Cryo-EM and artificial intelligence visualize endogenous protein community members

The human touch: Utilizing AlphaFold 3 to analyze structures of endogenous metabolons

Computational structural biology aims to accurately predict biomolecular complexes with AlphaFold 3 spearheading the field. However, challenges loom for structural analysis, especially when complex assemblies such as the pyruvate dehydrogenase complex (PDHc), which catalyzes the link reaction in cellular respiration, are studied. PDHc subcomplexes are challenging to predict, particularly interactions involving weaker, lower-affinity subcomplexes. Supervised modeling, i.e., integrative structural biology, will continue to play a role in fine-tuning this type of prediction (e.g., removing clashes, rebuilding loops/disordered regions, and redocking interfaces). 3D analysis of endogenous metabolic complexes continues to require, in addition to AI, precise and multi-faceted interrogation methods.

Modular Structure and Polymerization Status of GABAA Receptors Illustrated with EM Analysis and AlphaFold2 Prediction

Type-A γ-aminobutyric acid (GABAA) receptors are channel proteins crucial to mediating neuronal balance in the central nervous system (CNS). The structure of GABAA receptors allows for multiple binding sites and is key to drug development. Yet the formation mechanism of the receptor's distinctive pentameric structure is still unknown. This study aims to investigate the role of three predominant subunits of the human GABAA receptor in the formation of protein pentamers. Through purifying and refolding the protein fragments of the GABAA receptor α1, β2, and γ2 subunits, the particle structures were visualised with negative staining electron microscopy (EM). To aid the analysis, AlphaFold2 was used to compare the structures. Results show that α1 and β2 subunit fragments successfully formed homo-oligomers, particularly homopentameric structures, while the predominant heteropentameric GABAA receptor was also replicated through the combination of the three subunits. However, homopentameric structures were not observed with the γ2 subunit proteins. A comparison of the AlphaFold2 predictions and the previously obtained cryo-EM structures presents new insights into the subunits' modular structure and polymerization status. By performing experimental and computational studies, a deeper understanding of the complex structure of GABAA receptors is provided. Hopefully, this study can pave the way to developing novel therapeutics for neuropsychiatric diseases.

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.

A predicted model-aided reconstruction algorithm for X-ray free-electron laser single-particle imaging

Ultra-intense, ultra-fast X-ray free-electron lasers (XFELs) enable the imaging of single protein molecules under ambient temperature and pressure. A crucial aspect of structure reconstruction involves determining the relative orientations of each diffraction pattern and recovering the missing phase information. In this paper, we introduce a predicted model-aided algorithm for orientation determination and phase retrieval, which has been tested on various simulated datasets and has shown significant improvements in the success rate, accuracy and efficiency of XFEL data reconstruction.

FilamentID reveals the composition and function of metabolic enzyme polymers during gametogenesis

Gamete formation and subsequent offspring development often involve extended phases of suspended cellular development or even dormancy. How cells adapt to recover and resume growth remains poorly understood. Here, we visualized budding yeast cells undergoing meiosis by cryo-electron tomography (cryoET) and discovered elaborate filamentous assemblies decorating the nucleus, cytoplasm, and mitochondria. To determine filament composition, we developed a "filament identification" (FilamentID) workflow that combines multiscale cryoET/cryo-electron microscopy (cryoEM) analyses of partially lysed cells or organelles. FilamentID identified the mitochondrial filaments as being composed of the conserved aldehyde dehydrogenase Ald4ALDH2 and the nucleoplasmic/cytoplasmic filaments as consisting of acetyl-coenzyme A (CoA) synthetase Acs1ACSS2. Structural characterization further revealed the mechanism underlying polymerization and enabled us to genetically perturb filament formation. Acs1 polymerization facilitates the recovery of chronologically aged spores and, more generally, the cell cycle re-entry of starved cells. FilamentID is broadly applicable to characterize filaments of unknown identity in diverse cellular contexts.

The advent of preventive high-resolution structural histopathology by artificial-intelligence-powered cryogenic electron tomography

Advances in cryogenic electron microscopy (cryoEM) single particle analysis have revolutionized structural biology by facilitating the in vitro determination of atomic- and near-atomic-resolution structures for fully hydrated macromolecular complexes exhibiting compositional and conformational heterogeneity across a wide range of sizes. Cryogenic electron tomography (cryoET) and subtomogram averaging are rapidly progressing toward delivering similar insights for macromolecular complexes in situ, without requiring tags or harsh biochemical purification. Furthermore, cryoET enables the visualization of cellular and tissue phenotypes directly at molecular, nanometric resolution without chemical fixation or staining artifacts. This forward-looking review covers recent developments in cryoEM/ET and related technologies such as cryogenic focused ion beam milling scanning electron microscopy and correlative light microscopy, increasingly enhanced and supported by artificial intelligence algorithms. Their potential application to emerging concepts is discussed, primarily the prospect of complementing medical histopathology analysis. Machine learning solutions are poised to address current challenges posed by "big data" in cryoET of tissues, cells, and macromolecules, offering the promise of enabling novel, quantitative insights into disease processes, which may translate into the clinic and lead to improved diagnostics and targeted therapeutics.

