practice. ColabFold-AF2 is an open-source Jupyter Notebook inside Google Colaboratory and a command-line tool that makes it easy to use AF2 while exposing its advanced options. ColabFold-AF2 shortens turnaround times of experiments because of its optimized usage of AF2's models. In this protocol, we guide the reader through ColabFold best practices by using three scenarios: (i) monomer prediction, (ii) complex prediction and (iii) conformation sampling. The first two scenarios cover classic static structure prediction and are demonstrated on the human glycosylphosphatidylinositol transamidase protein. The third scenario demonstrates an alternative use case of the AF2 models by predicting two conformations of the human alanine serine transporter 2. Users can run the protocol without computational expertise via Google Colaboratory or in a command-line environment for advanced users. Using Google Colaboratory, it takes <2 h to run each procedure. The data and code for this protocol are available at https://protocol.colabfold.com .

CryoSamba: Self-supervised deep volumetric denoising for cryo-electron tomography data

Cryogenic electron tomography (cryo-ET) has rapidly advanced as a high-resolution imaging tool for visualizing subcellular structures in 3D with molecular detail. Direct image inspection remains challenging due to inherent low signal-to-noise ratios (SNR). We introduce CryoSamba, a self-supervised deep learning-based model designed for denoising cryo-ET images. CryoSamba enhances single consecutive 2D planes in tomograms by averaging motion-compensated nearby planes through deep learning interpolation, effectively mimicking increased exposure. This approach amplifies coherent signals and reduces high-frequency noise, substantially improving tomogram contrast and SNR. CryoSamba operates on 3D volumes without needing pre-recorded images, synthetic data, labels or annotations, noise models, or paired volumes. CryoSamba suppresses high-frequency information less aggressively than do existing cryo-ET denoising methods, while retaining real information, as shown both by visual inspection and by Fourier Shell Correlation (FSC) analysis of icosahedrally symmetric virus particles. Thus, CryoSamba enhances the analytical pipeline for direct 3D tomogram visual interpretation.

Passage of the HIV capsid cracks the nuclear pore

Upon infection, human immunodeficiency virus type 1 (HIV-1) releases its cone-shaped capsid into the cytoplasm of infected T cells and macrophages. The capsid enters the nuclear pore complex (NPC), driven by interactions with numerous phenylalanine-glycine (FG)-repeat nucleoporins (FG-Nups). Whether NPCs structurally adapt to capsid passage and whether capsids are modified during passage remains unknown, however. Here, we combined super-resolution and correlative microscopy with cryoelectron tomography and molecular simulations to study the nuclear entry of HIV-1 capsids in primary human macrophages. Our data indicate that cytosolically bound cyclophilin A is stripped off capsids entering the NPC, and the capsid hexagonal lattice remains largely intact inside and beyond the central channel. Strikingly, the NPC scaffold rings frequently crack during capsid passage, consistent with computer simulations indicating the need for NPC widening. The unique cone shape of the HIV-1 capsid facilitates its entry into NPCs and helps to crack their rings.

AI protocol for retrieving protein dynamic structures from two-dimensional infrared spectra

Understanding the dynamic evolution of protein structures is crucial for uncovering their biological functions. Yet, real-time prediction of these dynamic structures remains a significant challenge. Two-dimensional infrared (2DIR) spectroscopy is a powerful tool for analyzing protein dynamics. However, translating its complex, low-dimensional signals into detailed three-dimensional structures is a daunting task. In this study, we introduce a machine learning-based approach that accurately predicts dynamic three-dimensional protein structures from 2DIR descriptors. Our method establishes a robust "spectrum-structure" relationship, enabling the recovery of three-dimensional structures across a wide variety of proteins. It demonstrates broad applicability in predicting dynamic structures along different protein folding trajectories, spanning timescales from microseconds to milliseconds. This approach also shows promise in identifying the structures of previously uncharacterized proteins based solely on their spectral descriptors. The integration of AI with 2DIR spectroscopy offers insights and represents a significant advancement in the real-time analysis of dynamic protein structures.

