Cryo-EM structure of the Plasmodium falciparum 80S ribosome bound to the anti-protozoan drug emetine

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

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

GOLEM: Automated and Robust Cryo-EM-Guided Ligand Docking with Explicit Water Molecules

A detailed understanding of ligand-protein interaction is essential for developing rational drug-design strategies. In recent years, technological advances in cryo-electron microscopy (cryo-EM) brought a new era to the structural determination of biological macromolecules and assemblies at high resolution, marking cryo-EM as a promising tool for studying ligand-protein interactions. However, even in high-resolution cryo-EM results, the densities for the bound small-molecule ligands are often of lower quality due to their relatively dynamic and flexible nature, frustrating their accurate coordinate assignment. To address the challenge of ligand modeling in cryo-EM maps, here we report the development of GOLEM (Genetic Optimization of Ligands in Experimental Maps), an automated and robust ligand docking method that predicts a ligand's pose and conformation in cryo-EM maps. GOLEM employs a Lamarckian genetic algorithm to perform a hybrid global/local search for exploring the ligand's conformational, orientational, and positional space. As an important feature, GOLEM explicitly considers water molecules and places them at optimal positions and orientations. GOLEM takes into account both molecular energetics and the correlation with the cryo-EM maps in its scoring function to optimally place the ligand. We have validated GOLEM against multiple cryo-EM structures with a wide range of map resolutions and ligand types, returning ligand poses in excellent agreement with the densities. As a VMD plugin, GOLEM is free of charge and accessible to the community. With these features, GOLEM will provide a valuable tool for ligand modeling in cryo-EM efforts toward drug discovery.

Characterization of ribosome stalling and no-go mRNA decay stimulated by the fragile X protein, FMRP

Loss of functional fragile X mental retardation protein (FMRP) causes fragile X syndrome and is the leading monogenic cause of autism spectrum disorders and intellectual disability. FMRP is most notably a translational repressor and is thought to inhibit translation elongation by stalling ribosomes as FMRP-bound polyribosomes from brain tissue are resistant to puromycin and nuclease treatment. Here, we present data showing that the C-terminal noncanonical RNA-binding domain of FMRP is essential and sufficient to induce puromycin-resistant mRNA•ribosome complexes. Given that stalled ribosomes can stimulate ribosome collisions and no-go mRNA decay (NGD), we tested the ability of FMRP to drive NGD of its target transcripts in neuroblastoma cells. Indeed, FMRP and ribosomal proteins, but not poly(A)-binding protein, were enriched in isolated nuclease-resistant disomes compared to controls. Using siRNA knockdown and RNA-seq, we identified 16 putative FMRP-mediated NGD substrates, many of which encode proteins involved in neuronal development and function. Increased mRNA stability of four putative substrates was also observed when either FMRP was depleted or NGD was prevented via RNAi. Taken together, these data support that FMRP stalls ribosomes but only stimulates NGD of a small select set of transcripts, revealing a minor role of FMRP that would be misregulated in fragile X syndrome.

Visualizing the translation landscape in human cells at high resolution

Obtaining comprehensive structural descriptions of macromolecules within their natural cellular context holds immense potential for understanding fundamental biology and improving health. Here, we present the landscape of protein synthesis inside human cells in unprecedented detail obtained using an approach which combines automated cryo-focused ion beam (FIB) milling and in situ single-particle cryo-electron microscopy (cryo-EM). With this in situ cryo-EM approach we resolved a 2.19 Å consensus structure of the human 80S ribosome and unveiled its 21 distinct functional states, nearly all higher than 3 Å resolution. In contrast to in vitro studies, we identified protein factors, including SERBP1, EDF1 and NAC/3, not enriched on purified ribosomes. Most strikingly, we observed that SERBP1 binds to the ribosome in almost all translating and non-translating states to bridge the 60S and 40S ribosomal subunits. These newly observed binding sites suggest that SERBP1 may serve an important regulatory role in translation. We also uncovered a detailed interface between adjacent translating ribosomes which can form the helical polysome structure. Finally, we resolved high-resolution structures from cells treated with homoharringtonine and cycloheximide, and identified numerous polyamines bound to the ribosome, including a spermidine that interacts with cycloheximide bound at the E site of the ribosome, underscoring the importance of high-resolution in situ studies in the complex native environment. Collectively, our work represents a significant advancement in detailed structural studies within cellular contexts.

Structure of an internal loop motif with three consecutive U•U mismatches from stem-loop 1 in the 3'-UTR of the SARS-CoV-2 genomic RNA

The single-stranded RNA genome of SARS-CoV-2 is highly structured. Numerous helical stem-loop structures interrupted by mismatch motifs are present in the functionally important 5'- and 3'-UTRs. These mismatches modulate local helical geometries and feature unusual arrays of hydrogen bonding donor and acceptor groups. However, their conformational and dynamical properties cannot be directly inferred from chemical probing and are difficult to predict theoretically. A mismatch motif (SL1-motif) consisting of three consecutive U•U base pairs is located in stem-loop 1 of the 3'-UTR. We combined NMR-spectroscopy and MD-simulations to investigate its structure and dynamics. All three U•U base pairs feature two direct hydrogen bonds and are as stable as Watson-Crick A:U base pairs. Plasmodium falciparum 25S rRNA contains a triple U•U mismatch motif (Pf-motif) differing from SL1-motif only with respect to the orientation of the two closing base pairs. Interestingly, while the geometry of the outer two U•U mismatches was identical in both motifs the preferred orientation of the central U•U mismatch was different. MD simulations and potassium ion titrations revealed that the potassium ion-binding mode to the major groove is connected to the different preferred geometries of the central base pair in the two motifs.

Confronting heterogeneity in cryogenic electron microscopy data: Innovative strategies and future perspectives with data-driven methods

The surge in the influx of data from cryogenic electron microscopy (cryo-EM) experiments has intensified the demand for robust algorithms capable of autonomously managing structurally heterogeneous datasets. This presents a wealth of exciting opportunities from a data science viewpoint, inspiring the development of numerous innovative, application-specific methods, many of which leverage contemporary data-driven techniques. However, addressing the challenges posed by heterogeneous datasets remains a paramount yet unresolved issue in the field. Here, we explore the subtleties of this challenge and the array of strategies devised to confront it. We pinpoint the shortcomings of existing methodologies and deliberate on prospective avenues for improvement. Specifically, our discussion focuses on strategies to mitigate model overfitting and manage data noise, as well as the effects of constraints, priors, and invariances on the optimization process.