Delineating organizational principles of the endogenous L-A virus by cryo-EM and computational analysis of native cell extracts

The high abundance of most viruses in infected host cells benefits their structural characterization. However, endogenous viruses are present in low copy numbers and are therefore challenging to investigate. Here, we retrieve cell extracts enriched with an endogenous virus, the yeast L-A virus. The determined cryo-EM structure discloses capsid-stabilizing cation-π stacking, widespread across viruses and within the Totiviridae, and an interplay of non-covalent interactions from ten distinct capsomere interfaces. The capsid-embedded mRNA decapping active site trench is supported by a constricting movement of two flexible opposite-facing loops. tRNA-loaded polysomes and other biomacromolecules, presumably mRNA, are found in virus proximity within the cell extract. Mature viruses participate in larger viral communities resembling their rare in-cell equivalents in terms of size, composition, and inter-virus distances. Our results collectively describe a 3D-architecture of a viral milieu, opening the door to cell-extract-based high-resolution structural virology.

Structural Analysis of Protein Complexes by Cryo-Electron Microscopy

Structural studies of bio-complexes using single particle cryo-Electron Microscopy (cryo-EM) is nowadays a well-established technique in structural biology and has become competitive with X-ray crystallography. Development of digital registration systems for electron microscopy images and algorithms for the fast and efficient processing of the recorded images and their following analysis has facilitated the determination of structures at near-atomic resolution. The latest advances in EM have enabled the determination of protein complex structures at 1.4-3 Å resolution for an extremely broad range of sizes (from ~100 kDa up to hundreds of MDa (Bartesaghi et al., Science 348(6239):1147-1151, 2015; Herzik et al., Nat Commun 10:1032, 2019; Wu et al., J Struct Biol X 4:100020, 2020; Zhang et al., Nat Commun 10:5511, 2019; Zhang et al., Cell Res 30(12):1136-1139, 2020; Yip et al., Nature 587(7832):157-161, 2020; https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year )). In 2022, nearly 1200 structures deposited to the EMDB database were at a resolution of better than 3 Å ( https://www.ebi.ac.uk/emdb/statistics/emdb_resolution_year ).To date, the highest resolutions have been achieved for apoferritin, which comprises a homo-oligomer of high point group symmetry (O432) and has rigid organization together with high stability (Zhang et al., Cell Res 30(12):1136-1139, 2020; Yip et al., Nature 587(7832):157-161, 2020). It has been used as a test object for the assessments of modern cryo-microscopes and processing methods during the last 5 years. In contrast to apoferritin bacterial secretion systems are typical examples of multi protein complexes exhibiting high flexibility owing to their functions relating to the transportation of small molecules, proteins, and DNA into the extracellular space or target cells. This makes their structural characterization extremely challenging (Barlow, Methods Mol Biol 532:397-411, 2009; Costa et al., Nat Rev Microbiol 13:343-359, 2015). The most feasible approach to reveal their spatial organization and functional modification is cryo-electron microscopy (EM). During the last decade, structural cryo-EM has become broadly used for the analysis of the bio-complexes that comprise multiple components and are not amenable to crystallization (Lyumkis, J Biol Chem 294:5181-5197, 2019; Orlova and Saibil, Methods Enzymol 482:321-341, 2010; Orlova and Saibil, Chem Rev 111(12):7710-7748, 2011).In this review, we will describe the basics of sample preparation for cryo-EM, the principles of digital data collection, and the logistics of image analysis focusing on the common steps required for reconstructions of both small and large biological complexes together with refinement of their structures to nearly atomic resolution. The workflow of processing will be illustrated by examples of EM analysis of Type IV Secretion System.

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

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

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

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

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

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

Visualizing everything, everywhere, all at once: Cryo-EM and the new field of structureomics

Twenty years ago, the release of the first draft of the human genome sequence instigated a paradigm shift in genomics and molecular biology. Arguably, structural biology is entering an analogous era, with availability of an experimentally determined or predicted molecular model for almost every protein-coding gene from many genomes-producing a reference "structureome". Structural predictions require experimental validation and not all proteins conform to a single structure, making any reference structureome necessarily incomplete. Despite these limitations, a reference structureome can be used to characterize cell state in more detail than by quantifying sequence or expression levels alone. Cryogenic electron microscopy (cryo-EM) is a method that can generate atomic resolution views of molecules and cells frozen in place. In this perspective I consider how emerging cryo-EM methods are contributing to the new field of structureomics.