Integrative mapping reveals molecular features underlying the mechanism of nucleocytoplasmic transport

Nuclear Pore Complexes (NPCs) enable rapid, selective, and robust nucleocytoplasmic transport. To explain how transport emerges from the system components and their interactions, we used experimental data and theoretical information to construct an integrative Brownian dynamics model of transport through an NPC, coupled to a kinetic model of transport in the cell. The model recapitulates key aspects of transport for a wide range of molecular cargos, including pre-ribosomes and viral capsids. It quantifies how flexible phenylalanine-glycine (FG) repeat proteins raise an entropy barrier to passive diffusion and how this barrier is selectively lowered in facilitated diffusion by the many transient interactions of nuclear transport receptors with the FG repeats. Selective transport is enhanced by "fuzzy" multivalent interactions, redundant FG repeats, coupling to the energy-dependent RanGTP concentration gradient, and exponential dependence of transport kinetics on the transport barrier. Our model will facilitate rational modulation of the NPC and its artificial mimics.

Scaling of the GROMACS Molecular Dynamics Code to 65k CPU Cores on an HPC Cluster

We benchmarked the performance of the GROMACS 2024 molecular dynamics (MD) code on a modern high-performance computing (HPC) cluster with AMD CPUs on up to 65,536 CPU cores. We used five different MD systems, ranging in size from about 82,000 to 204 million atoms, and evaluated their performance using two different Message Passing Interface (MPI) libraries, Intel-MPI and Open-MPI. The largest system showed near-perfect strong scaling up to 512 nodes or 65,536 cores, maintaining a parallel efficiency above 0.9 even at the highest level of parallelization. Energy efficiency for a given number of nodes was generally equal to or slightly better than parallel efficiency. We achieved peak performances of 687 ns/d for the 82k atom system, 116 ns/d for the 53M atom system, and about 35 ns/d for the largest 204M atom system. These results demonstrate that highly optimized software running on a state-of-the-art HPC cluster provides sufficient computing power to simulate biomolecular systems at the mesoscale of viruses and organelles, and potentially small cells in the near future.

In situ and in vitro cryo-EM reveal structures of mycobacterial encapsulin assembly intermediates

Prokaryotes rely on proteinaceous compartments such as encapsulin to isolate harmful reactions. Encapsulin are widely expressed by bacteria, including the Mycobacteriaceae, which include the human pathogens Mycobacterium tuberculosis and Mycobacterium leprae. Structures of fully assembled encapsulin shells have been determined for several species, but encapsulin assembly and cargo encapsulation are still poorly characterised, because of the absence of encapsulin structures in intermediate assembly states. We combine in situ and in vitro structural electron microscopy to show that encapsulins are dynamic assemblies with intermediate states of cargo encapsulation and shell assembly. Using cryo-focused ion beam (FIB) lamella preparation and cryo-electron tomography (CET), we directly visualise encapsulins in Mycobacterium marinum, and observed ribbon-like attachments to the shell, encapsulin shells with and without cargoes, and encapsulin shells in partially assembled states. In vitro cryo-electron microscopy (EM) single-particle analysis of the Mycobacterium tuberculosis encapsulin was used to obtain three structures of the encapsulin shell in intermediate states, as well as a 2.3 Å structure of the fully assembled shell. Based on the analysis of the intermediate encapsulin shell structures, we propose a model of encapsulin self-assembly via the pairwise addition of monomers.

Conformational dynamics of the nuclear pore complex central channel

The nuclear pore complex (NPC) is a vital regulator of molecular transport between the nucleus and cytoplasm in eukaryotic cells. At the heart of the NPC's function are intrinsically disordered phenylalanineglycine-rich nucleoporins (FG-Nups), which form a dynamic permeability barrier within the central channel. This disordered nature facilitates efficient nucleocytoplasmic transport but also poses significant challenges to its characterization, especially within the nano-confined environment of the NPC. Recent advances in experimental techniques, such as cryo-electron microscopy, atomic force microscopy, fluorescence microscopy, and nuclear magnetic resonance, along with computational modeling, have illuminated the conformational flexibility of FG-Nups, which underpins their functional versatility. This review synthesizes these advancements, emphasizing how disruptions in FG-Nup behavior-caused by mutations or pathological interactions-contribute to diseases such as neurodegenerative disorders, aging-related decline, and viral infections. Despite progress, challenges persist in deciphering FG-Nup dynamics within the crowded and complex cellular environment, especially under pathological conditions. Addressing these gaps is critical for advancing therapeutic strategies targeting NPC dysfunction in disease progression.