Improved Denoising of Cryo-Electron Microscopy Micrographs with Simulation-Aware Pretraining

Cryo-electron microscopy (cryo-EM) has emerged as a potent technique for determining the structure and functionality of biological macromolecules. However, limited by the physical imaging conditions, such as low electron beam dose, micrographs in cryo-EM typically contend with an extremely low signal-to-noise ratio (SNR), impeding the efficiency and efficacy of subsequent analyses. Therefore, there is a growing demand for an efficient denoising algorithm designed for cryo-EM micrographs, aiming to enhance the quality of macromolecular analysis. However, owing to the absence of a comprehensive and well-defined dataset with ground truth images, supervised image denoising methods exhibit limited generalization when applied to experimental micrographs. To tackle this challenge, we introduce a simulation-aware image denoising (SaID) pretrained model designed to enhance the SNR of cryo-EM micrographs where the training is solely based on an accurately simulated dataset. First, we propose a parameter calibration algorithm for simulated dataset generation, aiming to align simulation parameters with those of experimental micrographs. Second, leveraging the accurately simulated dataset, we propose to train a deep general denoising model that can well generalize to real experimental cryo-EM micrographs. Comprehensive experimental results demonstrate that our pretrained denoising model achieves excellent denoising performance on experimental cryo-EM micrographs, significantly streamlining downstream analysis.

CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net

Picking protein particles in cryo-electron microscopy (cryo-EM) micrographs is a crucial step in the cryo-EM-based structure determination. However, existing methods trained on a limited amount of cryo-EM data still cannot accurately pick protein particles from noisy cryo-EM images. The general foundational artificial intelligence-based image segmentation model such as Meta's Segment Anything Model (SAM) cannot segment protein particles well because their training data do not include cryo-EM images. Here, we present a novel approach (CryoSegNet) of integrating an attention-gated U-shape network (U-Net) specially designed and trained for cryo-EM particle picking and the SAM. The U-Net is first trained on a large cryo-EM image dataset and then used to generate input from original cryo-EM images for SAM to make particle pickings. CryoSegNet shows both high precision and recall in segmenting protein particles from cryo-EM micrographs, irrespective of protein type, shape and size. On several independent datasets of various protein types, CryoSegNet outperforms two top machine learning particle pickers crYOLO and Topaz as well as SAM itself. The average resolution of density maps reconstructed from the particles picked by CryoSegNet is 3.33 Å, 7% better than 3.58 Å of Topaz and 14% better than 3.87 Å of crYOLO. It is publicly available at https://github.com/jianlin-cheng/CryoSegNet.

Swin-cryoEM: Multi-class cryo-electron micrographs single particle mixed detection method

Cryo-electron micrograph images have various characteristics such as varying sizes, shapes, and distribution densities of individual particles, severe background noise, high levels of impurities, irregular shapes, blurred edges, and similar color to the background. How to demonstrate good adaptability in the field of image vision by picking up single particles from multiple types of cryo-electron micrographs is currently a challenge in the field of cryo-electron micrographs. This paper combines the characteristics of the MixUp hybrid enhancement algorithm, enhances the image feature information in the pre-processing stage, builds a feature perception network based on the channel self-attention mechanism in the forward network of the Swin Transformer model network, achieving adaptive adjustment of self-attention mechanism between different single particles, increasing the network's tolerance to noise, Incorporating PReLU activation function to enhance information exchange between pixel blocks of different single particles, and combining the Cross-Entropy function with the softmax function to construct a classification network based on Swin Transformer suitable for cryo-electron micrograph single particle detection model (Swin-cryoEM), achieving mixed detection of multiple types of single particles. Swin-cryoEM algorithm can better solve the problem of good adaptability in picking single particles of many types of cryo-electron micrographs, improve the accuracy and generalization ability of the single particle picking method, and provide high-quality data support for the three-dimensional reconstruction of a single particle. In this paper, ablation experiments and comparison experiments were designed to evaluate and compare Swin-cryoEM algorithms in detail and comprehensively on multiple datasets. The Average Precision is an important evaluation index of the evaluation model, and the optimal Average Precision reached 95.5% in the training stage Swin-cryoEM, and the single particle picking performance was also superior in the prediction stage. This model inherits the advantages of the Swin Transformer detection model and is superior to mainstream models such as Faster R-CNN and YOLOv5 in terms of the single particle detection capability of cryo-electron micrographs.

The Giardia lamblia ribosome structure reveals divergence in several biological pathways and the mode of emetine function

Giardia lamblia is a deeply branching protist and a human pathogen. Its unusual biology presents the opportunity to explore conserved and fundamental molecular mechanisms. We determined the structure of the G. lamblia 80S ribosome bound to tRNA, mRNA, and the antibiotic emetine by cryo-electron microscopy, to an overall resolution of 2.49 Å. The structure reveals rapidly evolving protein and nucleotide regions, differences in the peptide exit tunnel, and likely altered ribosome quality control pathways. Examination of translation initiation factor binding sites suggests these interactions are conserved despite a divergent initiation mechanism. Highlighting the potential of G. lamblia to resolve conserved biological principles; our structure reveals the interactions of the translation inhibitor emetine with the ribosome and mRNA, thus providing insight into the mechanism of action for this widely used antibiotic. Our work defines key questions in G. lamblia and motivates future experiments to explore the diversity of eukaryotic gene regulation.

A glimpse into Giardia lamblia unique translational machinery

Eiler et al. used cryo-electron microscopy to determine a 2.49 Å resolution structure of Giardia lamblia 80S ribosome bound to tRNA, mRNA, and the anti-protozoal drug emetine. The structure reveals some critical aspects of translation in G. lamblia, including the lack of ribosomal protein RACK1, and how emetine blocks translation by interacting with both the ribosome and mRNA.

