DeepTracer for fast de novo cryo-EM protein structure modeling and special studies on CoV-related complexes

Ensemble refinement of mismodeled cryo-EM RNA structures using all-atom simulations

The advent of single-particle cryogenic electron microscopy (cryo-EM) has enabled near-atomic resolution imaging of large macromolecules, enhancing functional insights. However, current cryo-EM refinement tools condense all single-particle images into a single structure, which can misrepresent highly flexible molecules like RNAs. Here, we combine molecular dynamics simulations with cryo-EM density maps to better account for the structural dynamics of a complex and biologically relevant RNA macromolecule. Namely, using metainference, a Bayesian method, we reconstruct an ensemble of structures of the group II intron ribozyme, which better matches experimental data, and we reveal inaccuracies of single-structure approaches in modeling flexible regions. An analysis of all RNA-containing structures deposited in the Protein Data Bank reveals that this issue affects most cryo-EM structures in the 2.5-4 Å range. Thus, RNA structures determined by cryo-EM require careful handling, and our method may be broadly applicable to other RNA systems.

Finding antibodies in cryo-EM maps with CrAI

MOTIVATION

Therapeutic antibodies have emerged as a prominent class of new drugs due to their high specificity and their ability to bind to several protein targets. Once an initial antibody has been identified, its design and characteristics are refined using structural information, when it is available. Cryo-EM is currently the most effective method to obtain 3D structures. It relies on well-established methods to process raw data into a 3D map, which may, however, be noisy and contain artifacts. To fully interpret these maps the number, position, and structure of antibodies and other proteins present must be determined. Unfortunately, existing automated methods addressing this step have limited accuracy, require additional inputs and high-resolution maps, and exhibit long running times.

RESULTS

We propose the first fully automatic and efficient method dedicated to finding antibodies in cryo-EM maps: CrAI. This machine learning approach leverages the conserved structure of antibodies and a dedicated novel database that we built to solve this problem. Running a prediction takes only a few seconds, instead of hours, and requires nothing but the cryo-EM map, seamlessly integrating within automated analysis pipelines. Our method can find the location and pose of both Fabs and VHHs at resolutions up to 10 Å and is significantly more reliable than existing approaches.

AVAILABILITY AND IMPLEMENTATION

We make our method available both in open source github.com/Sanofi-Public/crai and as a ChimeraX bundle (crai).

Structures of Native Doublet Microtubules from Trichomonas vaginalis Reveal Parasite-Specific Proteins

Doublet microtubules (DMTs) are flagellar components required for the protist Trichomonas vaginalis (Tv) to swim through the human genitourinary tract to cause trichomoniasis, the most common non-viral sexually transmitted disease. Lack of structures of Tv's DMT (Tv-DMT) has prevented structure-guided drug design to manage Tv infection. Here, we determine the 16 nm, 32 nm, 48 nm and 96 nm-repeat structures of native Tv-DMT at resolution ranging from 3.4 to 4.4 Å by cryogenic electron microscopy (cryoEM) and built an atomic model for the entire Tv-DMT. These structures show that Tv-DMT is composed of 30 different proteins, including the α- and β-tubulin, 19 microtubule inner proteins (MIPs) and 9 microtubule outer proteins. While the A-tubule of Tv-DMT is simplistic compared to DMTs of other organisms, the B-tubule of Tv-DMT features parasite-specific proteins, such as TvFAP40 and TvFAP35. Notably, TvFAP40 and TvFAP35 form filaments near the inner and outer junctions, respectively, and interface with stabilizing MIPs. This atomic model of the Tv-DMT highlights diversity of eukaryotic motility machineries and provides a structural framework to inform rational design of therapeutics against trichomoniasis.

Fitting Atomic Structures into Cryo-EM Maps by Coupling Deep Learning-Enhanced Map Processing with Global-Local Optimization

With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.

Mechanism of mammalian transcriptional repression by noncoding RNA

Transcription by RNA polymerase II (Pol II) can be repressed by noncoding RNA, including the human RNA Alu. However, the mechanism by which endogenous RNAs repress transcription remains unclear. Here we present cryogenic-electron microscopy structures of Pol II bound to Alu RNA, which reveal that Alu RNA mimics how DNA and RNA bind to Pol II during transcription elongation. Further, we show how distinct domains of the general transcription factor TFIIF control repressive activity. Together, we reveal how a noncoding RNA can regulate mammalian gene expression.

A Labeled Dataset for AI-based Cryo-EM Map Enhancement

Cryo-electron microscopy (cryo-EM) has transformed structural biology by enabling near-atomic resolution imaging of macromolecular complexes. However, cryo-EM density maps suffer from intrinsic noise arising from structural sources, shot noise, and digital recording, which complicates accurate atomic structure building. While various methods for denoising cryo-EM density maps exist, there is a lack of standardized datasets for benchmarking artificial intelligence (AI) approaches. Here, we present an open-source dataset for cryo-EM density map denoising comprising 650 high-resolution (1-4 Å) experimental maps paired with three types of generated label maps: regression maps capturing idealized density distributions, binary classification maps distinguishing structural elements from background, and atom-type classification maps. Each map is standardized to 1 Å voxel size and validated through Fourier Shell Correlation analysis, demonstrating substantial resolution improvements in label maps compared to experimental maps. This resource bridges the gap between structural biology and artificial intelligence communities, enabling researchers to develop and benchmark innovative methods for enhancing cryo-EM density maps.