Revolving hexameric ATPases as asymmetric motors to translocate double-stranded DNA genome along one strand

DsDNA translocation through nanoscale pores is generally accomplished by ATPase biomotors. The discovery of the revolving dsDNA translocation mechanism, as opposed to rotation, in bacteriophage phi29 elucidated how ATPase motors move dsDNA. Revolution-driven, hexameric dsDNA motors have been reported in herpesvirus, bacterial FtsK, Streptomyces TraB, and T7 phage. This review explores the common relationship between their structure and mechanisms. Commonalities include moving along the 5'→3' strand, inchworm sequential action leading to an asymmetrical structure, channel chirality, channel size, and 3-step channel gating for controlling motion direction. The revolving mechanism and contact with one of the dsDNA strands addresses the historic controversy of dsDNA packaging using nicked, gapped, hybrid, or chemically modified DNA. These controversies surrounding dsDNA packaging activity using modified materials can be answered by whether the modification was introduced into the 3'→5' or 5'→3' strand. Perspectives concerning solutions to the controversy of motor structure and stoichiometry are also discussed.

Structural analysis of an endogenous 4-megadalton succinyl-CoA-generating metabolon

The oxoglutarate dehydrogenase complex (OGDHc) participates in the tricarboxylic acid cycle and, in a multi-step reaction, decarboxylates α-ketoglutarate, transfers succinyl to CoA, and reduces NAD+. Due to its pivotal role in metabolism, OGDHc enzymatic components have been studied in isolation; however, their interactions within the endogenous OGDHc remain elusive. Here, we discern the organization of a thermophilic, eukaryotic, native OGDHc in its active state. By combining biochemical, biophysical, and bioinformatic methods, we resolve its composition, 3D architecture, and molecular function at 3.35 Å resolution. We further report the high-resolution cryo-EM structure of the OGDHc core (E2o), which displays various structural adaptations. These include hydrogen bonding patterns confining interactions of OGDHc participating enzymes (E1o-E2o-E3), electrostatic tunneling that drives inter-subunit communication, and the presence of a flexible subunit (E3BPo), connecting E2o and E3. This multi-scale analysis of a succinyl-CoA-producing native cell extract provides a blueprint for structure-function studies of complex mixtures of medical and biotechnological value.

Does AlphaFold2 model proteins' intracellular conformations? An experimental test using cross-linking mass spectrometry of endogenous ciliary proteins

A major goal in structural biology is to understand protein assemblies in their biologically relevant states. Here, we investigate whether AlphaFold2 structure predictions match native protein conformations. We chemically cross-linked proteins in situ within intact Tetrahymena thermophila cilia and native ciliary extracts, identifying 1,225 intramolecular cross-links within the 100 best-sampled proteins, providing a benchmark of distance restraints obeyed by proteins in their native assemblies. The corresponding structure predictions were highly concordant, positioning 86.2% of cross-linked residues within Cɑ-to-Cɑ distances of 30 Å, consistent with the cross-linker length. 43% of proteins showed no violations. Most inconsistencies occurred in low-confidence regions or between domains. Overall, AlphaFold2 predictions with lower predicted aligned error corresponded to more correct native structures. However, we observe cases where rigid body domains are oriented incorrectly, as for ciliary protein BBC118, suggesting that combining structure prediction with experimental information will better reveal biologically relevant conformations.

Monomer and dimer structures of cytochrome bo3 ubiquinol oxidase from Escherichia coli

The Escherichia coli cytochrome bo3 ubiquinol oxidase is a four-subunit heme-copper oxidase that serves as a proton pump in the E. coli aerobic respiratory chain. Despite many mechanistic studies, it is unclear whether this ubiquinol oxidase functions as a monomer, or as a dimer in a manner similar to its eukaryotic counterparts-the mitochondrial electron transport complexes. In this study, we determined the monomeric and dimeric structures of the E. coli cytochrome bo3 ubiquinol oxidase reconstituted in amphipol by cryogenic electron microscopy single particle reconstruction (cryo-EM SPR) to a resolution of 3.15 and 3.46 Å, respectively. We have discovered that the protein can form a dimer with C2 symmetry, with the dimerization interface maintained by interactions between the subunit II of one monomer and the subunit IV of the other monomer. Moreover, the dimerization does not induce significant structural changes in the monomers, except the movement of a loop in subunit IV (residues 67-74).