Mechanisms of antiviral action and toxicities of ipecac alkaloids: Emetine and dehydroemetine exhibit anti-coronaviral activities at non-cardiotoxic concentrations

The emergence of highly infectious pathogens with their potential for triggering global pandemics necessitate the development of effective treatment strategies, including broad-spectrum antiviral therapies to safeguard human health. This study investigates the antiviral activity of emetine, dehydroemetine (DHE), and congeneric compounds against SARS-CoV-2 and HCoV-OC43, and evaluates their impact on the host cell. Concurrently, we assess the potential cardiotoxicity of these ipecac alkaloids. Significantly, our data reveal that emetine and the (-)-R,S isomer of 2,3-dehydroemetine (designated in this paper as DHE4) reduce viral growth at nanomolar concentrations (i.e., IC50 ∼ 50-100 nM), paralleling those required for inhibition of protein synthesis, while calcium channel blocking activity occurs at elevated concentrations (i.e., IC50 ∼ 40-60 µM). Our findings suggest that the antiviral mechanisms primarily involve disruption of host cell protein synthesis and is demonstrably stereoisomer specific. The prospect of a therapeutic window in which emetine or DHE4 inhibit viral propagation without cardiotoxicity renders these alkaloids viable candidates in strategies worthy of clinical investigation.

Proteome-Wide Identification of RNA-dependent proteins and an emerging role for RNAs in Plasmodium falciparum protein complexes

Ribonucleoprotein complexes are composed of RNA, RNA-dependent proteins (RDPs) and RNA-binding proteins (RBPs), and play fundamental roles in RNA regulation. However, in the human malaria parasite, Plasmodium falciparum, identification and characterization of these proteins are particularly limited. In this study, we use an unbiased proteome-wide approach, called R-DeeP, a method based on sucrose density gradient ultracentrifugation, to identify RDPs. Quantitative analysis by mass spectrometry identifies 898 RDPs, including 545 proteins not yet associated with RNA. Results are further validated using a combination of computational and molecular approaches. Overall, this method provides the first snapshot of the Plasmodium protein-protein interaction network in the presence and absence of RNA. R-DeeP also helps to reconstruct Plasmodium multiprotein complexes based on co-segregation and deciphers their RNA-dependence. One RDP candidate, PF3D7_0823200, is functionally characterized and validated as a true RBP. Using enhanced crosslinking and immunoprecipitation followed by high-throughput sequencing (eCLIP-seq), we demonstrate that this protein interacts with various Plasmodium non-coding transcripts, including the var genes and ap2 transcription factors.

Characterization of ribosome stalling and no-go mRNA decay stimulated by the Fragile X protein, FMRP

Loss of functional fragile X mental retardation protein (FMRP) causes fragile X syndrome (FXS) and is the leading monogenic cause of autism spectrum disorders and intellectual disability. FMRP is most notably a translational repressor and is thought to inhibit translation elongation by stalling ribosomes as FMRP-bound polyribosomes from brain tissue are resistant to puromycin and nuclease treatment. Here, we present data showing that the C-terminal non-canonical RNA-binding domain of FMRP is essential and sufficient to induce puromycin-resistant mRNA•ribosome complexes. Given that stalled ribosomes can stimulate ribosome collisions and no-go mRNA decay (NGD), we tested the ability of FMRP to drive NGD of its target transcripts in neuroblastoma cells. Indeed, FMRP and ribosomal proteins, but not PABPC1, were enriched in isolated nuclease-resistant disomes compared to controls. Using siRNA knockdown and RNA-seq, we identified 16 putative FMRP-mediated NGD substrates, many of which encode proteins involved in neuronal development and function. Increased mRNA stability of the putative substrates was also observed when either FMRP was depleted or NGD was prevented via RNAi. Taken together, these data support that FMRP stalls ribosomes and can stimulate NGD of a select set of transcripts in cells, revealing an unappreciated role of FMRP that would be misregulated in FXS.

In-silico evaluation of natural alkaloids against the main protease and spike glycoprotein as potential therapeutic agents for SARS-CoV-2

Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) is the causative agent of COVID-19 pandemic, which has resulted in global fatalities since late December 2019. Alkaloids play a significant role in drug design for various antiviral diseases, which makes them viable candidates for treating COVID-19. To identify potential antiviral agents, 102 known alkaloids were subjected to docking studies against the two key targets of SARS-CoV-2, namely the spike glycoprotein and main protease. The spike glycoprotein is vital for mediating viral entry into host cells, and main protease plays a crucial role in viral replication; therefore, they serve as compelling targets for therapeutic intervention in combating the disease. From the selection of alkaloids, the top 6 dual inhibitory compounds, namely liensinine, neferine, isoliensinine, fangchinoline, emetine, and acrimarine F, emerged as lead compounds with favorable docked scores. Interestingly, most of them shared the bisbenzylisoquinoline alkaloid framework and belong to Nelumbo nucifera, commonly known as the lotus plant. Docking analysis was conducted by considering the key active site residues of the selected proteins. The stability of the top three ligands with the receptor proteins was further validated through dynamic simulation analysis. The leads underwent ADMET profiling, bioactivity score analysis, and evaluation of drug-likeness and physicochemical properties. Neferine demonstrated a particularly strong affinity for binding, with a docking score of -7.5025 kcal/mol for main protease and -10.0245 kcal/mol for spike glycoprotein, and therefore a strong interaction with both target proteins. Of the lead alkaloids, emetine and fangchinoline demonstrated the lowest toxicity and high LD50 values. These top alkaloids, may support the body's defense and reduce the symptoms by their numerous biological potentials, even though some properties naturally point to their direct antiviral nature. These findings demonstrate the promising anti-COVID-19 properties of the six selected alkaloids, making them potential candidates for drug design. This study will be beneficial in effective drug discovery and design against COVID-19 with negligible side effects.

Structural basis of branching during RNA splicing

Branching is a critical step in RNA splicing that is essential for 5' splice site selection. Recent spliceosome structures have led to competing models for the recognition of the invariant adenosine at the branch point. However, there are no structures of any splicing complex with the adenosine nucleophile docked in the active site and positioned to attack the 5' splice site. Thus we lack a mechanistic understanding of adenosine selection and splice site recognition during RNA splicing. Here we present a cryo-electron microscopy structure of a group II intron that reveals that active site dynamics are coupled to the formation of a base triple within the branch-site helix that positions the 2'-OH of the adenosine for nucleophilic attack on the 5' scissile phosphate. This structure, complemented with biochemistry and comparative analyses to splicing complexes, supports a base triple model of adenosine recognition for branching within group II introns and the evolutionarily related spliceosome.