Structure, identification and characterization of the RibD-enolase complex in Francisella

Francisella tularensis is a highly infectious bacterium, a Tier 1-select agent, and the causative agent of tularemia, a potentially fatal zoonotic disease. In this study originally aiming to identify anti-tularemia drug targets, we serendipitously determined the atomic structures and identified their components of the native RibD-enolase protein complex in Francisella novicida; and subsequently systematically characterized the catalytic functions of the RibD-enolase complex. Originally discovered as individually protein in Escherichia coli and yeast, respectively, RibD and enolase are two essential enzymes involved in distinct metabolic pathways, both of which could serve as potential therapeutic targets for tularemia treatment and prevention. Our biochemical validation using pull-down assays confirmed the formation of this complex in vivo, revealing that all eluted RibD is bound to enolase, while the majority of enolase remained uncomplexed. Structural analysis reveals unique features of the Francisella complex, including key RibD-enolase interactions that mediate complex assembly and β-strand swapping between RibD subunits. Furthermore, molecular dynamics simulations of the ligand-bound RibD-enolase complex highlight localized conformational changes within the substrate-binding sites and suggest a gating mechanism between RibD's substrate and cofactor-binding sites to ensure efficient uptake and turnover. Despite the physical association between RibD and enolase, enzymatic assays indicated their catalytic activities are independent of each other, thus the complex may have alternative functional roles that warrant further exploration. Our study provides the first structural and biochemical characterization of the RibD-enolase complex, establishing a foundation for further investigations into its functional significance in Francisella and potential antibacterial development.

Advancing structure modeling from cryo-EM maps with deep learning

Cryo-electron microscopy (cryo-EM) has revolutionized structural biology by enabling the determination of biomolecular structures that are challenging to resolve using conventional methods. Interpreting a cryo-EM map requires accurate modeling of the structures of underlying biomolecules. Here, we concisely discuss the evolution and current state of automatic structure modeling from cryo-EM density maps. We classify modeling methods into two categories: de novo modeling methods from high-resolution maps (better than 5 Å) and methods that model by fitting individual structures of component proteins to maps at lower resolution (worse than 5 Å). Special attention is given to the role of deep learning in the modeling process, highlighting how AI-driven approaches are transformative in cryo-EM structure modeling. We conclude by discussing future directions in the field.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

AI-based methods for biomolecular structure modeling for Cryo-EM

Cryo-electron microscopy (Cryo-EM) has revolutionized structural biology by enabling the determination of macromolecular structures that were challenging to study with conventional methods. Processing cryo-EM data involves several computational steps to derive three-dimensional structures from raw projections. Recent advancements in artificial intelligence (AI) including deep learning have significantly improved the performance of these processes. In this review, we discuss state-of-the-art AI-based techniques used in key steps of cryo-EM data processing, including macromolecular structure modeling and heterogeneity analysis.

Structural basis of RNA polymerase complexes in African swine fever virus

African swine fever virus is highly contagious and causes a fatal infectious disease in pigs, resulting in a significant global impact on pork supply. The African swine fever virus RNA polymerase serves as a crucial multifunctional protein complex responsible for genome transcription and regulation. Therefore, it is essential to investigate its structural and functional characteristics for the prevention and control of African swine fever. Here, we determine the structures of endogenous African swine fever virus RNA polymerase in both nucleic acid-free and elongation states. The African swine fever virus RNA polymerase shares similarities with the core of typical RNA polymerases, but possesses a distinct subunit M1249L. Notably, the dynamic binding mode of M1249L with RNA polymerase, along with the C-terminal tail insertion of M1249L in the active center of DNA-RNA scaffold binding, suggests the potential of M1249L to regulate RNA polymerase activity within cells. These results are important for understanding the transcription cycle of the African swine fever virus and for developing antiviral strategies.

Structural Biology for Target Identification and Validation

Structural biology is catalyzing a paradigm shift in drug discovery towards rational approaches in target identification and validation. Leveraging structural insights obtained through cryo-EM or X-ray crystallography not only enhances the efficiency of drug discovery projects in terms of time and cost, but also significantly improves the likelihood of achieving market approval.Initiating a successful project necessitates more than just a robust package for target credentialing; it demands a comprehensive strategy for the identification and optimization of potential drugs. The critical evaluation of target druggability is markedly enhanced when supported by experimentally derived structural information. This nuanced approach ensures a more thorough understanding of the technical feasibility of drug development from the project's inception.

Secondary Structure Detection and Structure Modeling for Cryo-EM

Rapid advancements in cryogenic electron microscopy (cryo-EM) have revolutionized the field of structural biology by enabling the determination of complex macromolecular structures at unprecedented resolutions. When cryo-EM density maps have a resolution around 3 Å, the atomic structure can be modeled manually. However, as the resolution decreases, analyzing these density maps becomes increasingly challenging. For modeling structures in lower resolution maps, deep learning can be used to identify structural features in the maps to assist in structure modeling.Here, we present a suite of deep learning-based tools developed by our lab that enable structural biologists to work with cryo-EM maps of a wide range of resolutions. For cryo-EM maps at near-atomic resolution (5 Å or better), DeepMainmast automatically models all-atom structures by tracing the main chain from local map features of amino acids and atoms detected by deep learning; DAQ score quantifies map-model fit and indicates potential misassignments in protein models. In intermediate resolution maps (5-10 Å), Emap2sec and Emap2sec+ can accurately detect protein secondary structures and nucleic acids. These tools and more are available at our web server: https://em.kiharalab.org/ .