AlphaFold2 and its applications in the fields of biology and medicine

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Exploring AlphaFold2's Performance on Predicting Amino Acid Side-Chain Conformations and Its Utility in Crystal Structure Determination of B318L Protein

Recent technological breakthroughs in machine-learning-based AlphaFold2 (AF2) are pushing the prediction accuracy of protein structures to an unprecedented level that is on par with experimental structural quality. Despite its outstanding structural modeling capability, further experimental validations and performance assessments of AF2 predictions are still required, thus necessitating the development of integrative structural biology in synergy with both computational and experimental methods. Focusing on the B318L protein that plays an essential role in the African swine fever virus (ASFV) for viral replication, we experimentally demonstrate the high quality of the AF2 predicted model and its practical utility in crystal structural determination. Structural alignment implies that the AF2 model shares nearly the same atomic arrangement as the B318L crystal structure except for some flexible and disordered regions. More importantly, side-chain-based analysis at the individual residue level reveals that AF2's performance is likely dependent on the specific amino acid type and that hydrophobic residues tend to be more accurately predicted by AF2 than hydrophilic residues. Quantitative per-residue RMSD comparisons and further molecular replacement trials suggest that AF2 has a large potential to outperform other computational modeling methods in terms of structural determination. Additionally, it is numerically confirmed that the AF2 model is accurate enough so that it may well potentially withstand experimental data quality to a large extent for structural determination. Finally, an overall structural analysis and molecular docking simulation of the B318L protein are performed. Taken together, our study not only provides new insights into AF2's performance in predicting side-chain conformations but also sheds light upon the significance of AF2 in promoting crystal structural determination, especially when the experimental data quality of the protein crystal is poor.

Cryo-Electron Microscopy Snapshots of Eukaryotic Membrane Proteins in Native Lipid-Bilayer Nanodiscs

New technologies for purifying membrane-bound protein complexes in combination with cryo-electron microscopy (EM) have recently allowed the exploration of such complexes under near-native conditions. In particular, polymer-encapsulated nanodiscs enable the study of membrane proteins at high resolution while retaining protein-protein and protein-lipid interactions within a lipid bilayer. However, this powerful technology has not been exploited to address the important question of how endogenous─as opposed to overexpressed─membrane proteins are organized within a lipid environment. In this work, we demonstrate that biochemical enrichment protocols for native membrane-protein complexes from Chaetomium thermophilum in combination with polymer-based lipid-bilayer nanodiscs provide a substantial improvement in the quality of recovered endogenous membrane-protein complexes. Mass spectrometry results revealed ∼1123 proteins, while multiple 2D class averages and two 3D reconstructions from cryo-EM data furnished prominent structural signatures. This integrated methodological approach to enriching endogenous membrane-protein complexes provides unprecedented opportunities for a deeper understanding of eukaryotic membrane proteomes.

New opportunities in integrative structural modeling

Integrative structural modeling enables structure determination of macromolecules and their complexes by integrating data from multiple sources. It has been successfully used to characterize macromolecular structures when a single structural biology technique was insufficient. Recent developments in cellular structural biology, including in-cell cryo-electron tomography and artificial intelligence-based structure prediction, have created new opportunities for integrative structural modeling. Here, we will review these opportunities along with the latest developments in integrative modeling methods and their applications. We also highlight open challenges and directions for further development.

Structures and comparison of endogenous 2-oxoglutarate and pyruvate dehydrogenase complexes from bovine kidney

The α-keto acid dehydrogenase complex family catalyzes the essential oxidative decarboxylation of α-keto acids to yield acyl-CoA and NADH. Despite performing the same overarching reaction, members of the family have different component structures and structural organization between each other and across phylogenetic species. While native structures of α-keto acid dehydrogenase complexes from bacteria and fungi became available recently, the atomic structure and organization of their mammalian counterparts in native states remain unknown. Here, we report the cryo-electron microscopy structures of the endogenous cubic 2-oxoglutarate dehydrogenase complex (OGDC) and icosahedral pyruvate dehydrogenase complex (PDC) cores from bovine kidney determined at resolutions of 3.5 Å and 3.8 Å, respectively. The structures of multiple proteins were reconstructed from a single lysate sample, allowing direct structural comparison without the concerns of differences arising from sample preparation and structure determination. Although native and recombinant E2 core scaffold structures are similar, the native structures are decorated with their peripheral E1 and E3 subunits. Asymmetric sub-particle reconstructions support heterogeneity in the arrangements of these peripheral subunits. In addition, despite sharing a similar monomeric fold, OGDC and PDC E2 cores have distinct interdomain and intertrimer interactions, which suggests a means of modulating self-assembly to mitigate heterologous binding between mismatched E2 species. The lipoyl moiety lies near a mobile gatekeeper within the interdomain active site of OGDC E2 and PDC E2. Analysis of the twofold related intertrimer interface identified secondary structural differences and chemical interactions between icosahedral and cubic geometries of the core. Taken together, our study provides a direct structural comparison of OGDC and PDC from the same source and offers new insights into determinants of interdomain interactions and of architecture diversity among α-keto acid dehydrogenase complexes.