Single-cell quantitative bioimaging of P. berghei liver stage translation

IMPORTANCE

Plasmodium parasites cause malaria in humans. New multistage active antimalarial drugs are needed, and a promising class of drugs targets the core cellular process of translation, which has many potential molecular targets. During the obligate liver stage, Plasmodium parasites grow in metabolically active hepatocytes, making it challenging to study core cellular processes common to both host cells and parasites, as the signal from the host typically overwhelms that of the parasite. Here, we present and validate a flexible assay to quantify Plasmodium liver stage translation using a technique to fluorescently label the newly synthesized proteins of both host and parasite followed by computational separation of their respective nascent proteomes in confocal image sets. We use the assay to determine whether a test set of known compounds are direct or indirect liver stage translation inhibitors and show that the assay can also predict the mode of action for novel antimalarial compounds.

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

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

Prognostic value of amino acid metabolism-related gene expression in invasive breast carcinoma

BACKGROUND

An increasing number of studies indicated that metabolic reprogramming of amino acid metabolism may either promote or inhibit tumor progression. The purpose of this study was to investigate the ability of a gene risk signature associated with amino acid metabolism to predict the prognosis and immune characteristics of invasive breast carcinoma.

METHODS

LASSO Cox regression analysis was performed to construct and validate the prognostic risk signature based on the expression of 9 amino acid metabolism-related genes. The predictive value of the signature, immune characteristics, and chemotherapeutic drugs was also predicted. Finally, 9 significant genes were examined in MDA-MB-231 and MCF-7 cells, and the predicted chemotherapeutic drugs were also verified.

RESULTS

The prognosis of the low-risk group was better than that of the high-risk group. The areas under the curve (AUCs) at 1, 2, and 3 years were 0.852, 0.790, and 0.736, respectively. In addition, the GSEA results for KEGG and GO revealed that samples with a high-risk score exhibited a variety of highly malignant manifestations. The high-risk group was characterized by an increased number of M2 macrophages, a high level of tumor purity, low levels of APC co-stimulation, cytolytic activity, HLA, para-inflammation, and type I IFN response. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) confirmed that MDA-MB-231 and MCF-7 cells express 9 amino acid metabolism-related genes differently. In addition, cell experiments were conducted to examine the effect of cephaeline-induced on cell viability, migration ability, and protein expression of the PI3K/AKT signaling pathway and HIF-1α.

CONCLUSION

We established a risk signature based on 9 amino acid metabolism-related genes for invasive breast carcinoma. Further analyses revealed that this risk signature is superior to other clinical indexes in survival prediction and that the subgroups identified by the risk signature exhibit distinct immune characteristics. Cephaeline was determined to be a superior option for patients in high-risk groups.

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

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

Single-cell quantitative bioimaging of P. berghei liver stage translation

Plasmodium parasite resistance to existing antimalarial drugs poses a devastating threat to the lives of many who depend on their efficacy. New antimalarial drugs and novel drug targets are in critical need, along with novel assays to accelerate their identification. Given the essentiality of protein synthesis throughout the complex parasite lifecycle, translation inhibitors are a promising drug class, capable of targeting the disease-causing blood stage of infection, as well as the asymptomatic liver stage, a crucial target for prophylaxis. To identify compounds capable of inhibiting liver stage parasite translation, we developed an assay to visualize and quantify translation in the P. berghei-HepG2 infection model. After labeling infected monolayers with o-propargyl puromycin (OPP), a functionalized analog of puromycin permitting subsequent bioorthogonal addition of a fluorophore to each OPP-terminated nascent polypetide, we use automated confocal feedback microscopy followed by batch image segmentation and feature extraction to visualize and quantify the nascent proteome in individual P. berghei liver stage parasites and host cells simultaneously. After validation, we demonstrate specific, concentration-dependent liver stage translation inhibition by both parasite-selective and pan-eukaryotic active compounds, and further show that acute pre-treatment and competition modes of the OPP assay can distinguish between direct and indirect translation inhibitors. We identify a Malaria Box compound, MMV019266, as a direct translation inhibitor in P. berghei liver stages and confirm this potential mode of action in P. falciparum asexual blood stages.

Formation characteristics of long-term memory in Bactrocera dorsalis

Studies on insects have contributed significantly to a better understanding of learning and memory, which is a necessary cognitive capability for all animals. Although the formation of memory has been studied in some model insects, more evidence is required to clarify the characteristics of memory formation, especially long-term memory (LTM), which is important for reliably storing information. Here, we explored this question by examining Bactrocera dorsalis, an agricultural pest with excellent learning abilities. Using the classical conditioning paradigm of the olfactory proboscis extension reflex (PER), we found that paired conditioning with multiple trials (>3) spaced with an intertrial interval (≥10 min) resulted in stable memory that lasted for at least 3 d. Furthermore, even a single conditioning trial was sufficient for the formation of a 2-d memory. With the injection of protein inhibitors, protein-synthesis-dependent memory was confirmed to start 4 h after training, and its dependence on translation and transcription differed. Moreover, the results revealed that the dependence of memory on protein translation exhibited a time-window effect (4-6 h). Our findings provide an integrated view of LTM in insects, suggesting common mechanisms in LTM formation that play a key role in the biological basis of memory.

Potential treatments of COVID-19: Drug repurposing and therapeutic interventions

The coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The infection is caused when Spike-protein (S-protein) present on the surface of SARS-CoV-2 interacts with human cell surface receptor, Angiotensin-converting enzyme 2 (ACE2). This binding facilitates SARS-CoV-2 genome entry into the human cells, which in turn causes infection. Since the beginning of the pandemic, many different therapies have been developed to combat COVID-19, including treatment and prevention. This review is focused on the currently adapted and certain other potential therapies for COVID-19 treatment, which include drug repurposing, vaccines and drug-free therapies. The efficacy of various treatment options is constantly being tested through clinical trials and in vivo studies before they are made medically available to the public.