All-atom RNA structure determination from cryo-EM maps

Many methods exist for determining protein structures from cryogenic electron microscopy maps, but this remains challenging for RNA structures. Here we developed EMRNA, a method for accurate, automated determination of full-length all-atom RNA structures from cryogenic electron microscopy maps. EMRNA integrates deep learning-based detection of nucleotides, three-dimensional backbone tracing and scoring with consideration of sequence and secondary structure information, and full-atom construction of the RNA structure. We validated EMRNA on 140 diverse RNA maps ranging from 37 to 423 nt at 2.0-6.0 Å resolutions, and compared EMRNA with auto-DRRAFTER, phenix.map_to_model and CryoREAD on a set of 71 cases. EMRNA achieves a median accuracy of 2.36 Å root mean square deviation and 0.86 TM-score for full-length RNA structures, compared with 6.66 Å and 0.58 for auto-DRRAFTER. EMRNA also obtains a high residue coverage and sequence match of 93.30% and 95.30% in the built models, compared with 58.20% and 42.20% for phenix.map_to_model and 56.45% and 52.3% for CryoREAD. EMRNA is fast and can build an RNA structure of 100 nt within 3 min.

DiffFit: Visually-Guided Differentiable Fitting of Molecule Structures to a Cryo-EM Map

We introduce DiffFit, a differentiable algorithm for fitting protein atomistic structures into an experimental reconstructed Cryo-Electron Microscopy (cryo-EM) volume map. In structural biology, this process is necessary to semi-automatically composite large mesoscale models of complex protein assemblies and complete cellular structures that are based on measured cryo-EM data. The current approaches require manual fitting in three dimensions to start, resulting in approximately aligned structures followed by an automated fine-tuning of the alignment. The DiffFit approach enables domain scientists to fit new structures automatically and visualize the results for inspection and interactive revision. The fitting begins with differentiable three-dimensional (3D) rigid transformations of the protein atom coordinates followed by sampling the density values at the atom coordinates from the target cryo-EM volume. To ensure a meaningful correlation between the sampled densities and the protein structure, we proposed a novel loss function based on a multi-resolution volume-array approach and the exploitation of the negative space. This loss function serves as a critical metric for assessing the fitting quality, ensuring the fitting accuracy and an improved visualization of the results. We assessed the placement quality of DiffFit with several large, realistic datasets and found it to be superior to that of previous methods. We further evaluated our method in two use cases: automating the integration of known composite structures into larger protein complexes and facilitating the fitting of predicted protein domains into volume densities to aid researchers in identifying unknown proteins. We implemented our algorithm as an open-source plugin (github.com/nanovis/DiffFit) in ChimeraX, a leading visualization software in the field. All supplemental materials are available at osf. io/5tx4q.

DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multichain protein complexes. However, modeling a large complex structure, such as those with more than ten chains, is challenging, particularly when the map resolution decreases. Here we present DiffModeler, a fully automated method for modeling large protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. DiffModeler showed an average template modeling score of 0.88 and 0.91 for two datasets of cryo-EM maps of 0-5 Å resolution and 0.92 for intermediate resolution maps (5-10 Å), substantially outperforming existing methodologies. Further benchmarking at low resolutions (10-20 Å) confirms its versatility, demonstrating plausible performance.

Single particle cryo-EM map and model validation: It's not crystal clear

The application of single particle cryogenic electron microscopy (cryo-EM) to structure determination continues to have a transformative impact on our understanding on biological systems. While there has been a great deal of algorithmic development focused on improving attainable resolutions and streamlining atomic model building, there has not been commensurate development of validation metrics to ensure the accuracy of our cryo-EM maps and models. This review emphasizes the persistent issues that currently complicate single particle cryo-EM structure validation, and highlights the metrics that are gaining broad acceptance by the community. This article aims to underscore the need for further development of validation criteria and the potential role of machine learning methodologies in confidently assessing the quality of cryo-EM structures.

Automated identification of small molecules in cryo-electron microscopy data with density- and energy-guided evaluation

Methodological improvements in cryo-electron microscopy (cryoEM) have made it a useful tool in ligand-bound structure determination for biology and drug design. However, determining the conformation and identity of bound ligands is still challenging at the resolutions typical for cryoEM. Automated methods can aid in ligand conformational modeling, but current ligand identification tools - developed for X-ray crystallography data - perform poorly at resolutions common for cryoEM. Here, we present EMERALD-ID, a method capable of docking and evaluating small molecule conformations for ligand identification. EMERALD-ID identifies 43% of common ligands exactly and identifies closely related ligands in 66% of cases. We then use this tool to discover possible ligand identification errors, as well as previously unidentified ligands. Furthermore, we show EMERALD-ID is capable of identifying ligands from custom ligand libraries of various small molecule types, including human metabolites and drug fragments. Our method provides a valuable addition to cryoEM modeling tools to improve small molecule model accuracy and quality.

Architecture of the ATP-driven motor for protein import into chloroplasts

Thousands of nuclear-encoded proteins are transported into chloroplasts through the TOC-TIC translocon that spans the chloroplast envelope membranes. A motor complex pulls the translocated proteins out of the TOC-TIC complex into the chloroplast stroma by hydrolyzing ATP. The Orf2971-FtsHi complex has been suggested to serve as the ATP-hydrolyzing motor in Chlamydomonas reinhardtii, but little is known about its architecture and assembly. Here, we report the 3.2-Å resolution structure of the Chlamydomonas Orf2971-FtsHi complex. The 20-subunit complex spans the chloroplast inner envelope, with two bulky modules protruding into the intermembrane space and stromal matrix. Six subunits form a hetero-hexamer that potentially provides the pulling force through ATP hydrolysis. The remaining subunits, including potential enzymes/chaperones, likely facilitate the complex assembly and regulate its proper function. Taken together, our results provide the structural foundation for a mechanistic understanding of chloroplast protein translocation.

Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps

Cryo-electron microscopy (cryo-EM) is one of the most powerful experimental methods for macromolecular structure determination. However, accurate DNA/RNA structure modeling from cryo-EM maps is still challenging especially for protein-DNA/RNA or multi-chain DNA/RNA complexes. Here we propose a deep learning-based method for accurate de novo structure determination of DNA/RNA from cryo-EM maps at  <5 Å resolutions, which is referred to as EM2NA. EM2NA is extensively evaluated on a diverse test set of 50 experimental maps at 2.0-5.0 Å resolutions, and compared with state-of-the-art methods including CryoREAD, ModelAngelo, and phenix.map_to_model. On average, EM2NA achieves a residue coverage of 83.15%, C4' RMSD of 1.06 Å, and sequence recall of 46.86%, which outperforms the existing methods. Moreover, EM2NA is applied to build the DNA/RNA structures with 10 to 5347 nt from an EMDB-wide data set of 263 unmodeled raw maps, demonstrating its ability in the blind model building of DNA/RNA from cryo-EM maps. EM2NA is fast and can normally build a DNA/RNA structure of  <500 nt within 10 minutes.

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

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

NucleoFind: a deep-learning network for interpreting nucleic acid electron density

Nucleic acid electron density interpretation after phasing by molecular replacement or other methods remains a difficult problem for computer programs to deal with. Programs tend to rely on time-consuming and computationally exhaustive searches to recognise characteristic features. We present NucleoFind, a deep-learning-based approach to interpreting and segmenting electron density. Using an electron density map from X-ray crystallography obtained after molecular replacement, the positions of the phosphate group, sugar ring and nitrogenous base group can be predicted with high accuracy. On average, 78% of phosphate atoms, 85% of sugar atoms and 83% of base atoms are positioned in predicted density after giving NucleoFind maps produced following successful molecular replacement. NucleoFind can use the wealth of context these predicted maps provide to build more accurate and complete nucleic acid models automatically.

Cryo-EM images of phase-separated lipid bilayer vesicles analyzed with a machine-learning approach

Lateral lipid heterogeneity (i.e., raft formation) in biomembranes plays a functional role in living cells. Three-component mixtures of low- and high-melting lipids plus cholesterol offer a simplified experimental model for raft domains in which a liquid-disordered (Ld) phase coexists with a liquid-ordered (Lo) phase. Using such models, we recently showed that cryogenic electron microscopy (cryo-EM) can detect phase separation in lipid vesicles based on differences in bilayer thickness. However, the considerable noise within cryo-EM data poses a significant challenge for accurately determining the membrane phase state at high spatial resolution. To this end, we have developed an image-processing pipeline that utilizes machine learning (ML) to predict the bilayer phase in projection images of lipid vesicles. Importantly, the ML method exploits differences in both the thickness and molecular density of Lo compared to Ld, which leads to improved phase identification. To assess accuracy, we used artificial images of phase-separated lipid vesicles generated from all-atom molecular dynamics simulations of Lo and Ld phases. Synthetic ground-truth data sets mimicking a series of compositions along a tieline of Ld + Lo coexistence were created and then analyzed with various ML models. For all tieline compositions, we find that the ML approach can correctly identify the bilayer phase with >90% accuracy, thus providing a means to isolate the intensity profiles of coexisting Ld and Lo phases, as well as accurately determine domain-size distributions, number of domains, and phase-area fractions. The method described here provides a framework for characterizing nanoscopic lateral heterogeneities in membranes and paves the way for a more detailed understanding of raft properties in biological contexts.

Structures of Native Doublet Microtubules from Trichomonas vaginalis Reveal Parasite-Specific Proteins as Potential Drug Targets

Doublet microtubules (DMTs) are flagellar components required for the protist Trichomonas vaginalis (Tv) to swim through the human genitourinary tract to cause trichomoniasis, the most common non-viral sexually transmitted disease. Lack of DMT structures has prevented structure-guided drug design to manage Tv infection. Here, we determined the cryo-EM structure of native Tv-DMTs, identifying 29 unique proteins, including 18 microtubule inner proteins and 9 microtubule outer proteins. While the A-tubule is simplistic compared to DMTs of other organisms, the B-tubule features specialized, parasite-specific proteins, such as TvFAP40 and TvFAP35 that form filaments near the inner and outer junctions, respectively, to stabilize DMTs and enable Tv locomotion. Notably, a small molecule, assigned as IP6, is coordinated within a pocket of TvFAP40 and has characteristics of a drug molecule. This first atomic model of the Tv-DMT highlights the diversity of eukaryotic motility machinery and provides a structural framework to inform rational design of therapeutics.

DomainFit: Identification of protein domains in cryo-EM maps at intermediate resolution using AlphaFold2-predicted models

Cryoelectron microscopy (cryo-EM) has revolutionized the structural determination of macromolecular complexes. With the paradigm shift to structure determination of highly complex endogenous macromolecular complexes ex vivo and in situ structural biology, there are an increasing number of structures of native complexes. These complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because side chains cannot be visualized reliably. Here, we present DomainFit, a program for semi-automated domain-level protein identification from cryo-EM maps, particularly at resolutions lower than 4 Å. By fitting domains from AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the domains and protein candidates forming the density. Using DomainFit, we identified two microtubule inner proteins, one of which contains a CCDC81 domain and is exclusively localized in the proximal region of the doublet microtubule in Tetrahymena thermophila.