Genomic analysis and biochemical profiling of an unaxenic strain of Synechococcus sp. isolated from the Peruvian Amazon Basin region

Cyanobacteria are diverse photosynthetic microorganisms able to produce a myriad of bioactive chemicals. To make possible the rational exploitation of these microorganisms, it is fundamental to know their metabolic capabilities and to have genomic resources. In this context, the main objective of this research was to determine the genome features and the biochemical profile of Synechococcus sp. UCP002. The cyanobacterium was isolated from the Peruvian Amazon Basin region and cultured in BG-11 medium. Growth parameters, genome features, and the biochemical profile of the cyanobacterium were determined using standardized methods. Synechococcus sp. UCP002 had a specific growth rate of 0.086 ± 0.008 μ and a doubling time of 8.08 ± 0.78 h. The complete genome of Synechococcus sp. UCP002 had a size of ∼3.53 Mb with a high coverage (∼200x), and its quality parameters were acceptable (completeness = 99.29%, complete and single-copy genes = 97.5%, and contamination = 0.35%). Additionally, the cyanobacterium had six plasmids ranging from 24 to 200 kbp. The annotated genome revealed ∼3,422 genes, ∼ 3,374 protein-coding genes (with ∼41.31% hypothetical protein-coding genes), two CRISPR Cas systems, and 61 non-coding RNAs. Both the genome and plasmids had the genes for prokaryotic defense systems. Additionally, the genome had genes coding the transcription factors of the metalloregulator ArsR/SmtB family, involved in sensing heavy metal pollution. The biochemical profile showed primary nutrients, essential amino acids, some essential fatty acids, pigments (e.g., all-trans-β-carotene, chlorophyll a, and phycocyanin), and phenolic compounds. In conclusion, Synechococcus sp. UCP002 shows biotechnological potential to produce human and animal nutrients and raw materials for biofuels and could be a new source of genes for synthetic biological applications.

Artificial Intelligence in Cryo-Electron Microscopy

Cryo-electron microscopy (cryo-EM) has become an unrivaled tool for determining the structure of macromolecular complexes. The biological function of macromolecular complexes is inextricably tied to the flexibility of these complexes. Single particle cryo-EM can reveal the conformational heterogeneity of a biochemically pure sample, leading to well-founded mechanistic hypotheses about the roles these complexes play in biology. However, the processing of increasingly large, complex datasets using traditional data processing strategies is exceedingly expensive in both user time and computational resources. Current innovations in data processing capitalize on artificial intelligence (AI) to improve the efficiency of data analysis and validation. Here, we review new tools that use AI to automate the data analysis steps of particle picking, 3D map reconstruction, and local resolution determination. We discuss how the application of AI moves the field forward, and what obstacles remain. We also introduce potential future applications of AI to use cryo-EM in understanding protein communities in cells.

Frontiers in the enzymology of thiamin diphosphate-dependent enzymes

Enzymes that use thiamin diphosphate (ThDP), the biologically active derivative of vitamin B1, as a cofactor play important roles in cellular metabolism in all domains of life. The analysis of ThDP enzymes in the past decades have provided a general framework for our understanding of enzyme catalysis of this protein family. In this review, we will discuss recent advances in the field that include the observation of "unusual" reactions and reaction intermediates that highlight the chemical versatility of the thiamin cofactor. Further topics cover the structural basis of cooperativity of ThDP enzymes, novel insights into the mechanism and structure of selected enzymes, and the discovery of "superassemblies" as reported, for example, acetohydroxy acid synthase. Finally, we summarize recent findings in the structural organisation and mode of action of 2-keto acid dehydrogenase multienzyme complexes and discuss future directions of this exciting research field.

DeepTracer-ID: De novo protein identification from cryo-EM maps

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.

An AI-assisted cryo-EM pipeline for structural studies of cellular extracts

Proteins, the building blocks of life, often form large assemblies to perform their function but are traditionally studied separately in structural biology. In this issue of Structure, Skalidis et al. (2022) present a workflow to identify members of intact protein communities and solve their structures de novo to near-atomic resolution.