Genome-Scale CRISPR Screening Reveals Host Factors Required for Ribosome Formation and Viral Replication

Viruses are known to co-opt host machinery for translation initiation, but less is known about which host factors are required for the formation of ribosomes used to synthesize viral proteins. Using a loss-of-function CRISPR screen, we show that synthesis of a flavivirus-encoded fluorescent reporter depends on multiple host factors, including several 60S ribosome biogenesis proteins. Viral phenotyping revealed that two of these factors, SBDS, a known ribosome biogenesis factor, and the relatively uncharacterized protein SPATA5, were broadly required for replication of flaviviruses, coronaviruses, alphaviruses, paramyxoviruses, an enterovirus, and a poxvirus. Mechanistic studies revealed that loss of SPATA5 caused defects in rRNA processing and ribosome assembly, suggesting that this human protein may be a functional ortholog of yeast Drg1. These studies implicate specific ribosome biogenesis proteins as viral host dependency factors that are required for synthesis of virally encoded protein and accordingly, optimal viral replication. IMPORTANCE Viruses are well known for their ability to co-opt host ribosomes to synthesize viral proteins. The specific factors involved in translation of viral RNAs are not fully described. In this study, we implemented a unique genome-scale CRISPR screen to identify previously uncharacterized host factors that are important for the synthesis of virally encoded protein. We found that multiple genes involved in 60S ribosome biogenesis were required for viral RNA translation. Loss of these factors severely impaired viral replication. Mechanistic studies on the AAA ATPase SPATA5 indicate that this host factor is required for a late step in ribosome formation. These findings reveal insight into the identity and function of specific ribosome biogenesis proteins that are critical for viral infections.

A strategy combining denoising and cryo-EM single particle analysis

In cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), high-resolution three-dimensional structures of biological macromolecules are determined by iteratively aligning and averaging a large number of two-dimensional projections of molecules. Since the correlation measures are sensitive to the signal-to-noise ratio, various parameter estimation steps in SPA will be disturbed by the high-intensity noise in cryo-EM. However, denoising algorithms tend to damage high frequencies and suppress mid- and high-frequency contrast of micrographs, which exactly the precise parameter estimation relies on, therefore, limiting their application in SPA. In this study, we suggest combining a cryo-EM image processing pipeline with denoising and maximizing the signal's contribution in various parameter estimation steps. To solve the inherent flaws of denoising algorithms, we design an algorithm named MScale to correct the amplitude distortion caused by denoising and propose a new orientation determination strategy to compensate for the high-frequency loss. In the experiments on several real datasets, the denoised particles are successfully applied in the class assignment estimation and orientation determination tasks, ultimately enhancing the quality of biomacromolecule reconstruction. The case study on classification indicates that our strategy not only improves the resolution of difficult classes (up to 5 Å) but also resolves an additional class. In the case study on orientation determination, our strategy improves the resolution of the final reconstructed density map by 0.34 Å compared with conventional strategy. The code is available at https://github.com/zhanghui186/Mscale.

ZART: A novel multiresolution reconstruction algorithm with motion-blur correction for single particle analysis

One of the main purposes of CryoEM Single Particle Analysis is to reconstruct the three-dimensional structure of a macromolecule thanks to the acquisition of many particle images representing different poses of the sample. By estimating the orientation of each projected particle, it is possible to recover the underlying 3D volume by multiple 3D reconstruction methods, usually working either in Fourier or in real space. However, the reconstruction from the projected images works under the assumption that all particles in the dataset correspond to the same conformation of the macromolecule. Although this requisite holds for some macromolecules, it is not true for flexible specimens, leading to motion-induced artefacts in the reconstructed CryoEM maps. In this work, we introduce a new Algebraic Reconstruction Technique called ZART, which is able to include continuous flexibility information during the reconstruction process to improve local resolution and reduce motion blurring. The conformational changes are modelled through Zernike3D polynomials. Our implementation allows for a multiresolution description of the macromolecule adapting itself to the local resolution of the reconstructed map. In addition, ZART has also proven to be a useful algorithm in cases where flexibility is not so dominant, as it improves the overall aspect of the reconstructed maps by improving their local and global resolution.

Three-dimensional alignment of density maps in cryo-electron microscopy

A common task in cryo-electron microscopy data processing is to compare three-dimensional density maps of macromolecules. In this paper, we propose an algorithm for aligning three-dimensional density maps, which exploits common lines between projection images of the maps. The algorithm is fully automatic and handles rotations, reflections (handedness), and translations between the maps. In addition, the algorithm is applicable to any type of molecular symmetry without requiring any information regarding the symmetry of the maps. We evaluate our alignment algorithm on publicly available density maps, demonstrating its accuracy and efficiency. The algorithm is available at https://github.com/ShkolniskyLab/emalign.

Signal enhancement for two-dimensional cryo-EM data processing

Different tasks in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM) require enhancing the quality of the highly noisy raw images. To this end, we develop an efficient algorithm for signal enhancement of cryo-EM images. The enhanced images can be used for a variety of downstream tasks, such as two-dimensional classification, removing uninformative images, constructing ab initio models, generating templates for particle picking, providing a quick assessment of the data set, dimensionality reduction, and symmetry detection. The algorithm includes built-in quality measures to assess its performance and alleviate the risk of model bias. We demonstrate the effectiveness of the proposed algorithm on several experimental data sets. In particular, we show that the quality of the resulting images is high enough to produce ab initio models of Å resolution. The algorithm is accompanied by a publicly available, documented, and easy-to-use code.

Fast principal component analysis for cryo-electron microscopy images

Principal component analysis (PCA) plays an important role in the analysis of cryo-electron microscopy (cryo-EM) images for various tasks such as classification, denoising, compression, and ab initio modeling. We introduce a fast method for estimating a compressed representation of the 2-D covariance matrix of noisy cryo-EM projection images affected by radial point spread functions that enables fast PCA computation. Our method is based on a new algorithm for expanding images in the Fourier-Bessel basis (the harmonics on the disk), which provides a convenient way to handle the effect of the contrast transfer functions. For N images of size L × L, our method has time complexity O(NL3 + L4) and space complexity O(NL2 + L3). In contrast to previous work, these complexities are independent of the number of different contrast transfer functions of the images. We demonstrate our approach on synthetic and experimental data and show acceleration by factors of up to two orders of magnitude.

Detecting Non-Overlapping Signals with Dynamic Programming

This paper studies the classical problem of detecting the locations of signal occurrences in a one-dimensional noisy measurement. Assuming the signal occurrences do not overlap, we formulate the detection task as a constrained likelihood optimization problem and design a computationally efficient dynamic program that attains its optimal solution. Our proposed framework is scalable, simple to implement, and robust to model uncertainties. We show by extensive numerical experiments that our algorithm accurately estimates the locations in dense and noisy environments, and outperforms alternative methods.