Structural determinants of co-translational protein complex assembly

Protein assembly into functional complexes is critical to life's processes. While complex assembly is classically described as occurring between fully synthesized proteins, recent work showed that co-translational assembly is prevalent in human cells. However, the biological basis for the existence of this process and the identity of protein pairs that assemble co-translationally remain unknown. We show that co-translational assembly is governed by structural characteristics of complexes and involves mutually stabilized subunits. Accordingly, co-translationally assembling subunits are unstable in isolation and exhibit synchronized proteostasis with their partner. By leveraging structural signatures and AlphaFold2-based predictions, we accurately predicted co-translational assembly, including pair identities, at proteome scale and across species. We validated our predictions by ribosome profiling, stoichiometry perturbations, and single-molecule RNA-fluorescence in situ hybridization (smFISH) experiments that revealed co-localized mRNAs. This work establishes a fundamental connection between protein structure and the translation process, highlighting the overarching impact of three-dimensional structure on gene expression, mRNA localization, and proteostasis.

Nuclear pore permeability and fluid flow are modulated by its dilation state

Changing environmental conditions necessitate rapid adaptation of cytoplasmic and nuclear volumes. We use the slime mold Dictyostelium discoideum, known for its ability to tolerate extreme changes in osmolarity, to assess which role nuclear pore complexes (NPCs) play in achieving nuclear volume adaptation and relieving mechanical stress. We capitalize on the unique properties of D. discoideum to quantify fluid flow across NPCs. D. discoideum has an elaborate NPC structure in situ. Its dilation state affects NPC permeability for nucleocytosolic flow. Based on mathematical concepts adapted from hydrodynamics, we conceptualize this phenomenon as porous flow across NPCs, which is distinct from canonically characterized modes of nucleocytoplasmic transport because of its dependence on pressure. Viral NPC blockage decreased nucleocytosolic flow. Our results may be relevant for any biological conditions that entail rapid nuclear size adaptation, including metastasizing cancer cells, migrating cells, or differentiating tissues.

Opening the gate: Complexity and modularity of the nuclear pore scaffold and basket

Nuclear pore complexes (NPCs) are giant molecular assemblies that form the gateway between the nucleus and the cytoplasm and accommodate the bidirectional transport of a large variety of cargoes. Recent years have seen tremendous advances in our understanding of their building principles and have in particular called attention to the flexibility and variability of NPC composition and structure. Here, we review these recent advances and discuss how the newest technologies push the boundaries of nuclear pore research forward, with a specific highlight on the NPC scaffold and a prominent pore appendage, the nuclear basket, whose architecture has long been elusive.

Nucleoporin PNET1 coordinates mitotic nuclear pore complex dynamics for rapid cell division

The nuclear pore complex (NPC) is a cornerstone of eukaryotic cell functionality, orchestrating the nucleocytoplasmic shuttling of macromolecules. Here we report that Plant Nuclear Envelope Transmembrane 1 (PNET1), a transmembrane nucleoporin, is an adaptable NPC component that is mainly expressed in actively dividing cells. PNET1's selective incorporation into the NPC is required for rapid cell growth in highly proliferative meristem and callus tissues in Arabidopsis. We demonstrate that the cell cycle-dependent phosphorylation of PNET1 coordinates mitotic disassembly and post-mitotic reassembly of NPCs during the cell cycle. PNET1 hyperphosphorylation disrupts its interaction with the NPC scaffold, facilitating NPC dismantling and nuclear membrane breakdown to trigger mitosis. In contrast, nascent, unphosphorylated PNET1 is incorporated into the nuclear pore membrane in the daughter cells, where it restores interactions with scaffolding nucleoporins for NPC reassembly. The expression of the human PNET1 homologue is required for and markedly upregulated during cancer cell growth, suggesting that PNET1 plays a conserved role in facilitating rapid cell division during open mitosis in highly proliferative tissues.