Structural biology in cellulo: Minding the gap between conceptualization and realization

Recent technological advances have deepened our perception of cellular structure. However, most structural data doesn't originate from intact cells, limiting our understanding of cellular processes. Here, we discuss current and future developments that will bring us towards a structural picture of the cell. Electron cryotomography is the standard bearer, with its ability to provide in cellulo snapshots. Single-particle electron microscopy (of purified biomolecules and of complex mixtures) and covalent crosslinking combined with mass spectrometry also have significant roles to play, as do artificial intelligence algorithms in their many guises. To integrate these multiple approaches, data curation and standardisation will be critical - as is the need to expand efforts beyond our current protein-centric view to the other (macro)molecules that sustain life.

The Development and Opportunities of Predictive Biotechnology

Recent advances in bioeconomy allow a holistic view of existing and new process chains and enable novel production routines continuously advanced by academia and industry. All this progress benefits from a growing number of prediction tools that have found their way into the field. For example, automated genome annotations, tools for building model structures of proteins, and structural protein prediction methods such as AlphaFold2TM or RoseTTAFold have gained popularity in recent years. Recently, it has become apparent that more and more AI-based tools are being developed and used for biocatalysis and biotechnology. This is an excellent opportunity for academia and industry to accelerate advancements in the field further. Biotechnology, as a rapidly growing interdisciplinary field, stands to benefit greatly from these developments.

Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information

The dynamics of proteins are crucial for understanding their mechanisms. However, computationally predicting protein dynamic information has proven challenging. Here, we propose a neural network model, RMSF-net, which outperforms previous methods and produces the best results in a large-scale protein dynamics dataset; this model can accurately infer the dynamic information of a protein in only a few seconds. By learning effectively from experimental protein structure data and cryo-electron microscopy (cryo-EM) data integration, our approach is able to accurately identify the interactive bidirectional constraints and supervision between cryo-EM maps and PDB models in maximizing the dynamic prediction efficacy. Rigorous 5-fold cross-validation on the dataset demonstrates that RMSF-net achieves test correlation coefficients of 0.746 ± 0.127 at the voxel level and 0.765 ± 0.109 at the residue level, showcasing its ability to deliver dynamic predictions closely approximating molecular dynamics simulations. Additionally, it offers real-time dynamic inference with minimal storage overhead on the order of megabytes. RMSF-net is a freely accessible tool and is anticipated to play an essential role in the study of protein dynamics.

Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein-nucleic acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: Escherichia coli beta-galactosidase with inhibitor, SARS-CoV-2 virus RNA-dependent RNA polymerase with covalently bound nucleotide analog and SARS-CoV-2 virus ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. The quality of submitted ligand models and surrounding atoms were analyzed by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics and contact scores. A composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

De novo atomic protein structure modeling for cryoEM density maps using 3D transformer and HMM

Accurately building 3D atomic structures from cryo-EM density maps is a crucial step in cryo-EM-based protein structure determination. Converting density maps into 3D atomic structures for proteins lacking accurate homologous or predicted structures as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated de novo cryo-EM structure modeling method. Cryo2Struct utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps, followed by an innovative Hidden Markov Model (HMM) to connect predicted atoms and build protein backbone structures. Cryo2Struct produces substantially more accurate and complete protein structural models than the widely used ab initio method Phenix. Additionally, its performance in building atomic structural models is robust against changes in the resolution of density maps and the size of protein structures.

Structural basis for odorant recognition of the insect odorant receptor OR-Orco heterocomplex

Insects detect and discriminate a diverse array of chemicals using odorant receptors (ORs), which are ligand-gated ion channels comprising a divergent odorant-sensing OR and a conserved odorant receptor co-receptor (Orco). In this work, we report structures of the ApOR5-Orco heterocomplex from the pea aphid Acyrthosiphon pisum alone and bound to its known activating ligand, geranyl acetate. In these structures, three ApOrco subunits serve as scaffold components that cannot bind the ligand and remain relatively unchanged. Upon ligand binding, the pore-forming helix S7b of ApOR5 shifts outward from the central pore axis, causing an asymmetrical pore opening for ion influx. Our study provides insights into odorant recognition and channel gating of the OR-Orco heterocomplex and offers structural resources to support development of innovative insecticides and repellents for pest control.

Structures of Native Doublet Microtubules from Trichomonas vaginalis Reveal Parasite-Specific Proteins as Potential Drug Targets

Doublet microtubules (DMTs) are flagellar components required for the protist Trichomonas vaginalis ( Tv ) to swim through the human genitourinary tract to cause trichomoniasis, the most common non-viral sexually transmitted disease. Lack of DMT structures has prevented structure-guided drug design to manage Tv infection. Here, we determined the cryo-EM structure of native Tv- DMTs, identifying 29 unique proteins, including 18 microtubule inner proteins and 9 microtubule outer proteins. While the A-tubule is simplistic compared to DMTs of other organisms, the B-tubule features specialized, parasite-specific proteins, like Tv FAP40 and Tv FAP35 that form filaments near the inner and outer junctions, respectively, to stabilize DMTs and enable Tv locomotion. Notably, a small molecule, assigned as IP6, is coordinated within a pocket of Tv FAP40 and has characteristics of a drug molecule. This first atomic model of the Tv -DMT highlights the diversity of eukaryotic motility machinery and provides a structural framework to inform the rational design of therapeutics.