Estimating conformational landscapes from Cryo-EM particles by 3D Zernike polynomials

The new developments in Cryo-EM Single Particle Analysis are helping us to understand how the macromolecular structure and function meet to drive biological processes. By capturing many states at the particle level, it is possible to address how macromolecules explore different conformations, information that is classically extracted through 3D classification. However, the limitations of classical approaches prevent us from fully understanding the complete conformational landscape due to the reduced number of discrete states accurately reconstructed. To characterize the whole structural spectrum of a macromolecule, we propose an extension of our Zernike3D approach, able to extract per-image continuous flexibility information directly from a particle dataset. Also, our method can be seamlessly applied to images, maps or atomic models, opening integrative possibilities. Furthermore, we introduce the ZART reconstruction algorithm, which considers the Zernike3D deformation fields to revert particle conformational changes during the reconstruction process, thus minimizing the blurring induced by molecular motions.

Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy

Biological macromolecules and assemblies precisely rearrange their atomic 3D structures to execute cellular functions. Understanding the mechanisms by which these molecular machines operate requires insight into the ensemble of structural states they occupy during the functional cycle. Single-particle cryo-electron microscopy (cryo-EM) has become the preferred method to provide near-atomic resolution, structural information about dynamic biological macromolecules elusive to other structure determination methods. Recent advances in cryo-EM methodology have allowed structural biologists not only to probe the structural intermediates of biochemical reactions, but also to resolve different compositional and conformational states present within the same dataset. This article reviews newly developed sample preparation and single-particle analysis (SPA) techniques for high-resolution structure determination of intrinsically dynamic and heterogeneous samples, shedding light upon the intricate mechanisms employed by molecular machines and helping to guide drug discovery efforts.

Emetine, a potent alkaloid for the treatment of SARS-CoV-2 targeting papain-like protease and non-structural proteins: pharmacokinetics, molecular docking and dynamic studies

The main objective of this study is to find out the anti-SARS-CoV-2 potential of emetine by using molecular docking and dynamic simulation approaches. Interestingly, molecular docking studies suggest that Emetine showed significant binding affinity toward Nsp15 (-10.8 kcal/mol) followed by Nsp12 (-9.5 kcal/mol), RNA-dependent RNA polymerase, RdRp (-9.5 kcal/mol), Nsp16 (-9.4 kcal/mol), Nsp10 (-9.2 kcal/mol), Papain-like protein (-9.0 kcal/mol), Nsp13 (-9.0 kcal/mol), Nsp14 (-8.9 kcal/mol) and Spike Protein Receptor Domain (-8.8 kcal/mol) and chymotrypsin-like protease, 3CLpro (-8.5 kcal/mol), respectively, which are essential for viral infection and replication. In addition, molecular dynamic simulation (MD) was also performed for 140 ns to explore the stability behavior of the main targets and inhibitor complexes as well as the binding mechanics of the ligand to the target proteins. The obtained MD results followed by absolute binding energy calculation confirm that the binding of emetine at the level of the various receptors is more stable. The complex EmetineNSP15, mechanistically was stabilized as follows: Emetine first binds to the monomer, after, binds to the second inducing the formation of a dimer which in turn leading to the formation of complex that simulation stabilizes it at a value less than 5 Å. Overall, supported by the powerful and good pharmacokinetic data of Emetine, our findings with clinical trials may be helpful to confirm that Emetine could be promoted in the prevention and eradication of COVID-19 by reducing the severity in the infected persons and therefore can open possible new strategies for drug repositioning. Communicated by Ramaswamy H. Sarma.

CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images

Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physics-based decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetrized loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.

Ab-initio contrast estimation and denoising of cryo-EM images

BACKGROUND AND OBJECTIVE

The contrast of cryo-EM images varies from one to another, primarily due to the uneven thickness of the ice layer. This contrast variation can affect the quality of 2-D class averaging, 3-D ab-initio modeling, and 3-D heterogeneity analysis. Contrast estimation is currently performed during 3-D iterative refinement. As a result, the estimates are not available at the earlier computational stages of class averaging and ab-initio modeling. This paper aims to solve the contrast estimation problem directly from the picked particle images in the ab-initio stage, without estimating the 3-D volume, image rotations, or class averages.

METHODS

The key observation underlying our analysis is that the 2-D covariance matrix of the raw images is related to the covariance of the underlying clean images, the noise variance, and the contrast variability between images. We show that the contrast variability can be derived from the 2-D covariance matrix and we apply the existing Covariance Wiener Filtering (CWF) framework to estimate it. We also demonstrate a modification of CWF to estimate the contrast of individual images.

RESULTS

Our method improves the contrast estimation by a large margin, compared to the previous CWF method. Its estimation accuracy is often comparable to that of an oracle that knows the ground truth covariance of the clean images. The more accurate contrast estimation also improves the quality of image restoration as demonstrated in both synthetic and experimental datasets.

CONCLUSIONS

This paper proposes an effective method for contrast estimation directly from noisy images without using any 3-D volume information. It enables contrast correction in the earlier stage of single particle analysis, and may improve the accuracy of downstream processing.

Visualizing Conformational Space of Functional Biomolecular Complexes by Deep Manifold Learning

The cellular functions are executed by biological macromolecular complexes in nonequilibrium dynamic processes, which exhibit a vast diversity of conformational states. Solving the conformational continuum of important biomolecular complexes at the atomic level is essential to understanding their functional mechanisms and guiding structure-based drug discovery. Here, we introduce a deep manifold learning framework, named AlphaCryo4D, which enables atomic-level cryogenic electron microscopy (cryo-EM) reconstructions that approximately visualize the conformational space of biomolecular complexes of interest. AlphaCryo4D integrates 3D deep residual learning with manifold embedding of pseudo-energy landscapes, which simultaneously improves 3D classification accuracy and reconstruction resolution via an energy-based particle-voting algorithm. In blind assessments using simulated heterogeneous datasets, AlphaCryo4D achieved 3D classification accuracy three times those of alternative methods and reconstructed continuous conformational changes of a 130-kDa protein at sub-3 Å resolution. By applying this approach to analyze several experimental datasets of the proteasome, ribosome and spliceosome, we demonstrate its potential generality in exploring hidden conformational space or transient states of macromolecular complexes that remain hitherto invisible. Integration of this approach with time-resolved cryo-EM further allows visualization of conformational continuum in a nonequilibrium regime at the atomic level, thus potentially enabling therapeutic discovery against highly dynamic biomolecular targets.