Rapid structural analysis of bacterial ribosomes in situ

Rapid structural analysis of purified proteins and their complexes has become increasingly common thanks to key methodological advances in cryo-electron microscopy (cryo-EM) and associated data processing software packages. In contrast, analogous structural analysis in cells via cryo-electron tomography (cryo-ET) remains challenging due to critical technical bottlenecks, including low-throughput sample preparation and imaging, and laborious data processing methods. Here, we describe a rapid in situ cryo-ET sample preparation and data analysis workflow that results in the routine determination of sub-nm resolution ribosomal structures. We apply this workflow to E. coli, producing a 5.8 Å structure of the 70S ribosome from cells in less than 10 days and facilitating the discovery of a minor population of 100S-like disomes. We envision our approach to be widely applicable to related bacterial samples.

A deep learning method for predicting interactions for intrinsically disordered regions of proteins

Intrinsically disordered proteins or regions (IDPs/IDRs) adopt diverse binding modes with different partners, ranging from ordered to multivalent to fuzzy conformations in the bound state. Characterizing IDR interfaces is challenging experimentally and computationally. Alphafold-multimer and Alphafold3, the state-of-the-art structure prediction methods, are less accurate at predicting IDR binding sites at their benchmarked confidence cutoffs. Their performance improves upon lowering the confidence cutoffs. Here, we developed Disobind, a deep-learning method that predicts inter-protein contact maps and interface residues for an IDR and a partner protein, given their sequences. It outperforms AlphaFold-multimer and AlphaFold3 at multiple confidence cutoffs. Combining the Disobind and AlphaFold-multimer predictions further improves the performance. In contrast to most current methods, Disobind considers the context of the binding partner and does not depend on structures and multiple sequence alignments. Its predictions can be used to localize IDRs in integrative structures of large assemblies and characterize and modulate IDR-mediated interactions.

HIV capsids: orchestrators of innate immune evasion, pathogenesis and pandemicity

Human immunodeficiency virus (HIV) is an exemplar virus, still the most studied and best understood and a model for mechanisms of viral replication, immune evasion and pathogenesis. In this review, we consider the earliest stages of HIV infection from transport of the virion contents through the cytoplasm to integration of the viral genome into host chromatin. We present a holistic model for the virus-host interaction during this pivotal stage of infection. Central to this process is the HIV capsid. The last 10 years have seen a transformation in the way we understand HIV capsid structure and function. We review key discoveries and present our latest thoughts on the capsid as a dynamic regulator of innate immune evasion and chromatin targeting. We also consider the accessory proteins Vpr and Vpx because they are incorporated into particles where they collaborate with capsids to manipulate defensive cellular responses to infection. We argue that effective regulation of capsid uncoating and evasion of innate immunity define pandemic potential and viral pathogenesis, and we review how comparison of different HIV lineages can reveal what makes pandemic lentiviruses special.

Graph-Theoretical Prediction and Analysis of Biologically Relevant Substructures in an Open and Closed Conformation of Respiratory Complex I

Protein complexes are functional modules within the hierarchy of the cellular organization. Large protein complexes often consist of smaller functional modules, which are biologically relevant substructures with specific functions. The first protein complex of the respiratory chain, complex I, consists of functional modules for the electron transfer from NADH to quinone and the translocation of protons across the inner mitochondrial membrane. Complex I is well-characterized and biological modules have been experimentally assigned. Nevertheless, there is an ongoing discussion about the coupling of the electron transfer and the proton translocation, and about the proton translocation pathways.We modelled a mammalian complex I in open and closed conformations as complex graphs, with vertices representing protein chains and edges representing chain-chain contacts. Using a graph-theoretical method, we computed the structural modules of complex I, which indicated functional, biological substructures. We described characteristic structural features of complex I and observed a rearrangement of the structural modules. The changes in the structural modules indicated the formation of a functional module in the membrane arm of complex I during the conformational change.

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.

Reading the repertoire: Progress in adaptive immune receptor analysis using machine learning

The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles. We outline key challenges for the field, highlighting the need for software benchmarking, targeted large-scale data generation, and coordinated research efforts.