Three-Dimensional Interaction Homology: Deconstructing Residue-Residue and Residue-Lipid Interactions in Membrane Proteins

A method is described to deconstruct the network of hydropathic interactions within and between a protein's sidechain and its environment into residue-based three-dimensional maps. These maps encode favorable and unfavorable hydrophobic and polar interactions, in terms of spatial positions for optimal interactions, relative interaction strength, as well as character. In addition, these maps are backbone angle-dependent. After map calculation and clustering, a finite number of unique residue sidechain interaction maps exist for each backbone conformation, with the number related to the residue's size and interaction complexity. Structures for soluble proteins (~749,000 residues) and membrane proteins (~387,000 residues) were analyzed, with the latter group being subdivided into three subsets related to the residue's position in the membrane protein: soluble domain, core-facing transmembrane domain, and lipid-facing transmembrane domain. This work suggests that maps representing residue types and their backbone conformation can be reassembled to optimize the medium-to-high resolution details of a protein structure. In particular, the information encoded in maps constructed from the lipid-facing transmembrane residues appears to paint a clear picture of the protein-lipid interactions that are difficult to obtain experimentally.

Two distinct archaeal type IV pili structures formed by proteins with identical sequence

Type IV pili (T4P) represent one of the most common varieties of surface appendages in archaea. These filaments, assembled from small pilin proteins, can be many microns long and serve diverse functions, including adhesion, biofilm formation, motility, and intercellular communication. Here, we determine atomic structures of two distinct adhesive T4P from Saccharolobus islandicus via cryo-electron microscopy (cryo-EM). Unexpectedly, both pili were assembled from the same pilin polypeptide but under different growth conditions. One filament, denoted mono-pilus, conforms to canonical archaeal T4P structures where all subunits are equivalent, whereas in the other filament, the tri-pilus, the same polypeptide exists in three different conformations. The three conformations in the tri-pilus are very different from the single conformation found in the mono-pilus, and involve different orientations of the outer immunoglobulin-like domains, mediated by a very flexible linker. Remarkably, the outer domains rotate nearly 180° between the mono- and tri-pilus conformations. Both forms of pili require the same ATPase and TadC-like membrane pore for assembly, indicating that the same secretion system can produce structurally very different filaments. Our results show that the structures of archaeal T4P appear to be less constrained and rigid than those of the homologous archaeal flagellar filaments that serve as helical propellers.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. Cryo2StructData is larger than existing, publicly available datasets for training AI methods to build atomic protein structures from cryo-EM density maps. We trained and tested deep learning models on Cryo2StructData to validate its quality showing that it is ready for being used to train and test AI methods for building atomic models.

Structural determination and modeling of ciliary microtubules

The axoneme, a microtubule-based array at the center of every cilium, has been the subject of structural investigations for decades, but only recent advances in cryo-EM and cryo-ET have allowed a molecular-level interpretation of the entire complex to be achieved. The unique properties of the nine doublet microtubules and central pair of singlet microtubules that form the axoneme, including the highly decorated tubulin lattice and the docking of massive axonemal complexes, provide opportunities and challenges for sample preparation, 3D reconstruction and atomic modeling. Here, the approaches used for cryo-EM and cryo-ET of axonemes are reviewed, while highlighting the unique opportunities provided by the latest generation of AI-guided tools that are transforming structural biology.

Automated model building and protein identification in cryo-EM maps

Interpreting electron cryo-microscopy (cryo-EM) maps with atomic models requires high levels of expertise and labour-intensive manual intervention in three-dimensional computer graphics programs1,2. Here we present ModelAngelo, a machine-learning approach for automated atomic model building in cryo-EM maps. By combining information from the cryo-EM map with information from protein sequence and structure in a single graph neural network, ModelAngelo builds atomic models for proteins that are of similar quality to those generated by human experts. For nucleotides, ModelAngelo builds backbones with similar accuracy to those built by humans. By using its predicted amino acid probabilities for each residue in hidden Markov model sequence searches, ModelAngelo outperforms human experts in the identification of proteins with unknown sequences. ModelAngelo will therefore remove bottlenecks and increase objectivity in cryo-EM structure determination.

DeepQs: Local quality assessment of cryo-EM density map by deep learning map-model fit score

Cryogenic electron microscopy maps are valuable for determining macromolecule structures. A proper quality assessment method is essential for cryo-EM map selection or revision. This article presents DeepQs, a novel approach to estimate local quality for 3D cryo-EM density maps, using a deep-learning algorithm based on map-model fit score. DeepQs is a parameter-free method for users and incorporates structural information between map and its related atomic model into well-trained models by deep learning. More specifically, the DeepQs approach leverages the interplay between map and atomic model through predefined map-model fit score, Q-score. DeepQs can get close results to the ground truth map-model fit scores with only cryo-EM map as input. In experiments, DeepQs demonstrates the lowest root mean square error with standard method Fourier shell correlation metric and high correlation with map-model fit score, Q-score, when compared with other local quality estimation methods in high-resolution dataset (<=5 Å). DeepQs can also be applied to evaluate the quality of the post-processed maps. In both cases, DeepQs runs faster by using GPU acceleration. Our program is available at http://www.csbio.sjtu.edu.cn/bioinf/DeepQs for academic use.

DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model

Cryogenic electron microscopy (cryo-EM) has now been widely used for determining multi-chain protein complexes. However, modeling a complex structure is challenging particularly when the map resolution is low, typically in the intermediate resolution range of 5 to 10 Å. Within this resolution range, even accurate structure fitting is difficult, let alone de novo modeling. To address this challenge, here we present DiffModeler, a fully automated method for modeling protein complex structures. DiffModeler employs a diffusion model for backbone tracing and integrates AlphaFold2-predicted single-chain structures for structure fitting. Extensive testing on cryo-EM maps at intermediate resolutions demonstrates the exceptional accuracy of DiffModeler in structure modeling, achieving an average TM-Score of 0.92, surpassing existing methodologies significantly. Notably, DiffModeler successfully modeled a protein complex composed of 47 chains and 13,462 residues, achieving a high TM-Score of 0.94. Further benchmarking at low resolutions (10-20 Å confirms its versatility, demonstrating plausible performance. Moreover, when coupled with CryoREAD, DiffModeler excels in constructing protein-DNA/RNA complex structures for near-atomic resolution maps (0-5 Å), showcasing state-of-the-art performance with average TM-Scores of 0.88 and 0.91 across two datasets.

Predictive modeling and cryo-EM: A synergistic approach to modeling macromolecular structure

Over the last 15 years, structural biology has seen unprecedented development and improvement in two areas: electron cryo-microscopy (cryo-EM) and predictive modeling. Once relegated to low resolutions, single-particle cryo-EM is now capable of achieving near-atomic resolutions of a wide variety of macromolecular complexes. Ushered in by AlphaFold, machine learning has powered the current generation of predictive modeling tools, which can accurately and reliably predict models for proteins and some complexes directly from the sequence alone. Although they offer new opportunities individually, there is an inherent synergy between these techniques, allowing for the construction of large, complex macromolecular models. Here, we give a brief overview of these approaches in addition to illustrating works that combine these techniques for model building. These examples provide insight into model building, assessment, and limitations when integrating predictive modeling with cryo-EM density maps. Together, these approaches offer the potential to greatly accelerate the generation of macromolecular structural insights, particularly when coupled with experimental data.

Outcomes of the EMDataResource Cryo-EM Ligand Modeling Challenge

The EMDataResource Ligand Model Challenge aimed to assess the reliability and reproducibility of modeling ligands bound to protein and protein/nucleic-acid complexes in cryogenic electron microscopy (cryo-EM) maps determined at near-atomic (1.9-2.5 Å) resolution. Three published maps were selected as targets: E. coli beta-galactosidase with inhibitor, SARS-CoV-2 RNA-dependent RNA polymerase with covalently bound nucleotide analog, and SARS-CoV-2 ion channel ORF3a with bound lipid. Sixty-one models were submitted from 17 independent research groups, each with supporting workflow details. We found that (1) the quality of submitted ligand models and surrounding atoms varied, as judged by visual inspection and quantification of local map quality, model-to-map fit, geometry, energetics, and contact scores, and (2) a composite rather than a single score was needed to assess macromolecule+ligand model quality. These observations lead us to recommend best practices for assessing cryo-EM structures of liganded macromolecules reported at near-atomic resolution.

DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting

The breakthrough in cryo-electron microscopy (cryo-EM) technology has led to an increasing number of density maps of biological macromolecules. However, constructing accurate protein complex atomic structures from cryo-EM maps remains a challenge. In this study, we extend our previously developed DEMO-EM to present DEMO-EM2, an automated method for constructing protein complex models from cryo-EM maps through an iterative assembly procedure intertwining chain- and domain-level matching and fitting for predicted chain models. The method was carefully evaluated on 27 cryo-electron tomography (cryo-ET) maps and 16 single-particle EM maps, where DEMO-EM2 models achieved an average TM-score of 0.92, outperforming those of state-of-the-art methods. The results demonstrate an efficient method that enables the rapid and reliable solution of challenging cryo-EM structure modeling problems.

The wwPDB Consortium, Turner J, Abbott S, Fonseca N, Pye R, Carrijo L, Duraisamy AK, Salih O, Wang Z, Kleywegt GJ, Morris KL, Patwardhan A, Burley SK, Crichlow G, Feng Z, Flatt JW, Ghosh S, Hudson BP, Lawson CL, Liang Y, Peisach E, Persikova I, Sekharan M, Shao C, Young J, Velankar S, Armstrong D, Bage M, Bueno WM, Evans G, Gaborova R, Ganguly S, Gupta D, Harrus D, Tanweer A, Bansal M, Rangannan V, Kurisu G, Cho H, Ikegawa Y, Kengaku Y, Kim JY, Niwa S, Sato J, Takuwa A, Yu J, Hoch JC, Baskaran K, Xu W, Zhang W, Ma X. EMDB-the Electron Microscopy Data Bank

The Electron Microscopy Data Bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide Protein Data Bank consortium (wwPDB; wwpdb.org) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

Cryo2StructData: A Large Labeled Cryo-EM Density Map Dataset for AI-based Modeling of Protein Structures

The advent of single-particle cryo-electron microscopy (cryo-EM) has brought forth a new era of structural biology, enabling the routine determination of large biological molecules and their complexes at atomic resolution. The high-resolution structures of biological macromolecules and their complexes significantly expedite biomedical research and drug discovery. However, automatically and accurately building atomic models from high-resolution cryo-EM density maps is still time-consuming and challenging when template-based models are unavailable. Artificial intelligence (AI) methods such as deep learning trained on limited amount of labeled cryo-EM density maps generate inaccurate atomic models. To address this issue, we created a dataset called Cryo2StructData consisting of 7,600 preprocessed cryo-EM density maps whose voxels are labelled according to their corresponding known atomic structures for training and testing AI methods to build atomic models from cryo-EM density maps. It is larger and of higher quality than any existing, publicly available dataset. We trained and tested deep learning models on Cryo2StructData to make sure it is ready for the large-scale development of AI methods for building atomic models from cryo-EM density maps.