Mito-FUNCAT-FACS reveals cellular heterogeneity in mitochondrial translation

Mitochondria possess their own genome that encodes components of oxidative phosphorylation (OXPHOS) complexes, and mitochondrial ribosomes within the organelle translate the mRNAs expressed from the mitochondrial genome. Given the differential OXPHOS activity observed in diverse cell types, cell growth conditions, and other circumstances, cellular heterogeneity in mitochondrial translation can be expected. Although individual protein products translated in mitochondria have been monitored, the lack of techniques that address the variation in overall mitochondrial protein synthesis in cell populations poses analytic challenges. Here, we adapted mitochondrial-specific fluorescent noncanonical amino acid tagging (FUNCAT) for use with fluorescence-activated cell sorting (FACS) and developed mito-FUNCAT-FACS. The click chemistry-compatible methionine analog L-homopropargylglycine (HPG) enabled the metabolic labeling of newly synthesized proteins. In the presence of cytosolic translation inhibitors, HPG was selectively incorporated into mitochondrial nascent proteins and conjugated to fluorophores via the click reaction (mito-FUNCAT). The application of in situ mito-FUNCAT to flow cytometry allowed us to separate changes in net mitochondrial translation activity from those of the organelle mass and detect variations in mitochondrial translation in cancer cells. Our approach provides a useful methodology for examining mitochondrial protein synthesis in individual cells.

High-Throughput Screening Platform To Identify Inhibitors of Protein Synthesis with Potential for the Treatment of Malaria

Artemisinin-based combination therapies have been crucial in driving down the global burden of malaria, the world’s largest parasitic killer. However, their efficacy is now threatened by the emergence of resistance in Southeast Asia and sub-Saharan Africa. Thus, there is a pressing need to develop new antimalarials with diverse mechanisms of action. One area of Plasmodium metabolism that has recently proven rich in exploitable antimalarial targets is protein synthesis, with a compound targeting elongation factor 2 now in clinical development and inhibitors of several aminoacyl-tRNA synthetases in lead optimization. Given the promise of these components of translation as viable drug targets, we rationalized that an assay containing all functional components of translation would be a valuable tool for antimalarial screening and drug discovery. Here, we report the development and validation of an assay platform that enables specific inhibitors of Plasmodium falciparum translation (PfIVT) to be identified. The primary assay in this platform monitors the translation of a luciferase reporter in a P. falciparum lysate-based expression system. Hits identified in this primary assay are assessed in a counterscreen assay that enables false positives that directly interfere with the luciferase to be triaged. The remaining hit compounds are then assessed in an equivalent human IVT assay. This platform of assays was used to screen MMV’s Pandemic and Pathogen Box libraries, identifying several selective inhibitors of protein synthesis. We believe this new high-throughput screening platform has the potential to greatly expedite the discovery of antimalarials that act via this highly desirable mechanism of action.

Association of the receptor for activated C-kinase 1 with ribosomes in Plasmodium falciparum

The receptor for activated C-kinase 1 (RACK1), a highly conserved eukaryotic protein, is known to have many varying biological roles and functions. Previous work has established RACK1 as a ribosomal protein, with defined regions important for ribosome binding in eukaryotic cells. In Plasmodium falciparum, RACK1 has been shown to be required for parasite growth, however, conflicting evidence has been presented about RACK1 ribosome binding and its role in mRNA translation. Given the importance of RACK1 as a regulatory component of mRNA translation and ribosome quality control, the case could be made in parasites that RACK1 either binds or does not bind the ribosome. Here, we used bioinformatics and transcription analyses to further characterize the P. falciparum RACK1 protein. Based on homology modeling and structural analyses, we generated a model of P. falciparum RACK1. We then explored mutant and chimeric human and P. falciparum RACK1 protein binding properties to the human and P. falciparum ribosome. We found that WT, chimeric, and mutant RACK1 exhibit distinct ribosome interactions suggesting different binding characteristics for P. falciparum and human RACK1 proteins. The ribosomal binding of RACK1 variants in human and parasite cells shown here demonstrates that although RACK1 proteins have highly conserved sequences and structures across species, ribosomal binding is affected by species-specific alterations to this protein. In conclusion, we show that in the case of P. falciparum, contrary to the structural data, RACK1 is found to bind ribosomes and actively translating polysomes in parasite cells.

3D reconstruction from cryo-EM projection images using two spherical embeddings

Single-particle analysis (SPA) in cryo-electron microscopy has become a powerful tool for determining and studying the macromolecular structure at an atomic level. However, since the SPA problem is a non-convex optimization problem with enormous search space and there is high level of noise in the input images, the existing methods may produce biased or even wrong final models. In this work, to deal with the problem, consistent constraints from the input data are explored in an embedding space, a 3D spherical surface. More specifically, the orientation of a projection image is represented by two intersection points of the normal vector and the local X-axis vector of the projection image on the unit spherical surface. To determine the orientations of the projection images, the global consistency constraints of the relative orientations of all the projection images are satisfied by two spherical embeddings which estimate the normal vectors and the local X-axis vectors of the projection images respectively. Compared to the traditional methods, the proposed method is shown to be able to rectify the initial computation errors and produce a more accurate estimation of the projection angles, which results in a better final model reconstruction from the noisy image data.

A 3D reconstruction method using two spherical embeddings to resolve projection angles of the cryo-EM images is shown to improve the initial model reconstruction for single-particle analysis.

Recent advances in developing small-molecule inhibitors against SARS-CoV-2

The COVID-19 pandemic caused by the novel SARS-CoV-2 virus has caused havoc across the entire world. Even though several COVID-19 vaccines are currently in distribution worldwide, with others in the pipeline, treatment modalities lag behind. Accordingly, researchers have been working hard to understand the nature of the virus, its mutant strains, and the pathogenesis of the disease in order to uncover possible drug targets and effective therapeutic agents. As the research continues, we now know the genome structure, epidemiological and clinical features, and pathogenic mechanism of SARS-CoV-2. Here, we summarized the potential therapeutic targets involved in the life cycle of the virus. On the basis of these targets, small-molecule prophylactic and therapeutic agents have been or are being developed for prevention and treatment of SARS-CoV-2 infection.

Applications of deep learning in electron microscopy

We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot be processed using manually implemented algorithms. We summarize the various network architectures and error metrics that have been applied to a range of EM-related problems including denoising and inpainting. We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM.