Nuclear-Targeting Peptides for Cancer Therapy

Nucleus is the central regulator of cells that controls cell proliferation, metabolism, and cell cycle, and is considered the most important organelle in cells. The precision medicine that can achieve nuclear targeting has achieved good therapeutic effects in anti-tumor therapy. However, the presence of biological barriers such as cell membranes and nuclear membranes in cells limit the delivery of therapeutic agents to the nucleus. Therefore, developing effective nuclear-targeting drug delivery strategies is particularly important. Nuclear-targeting peptides are a class of functional peptides that can penetrate cell membranes and target the nucleus. They mainly recognize and bind to the nuclear transport molecules (such as Importin-α/β) and transport the therapeutic agents to the nucleus through nuclear pore complexes (NPC). This review summarizes the most recent developments of strategies for anti-tumor therapy utilizing nuclear-targeting peptides, which will ultimately contribute to the development of more effective nuclear-targeting strategies to achieve better anti-tumor outcomes.

Selective degradation of multimeric proteins by TRIM21-based molecular glue and PROTAC degraders

Targeted protein degradation (TPD) utilizes molecular glues or proteolysis-targeting chimeras (PROTACs) to eliminate disease-causing proteins by promoting their interaction with E3 ubiquitin ligases. Current TPD approaches are limited by reliance on a small number of constitutively active E3 ubiquitin ligases. Here, we report that (S)-ACE-OH, a metabolite of the antipsychotic drug acepromazine, acts as a molecular glue to induce an interaction between the E3 ubiquitin ligase TRIM21 and the nucleoporin NUP98, leading to the degradation of nuclear pore proteins and disruption of nucleocytoplasmic trafficking. Functionalization of acepromazine into PROTACs enabled selective degradation of multimeric proteins, such as those within biomolecular condensates, while sparing monomeric proteins. This selectivity is consistent with the requirement of substrate-induced clustering for TRIM21 activation. As aberrant protein assemblies cause diseases such as autoimmunity, neurodegeneration, and cancer, our findings highlight the potential of TRIM21-based multimer-selective degraders as a strategy to tackle the direct causes of these diseases.

A lineage-specific protein network at the trypanosome nuclear envelope

The nuclear envelope (NE) separates translation and transcription and is the location of multiple functions, including chromatin organization and nucleocytoplasmic transport. The molecular basis for many of these functions have diverged between eukaryotic lineages. Trypanosoma brucei, a member of the early branching eukaryotic lineage Discoba, highlights many of these, including a distinct lamina and kinetochore composition. Here, we describe a cohort of proteins interacting with both the lamina and NPC, which we term lamina-associated proteins (LAPs). LAPs represent a diverse group of proteins, including two candidate NPC-anchoring pore membrane proteins (POMs) with architecture conserved with S. cerevisiae and H. sapiens, and additional peripheral components of the NPC. While many of the LAPs are Kinetoplastid specific, we also identified broadly conserved proteins, indicating an amalgam of divergence and conservation within the trypanosome NE proteome, highlighting the diversity of nuclear biology across the eukaryotes, increasing our understanding of eukaryotic and NPC evolution.

Regulating transport efficiency through the nuclear pore complex: The role of binding affinity with FG-Nups

Macromolecules are transported through the nuclear pore complex (NPC) via a series of transient binding and unbinding events with FG-Nups, which are intrinsically disordered proteins anchored to the pore's inner wall. Prior studies suggest that the weak and transient nature of this binding is crucial for maintaining the transported molecules' diffusivity. In this study, we explored the relationship between binding kinetics and transport efficiency using Brownian dynamics simulations. Our results indicate that the duration of binding is a critical factor in regulating transport efficiency. Specifically, excessively short binding durations insufficiently facilitate transport, while overly long durations impede molecular movement. We calculated the optimal binding duration for efficient molecular transport and found that it aligns with other theoretical predictions. Additionally, the calculated value is comparable to experimental measurements of the association timescale between nuclear transport receptors and FG-Nups at a single binding site. Our study provides a quantitative framework that bridges local molecular interactions with overall transport dynamics through the NPC, offering valuable insights into the mechanisms governing selective molecular transport.