De Novo Atomic Protein Structure Modeling for Cryo-EM Density Maps Using 3D Transformer and Hidden Markov Model

Accurately building three-dimensional (3D) atomic structures from 3D cryo-electron microscopy (cryo-EM) density maps is a crucial step in the cryo-EM-based determination of the structures of protein complexes. Despite improvements in the resolution of 3D cryo-EM density maps, the de novo conversion of density maps into 3D atomic structures for protein complexes that do not have accurate homologous or predicted structures to be used as templates remains a significant challenge. Here, we introduce Cryo2Struct, a fully automated ab initio cryo-EM structure modeling method that utilizes a 3D transformer to identify atoms and amino acid types in cryo-EM density maps first, and then employs a novel Hidden Markov Model (HMM) to connect predicted atoms to build backbone structures of proteins. Tested on a standard test dataset of 128 cryo-EM density maps with varying resolutions (2.1 - 5.6 °A) and different numbers of residues (730 - 8,416), Cryo2Struct built substantially more accurate and complete protein structural models than the widely used ab initio method - Phenix in terms of multiple evaluation metrics. Moreover, on a new test dataset of 500 recently released density maps with varying resolutions (1.9 - 4.0 °A) and different numbers of residues (234 - 8,828), it built more accurate models than on the standard dataset. And its performance is rather robust against the change of the resolution of density maps and the size of protein structures.

DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction

Three-dimensional structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy. Although the resolution of determined cryogenic electron microscopy maps has generally improved, there are still many cases where tracing protein main chains is difficult, even in maps determined at a near-atomic resolution. Here we developed a protein structure modeling method, DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, we integrated AlphaFold2 with the de novo density tracing protocol to combine their complementary strengths and achieved even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign the chain identity to the structure models of homo-multimers, which is not a trivial task for existing methods.

High-Resolution Structural Proteomics of Mitochondria Using the 'Build and Retrieve' Methodology

The application of integrated systems biology to the field of structural biology is a promising new direction, although it is still in the infant stages of development. Here we report the use of single particle cryo-EM to identify multiple proteins from three enriched heterogeneous fractions prepared from human liver mitochondrial lysate. We simultaneously identify and solve high-resolution structures of nine essential mitochondrial enzymes with key metabolic functions, including fatty acid catabolism, reactive oxidative species clearance, and amino acid metabolism. Our methodology also identified multiple distinct members of the acyl-CoA dehydrogenase family. This work highlights the potential of cryo-EM to explore tissue proteomics at the atomic level.

DomainFit: Identification of Protein Domains in cryo-EM maps at Intermediate Resolution using AlphaFold2-predicted Models

Cryo-electron microscopy (cryo-EM) has revolutionized our understanding of macromolecular complexes, enabling high-resolution structure determination. With the paradigm shift to in situ structural biology recently driven by the ground-breaking development of cryo-focused ion beam milling and cryo-electron tomography, there are an increasing number of structures at sub-nanometer resolution of complexes solved directly within their cellular environment. These cellular complexes often contain unidentified proteins, related to different cellular states or processes. Identifying proteins at resolutions lower than 4 Å remains challenging because the side chains cannot be visualized reliably. Here, we present DomainFit, a program for automated domain-level protein identification from cryo-EM maps at resolutions lower than 4 Å. By fitting domains from artificial intelligence-predicted models such as AlphaFold2-predicted models into cryo-EM maps, the program performs statistical analyses and attempts to identify the proteins forming the density. Using DomainFit, we identified two microtubule inner proteins, one of them, a CCDC81 domain-containing protein, is exclusively localized in the proximal region of the doublet microtubule from the ciliate Tetrahymena thermophila. The flexibility and capability of DomainFit makes it a valuable tool for analyzing in situ structures.

Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction

Structure modeling from maps is an indispensable step for studying proteins and their complexes with cryogenic electron microscopy (cryo-EM). Although the resolution of determined cryo-EM maps has generally improved, there are still many cases where tracing protein main-chains is difficult, even in maps determined at a near atomic resolution. Here, we have developed a protein structure modeling method, called DeepMainmast, which employs deep learning to capture the local map features of amino acids and atoms to assist main-chain tracing. Moreover, since Alphafold2 demonstrates high accuracy in protein structure prediction, we have integrated complementary strengths of de novo density tracing using deep learning with Alphafold2's structure modeling to achieve even higher accuracy than each method alone. Additionally, the protocol is able to accurately assign chain identity to the structure models of homo-multimers.

CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning

DNA and RNA play fundamental roles in various cellular processes, where their three-dimensional structures provide information critical to understanding the molecular mechanisms of their functions. Although an increasing number of nucleic acid structures and their complexes with proteins are determined by cryogenic electron microscopy (cryo-EM), structure modeling for DNA and RNA remains challenging particularly when the map is determined at a resolution coarser than atomic level. Moreover, computational methods for nucleic acid structure modeling are relatively scarce. Here, we present CryoREAD, a fully automated de novo DNA/RNA atomic structure modeling method using deep learning. CryoREAD identifies phosphate, sugar and base positions in a cryo-EM map using deep learning, which are traced and modeled into a three-dimensional structure. When tested on cryo-EM maps determined at 2.0 to 5.0 Å resolution, CryoREAD built substantially more accurate models than existing methods. We also applied the method to cryo-EM maps of biomolecular complexes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).