Cytoplasmic nucleoporin assemblage: the cellular artwork in physiology and disease

Nucleoporins, essential proteins building the nuclear pore, are pivotal for ensuring nucleocytoplasmic transport. While traditionally confined to the nuclear envelope, emerging evidence indicates their presence in various cytoplasmic structures, suggesting potential non-transport-related roles. This review consolidates findings on cytoplasmic nucleoporin assemblies across different states, including normal physiological conditions, stress, and pathology, exploring their structural organization, formation dynamics, and functional implications. We summarize the current knowledge and the latest concepts on the regulation of nucleoporin homeostasis, aiming to enhance our understanding of their unexpected roles in physiological and pathological processes.

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.

Nuclear pore and nucleocytoplasmic transport impairment in oxidative stress-induced neurodegeneration: relevance to molecular mechanisms in Pathogenesis of Parkinson's and other related neurodegenerative diseases

Nuclear pore complexes (NPCs) are embedded in the nuclear envelope and facilitate the exchange of macromolecules between the nucleus and cytoplasm in eukaryotic cells. The dysfunction of the NPC and nuclear transport plays a significant role in aging and the pathogenesis of various neurodegenerative diseases. Common features among these neurodegenerative diseases, including Parkinson's disease (PD), encompass mitochondrial dysfunction, oxidative stress and the accumulation of insoluble protein aggregates in specific brain regions. The susceptibility of dopaminergic neurons to mitochondrial stress underscores the pivotal role of mitochondria in PD progression. Disruptions in mitochondrial-nuclear communication are exacerbated by aging and α-synuclein-induced oxidative stress in PD. The precise mechanisms underlying mitochondrial impairment-induced neurodegeneration in PD are still unclear. Evidence suggests that perturbations in dopaminergic neuronal nuclei are linked to PD-related neurodegeneration. These perturbations involve structural damage to the nuclear envelope and mislocalization of pivotal transcription factors, potentially driven by oxidative stress or α-synuclein pathology. The presence of protein aggregates, pathogenic mutations, and ongoing oxidative stress can exacerbate the dysfunction of NPCs, yet this mechanism remains understudied in the context of oxidative stress-induced PD. This review summarizes the link between mitochondrial dysfunction and dopaminergic neurodegeneration and outlines the current evidence for nuclear envelope and nuclear transport abnormalities in PD, particularly in oxidative stress. We highlight the potential role of nuclear pore and nucleocytoplasmic transport dysfunction in PD and stress the importance of systematically investigating NPC components in PD.

Channel width modulates the permeability of DNA origami-based nuclear pore mimics

Nucleoporins (nups) in the nuclear pore complex (NPC) form a selective barrier that suppresses the diffusion of most macromolecules while enabling rapid transport of nuclear transport receptor (NTR)-bound cargos. Recent studies have shown that the NPC may dilate and constrict, but how altering the NPC diameter affects its selective barrier properties remains unclear. Here, we build DNA nanopores with programmable diameters and nup arrangements to model the constricted and dilated NPCs. We find that Nup62 proteins form a dynamic cross-channel barrier impermeable to hepatitis B virus (HBV) capsids when grafted inside 60-nm-wide nanopores but not in 79-nm pores, where Nup62 cluster locally. Furthermore, importin-β1 substantially changes the dynamics of Nup62 assemblies and facilitates the passage of HBV capsids through the 60-nm NPC mimics containing Nup62 and Nup153. Our study shows that transport channel width is critical to the permeability of nup barriers and underscores NTRs' role in dynamically remodeling nup assemblies and mediating the nuclear entry of viruses.

Ultra-broadband all-optical nonlinear activation function enabled by MoTe2/optical waveguide integrated devices

All-optical nonlinear activation functions (NAFs) are crucial for enabling rapid optical neural networks (ONNs). As linear matrix computation advances in integrated ONNs, on-chip all-optical NAFs face challenges such as limited integration, high latency, substantial power consumption, and a high activation threshold. In this work, we develop an integrated nonlinear optical activator based on the butt-coupling integration of two-dimensional (2D) MoTe2 and optical waveguides (OWGs). The activator exhibits an ultra-broadband response from visible to near-infrared wavelength, a low activation threshold of 0.94 μW, a small device size (~50 µm2), an ultra-fast response rate (2.08 THz), and high-density integration. The excellent nonlinear effects and broadband response of 2D materials have been utilized to create all-optical NAFs. These activators were applied to simulate MNIST handwritten digit recognition, achieving an accuracy of 97.6%. The results underscore the potential application of this approach in ONNs. Moreover, the classification of more intricate CIFAR-10 images demonstrated a generalizable accuracy of 94.6%. The present nonlinear activator promises a general platform for three-dimensional (3D) ultra-broadband ONNs with dense integration and low activation thresholds by integrating a variety of strong nonlinear optical (NLO) materials (e.g., 2D materials) and OWGs in glass.

Biomolecular simulations at the exascale: From drug design to organelles and beyond

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies.

Expanding insights from in situ cryo-EM

The combination of cryo-electron tomography and subtomogram analysis affords 3D high-resolution views of biological macromolecules in their native cellular environment, or in situ. Streamlined methods for acquiring and processing these data are advancing attainable resolutions into the realm of drug discovery. Yet regardless of resolution, structure prediction driven by artificial intelligence (AI) combined with subtomogram analysis is becoming powerful in understanding macromolecular assemblies. Automated and AI-assisted data mining is increasingly necessary to cope with the growing wealth of tomography data and to maximize the information obtained from them. Leveraging developments from AI and single-particle analysis could be essential in fulfilling the potential of in situ cryo-EM. Here, we highlight new developments for in situ cryo-EM and the emerging potential for AI in this process.

The molecular architecture of the nuclear basket

The nuclear pore complex (NPC) is the sole mediator of nucleocytoplasmic transport. Despite great advances in understanding its conserved core architecture, the peripheral regions can exhibit considerable variation within and between species. One such structure is the cage-like nuclear basket. Despite its crucial roles in mRNA surveillance and chromatin organization, an architectural understanding has remained elusive. Using in-cell cryo-electron tomography and subtomogram analysis, we explored the NPC's structural variations and the nuclear basket across fungi (yeast; S. cerevisiae), mammals (mouse; M. musculus), and protozoa (T. gondii). Using integrative structural modeling, we computed a model of the basket in yeast and mammals that revealed how a hub of nucleoporins (Nups) in the nuclear ring binds to basket-forming Mlp/Tpr proteins: the coiled-coil domains of Mlp/Tpr form the struts of the basket, while their unstructured termini constitute the basket distal densities, which potentially serve as a docking site for mRNA preprocessing before nucleocytoplasmic transport.

Docking a flexible basket onto the core of the nuclear pore complex

The nuclear basket attaches to the nucleoplasmic side of the nuclear pore complex (NPC), coupling transcription to mRNA quality control and export. The basket expands the functional repertoire of a subset of NPCs in Saccharomyces cerevisiae by drawing a unique RNA/protein interactome. Yet, how the basket docks onto the NPC core remains unknown. By integrating AlphaFold-based interaction screens, electron microscopy and membrane-templated reconstitution, we uncovered a membrane-anchored tripartite junction between basket and NPC core. The basket subunit Nup60 harbours three adjacent short linear motifs, which connect Mlp1, a parallel homodimer consisting of coiled-coil segments interrupted by flexible hinges, and the Nup85 subunit of the Y-complex. We reconstituted the Y-complex•Nup60•Mlp1 assembly on a synthetic membrane and validated the protein interfaces in vivo. Here we explain how a short linear motif-based protein junction can substantially reshape NPC structure and function, advancing our understanding of compositional and conformational NPC heterogeneity.

Exploring protein functions from structural flexibility using CABS-flex modeling

Understanding protein function often necessitates characterizing the flexibility of protein structures. However, simulating protein flexibility poses significant challenges due to the complex dynamics of protein systems, requiring extensive computational resources and accurate modeling techniques. In response to these challenges, the CABS-flex method has been developed as an efficient modeling tool that combines coarse-grained simulations with all-atom detail. Available both as a web server and a standalone package, CABS-flex is dedicated to a wide range of users. The web server version offers an accessible interface for straightforward tasks, while the standalone command-line program is designed for advanced users, providing additional features, analytical tools, and support for handling large systems. This paper examines the application of CABS-flex across various structure-function studies, facilitating investigations into the interplay among protein structure, dynamics, and function in diverse research fields. We present an overview of the current status of the CABS-flex methodology, highlighting its recent advancements, practical applications, and forthcoming challenges.

Interdependence between Nuclear Pore Gatekeepers and Genome Caretakers: Cues from Genome Instability Syndromes

This review starts off with the first germline homozygous variants of the Nucleoporin 98 gene (NUP98) in siblings whose clinical presentation recalls Rothmund-Thomson (RTS) and Werner (WS) syndromes. The progeroid phenotype caused by a gene associated with haematological malignancies and neurodegenerative disorders primed the search for interplay between caretakers involved in genome instability syndromes and Nuclear Pore Complex (NPC) components. In the context of basic information on NPC architecture and functions, we discuss the studies on the interdependence of caretakers and gatekeepers in WS and Hereditary Fibrosing Poikiloderma (POIKTMP), both entering in differential diagnosis with RTS. In WS, the WRN/WRNIP complex interacts with nucleoporins of the Y-complex and NDC1 altering NPC architecture. In POIKTMP, the mutated FAM111B, recruited by the Y-complex's SEC13 and NUP96, interacts with several Nups safeguarding NPC structure. The linkage of both defective caretakers to the NPC highlights the attempt to activate a repair hub at the nuclear periphery to restore the DNA damage. The two separate WS and POIKTMP syndromes are drawn close by the interaction of their damage sensors with the NPC and by the shared hallmark of short fragile telomeres disclosing a major role of both caretakers in telomere maintenance.

Generative artificial intelligence performs rudimentary structural biology modeling

Natural language-based generative artificial intelligence (AI) has become increasingly prevalent in scientific research. Intriguingly, capabilities of generative pre-trained transformer (GPT) language models beyond the scope of natural language tasks have recently been identified. Here we explored how GPT-4 might be able to perform rudimentary structural biology modeling. We prompted GPT-4 to model 3D structures for the 20 standard amino acids and an α-helical polypeptide chain, with the latter incorporating Wolfram mathematical computation. We also used GPT-4 to perform structural interaction analysis between the anti-viral nirmatrelvir and its target, the SARS-CoV-2 main protease. Geometric parameters of the generated structures typically approximated close to experimental references. However, modeling was sporadically error-prone and molecular complexity was not well tolerated. Interaction analysis further revealed the ability of GPT-4 to identify specific amino acid residues involved in ligand binding along with corresponding bond distances. Despite current limitations, we show the current capacity of natural language generative AI to perform basic structural biology modeling and interaction analysis with atomic-scale accuracy.

AlphaFold two years on: Validation and impact

Two years on from the initial release of AlphaFold, we have seen its widespread adoption as a structure prediction tool. Here, we discuss some of the latest work based on AlphaFold, with a particular focus on its use within the structural biology community. This encompasses use cases like speeding up structure determination itself, enabling new computational studies, and building new tools and workflows. We also look at the ongoing validation of AlphaFold, as its predictions continue to be compared against large numbers of experimental structures to further delineate the model's capabilities and limitations.

Integrative spatiotemporal modeling of biomolecular processes: application to the assembly of the Nuclear Pore Complex

Dynamic processes involving biomolecules are essential for the function of the cell. Here, we introduce an integrative method for computing models of these processes based on multiple heterogeneous sources of information, including time-resolved experimental data and physical models of dynamic processes. We first compute integrative structure models at fixed time points and then optimally select and connect these snapshots into a series of trajectories that optimize the likelihood of both the snapshots and transitions between them. The method is demonstrated by application to the assembly process of the human Nuclear Pore Complex in the context of the reforming nuclear envelope during mitotic cell division, based on live-cell correlated electron tomography, bulk fluorescence correlation spectroscopy-calibrated quantitative live imaging, and a structural model of the fully-assembled Nuclear Pore Complex. Modeling of the assembly process improves the model precision over static integrative structure modeling alone. The method is applicable to a wide range of time-dependent systems in cell biology, and is available to the broader scientific community through an implementation in the open source Integrative Modeling Platform software.