A multi-targeting drug design strategy for identifying potent anti-SARS-CoV-2 inhibitors

The COVID-19, caused by SARS-CoV-2, is threatening public health, and there is no effective treatment. In this study, we have implemented a multi-targeted anti-viral drug design strategy to discover highly potent SARS-CoV-2 inhibitors, which simultaneously act on the host ribosome, viral RNA as well as RNA-dependent RNA polymerases, and nucleocapsid protein of the virus, to impair viral translation, frameshifting, replication, and assembly. Driven by this strategy, three alkaloids, including lycorine, emetine, and cephaeline, were discovered to inhibit SARS-CoV-2 with EC50 values of low nanomolar levels potently. The findings in this work demonstrate the feasibility of this multi-targeting drug design strategy and provide a rationale for designing more potent anti-virus drugs.

Structural parasitology of the malaria parasite Plasmodium falciparum

The difficulty of faithfully recapitulating malarial protein complexes in heterologous expression systems has long impeded structural study for much of the Plasmodium falciparum proteome. However, recent advances in single-particle cryo electron microscopy (cryoEM) now enable structure determination at atomic resolution with significantly reduced requirements for both sample quantity and purity. Combined with recent developments in gene editing, these advances open the door to structure determination and structural proteomics of macromolecular complexes enriched directly from P. falciparum parasites. Furthermore, the combination of cryoEM with the rapidly emerging use of in situ cryo electron tomography (cryoET) to directly visualize ultrastructures and protein complexes in the native cellular context will yield exciting new insights into the molecular machinery underpinning malaria parasite biology and pathogenesis.

The Nitrobenzoxadiazole Derivative NBDHEX Behaves as Plasmodium falciparum Gametocyte Selective Inhibitor with Malaria Parasite Transmission Blocking Activity

This work describes the activity of 6-((7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)thio)hexan-1-ol (NBDHEX) and of its newly identified carboxylic acid metabolite on the human malaria parasite Plasmodium falciparum. NBDHEX has been previously identified as a potent cytotoxic agent against murine and human cancer cells as well as towards the protozoan parasite Giardia duodenalis. We show here that NBDHEX is active in vitro against all blood stages of P. falciparum, with the rare feature of killing the parasite stages transmissible to mosquitoes, the gametocytes, with a 4-fold higher potency than that on the pathogenic asexual stages. This activity importantly translates into blocking parasite transmission through the Anopheles vector in mosquito experimental infections. A mass spectrometry analysis identified covalent NBDHEX modifications in specific cysteine residues of five gametocyte proteins, possibly associated with its antiparasitic effect. The carboxylic acid metabolite of NBDHEX retains the gametocyte preferential inhibitory activity of the parent compound, making this novel P. falciparum transmission-blocking chemotype at least as a new tool to uncover biological processes targetable by gametocyte selective drugs. Both NBDHEX and its carboxylic acid metabolite show very limited in vitro cytotoxicity on VERO cells. This result and previous evidence that NBDHEX shows an excellent in vivo safety profile in mice and is orally active against human cancer xenografts make these molecules potential starting points to develop new P. falciparum transmission-blocking agents, enriching the repertoire of drugs needed to eliminate malaria.

Into the matrix: current methods for mitochondrial translation studies

In addition to the cytoplasmic translation system, eukaryotic cells house additional protein synthesis machinery in mitochondria. The importance of this in organello translation is exemplified by clinical pathologies associated with mutations in mitochondrial translation factors. Although a detailed understanding of mitochondrial translation has long been awaited, quantitative, comprehensive, and spatiotemporal measurements have posed analytic challenges. The recent development of novel approaches for studying mitochondrial protein synthesis has overcome these issues and expands our understanding of the unique translation system. Here, we review the current technologies for the investigation of mitochondrial translation and the insights provided by their application.

RNA Secondary Structurome Revealed Distinct Thermoregulation in Plasmodium falciparum

The development of Plasmodium parasites, a causative agent of malaria, requests two hosts and the completion of 11 different parasite stages during development. Therefore, an efficient and fast response of parasites to various complex environmental changes, such as ambient temperature, pH, ions, and nutrients, is essential for parasite development and survival. Among many of these environmental changes, temperature is a decisive factor for parasite development and pathogenesis, including the thermoregulation of rRNA expression, gametogenesis, and parasite sequestration in cerebral malaria. However, the exact mechanism of how Plasmodium parasites rapidly respond and adapt to temperature change remains elusive. As a fundamental and pervasive regulator of gene expression, RNA structure can be a specific mechanism for fine tuning various biological processes. For example, dynamic and temperature-dependent changes in RNA secondary structures can control the expression of different gene programs, as shown by RNA thermometers. In this study, we applied the in vitro and in vivo transcriptomic-wide secondary structurome approach icSHAPE to measure parasite RNA structure changes with temperature alteration at single-nucleotide resolution for ring and trophozoite stage parasites. Among 3,000 probed structures at different temperatures, our data showed structural changes in the global transcriptome, such as S-type rRNA, HRPII gene, and the erythrocyte membrane protein family. When the temperature drops from 37°C to 26°C, most of the genes in the trophozoite stage cause significantly more changes to the RNA structure than the genes in the ring stage. A multi-omics analysis of transcriptome data from RNA-seq and RNA structure data from icSHAPE reveals that the specific RNA secondary structure plays a significant role in the regulation of transcript expression for parasites in response to temperature changes. In addition, we identified several RNA thermometers (RNATs) that responded quickly to temperature changes. The possible thermo-responsive RNAs in Plasmodium falciparum were further mapped. To this end, we identified dynamic and temperature-dependent RNA structural changes in the P. falciparum transcriptome and performed a comprehensive characterization of RNA secondary structures over the course of temperature stress in blood stage development. These findings not only contribute to a better understanding of the function of the RNA secondary structure but may also provide novel targets for efficient vaccines or drugs.

End-to-end orientation estimation from 2D cryo-EM images

Cryo-electron microscopy (cryo-EM) is a Nobel Prize-winning technique for determining high-resolution 3D structures of biological macromolecules. A 3D structure is reconstructed from hundreds of thousands of noisy 2D projection images. However, existing 3D reconstruction methods are still time-consuming, and one of the major computational bottlenecks is recovering the unknown orientation of the particle in each 2D image. The dominant methods typically exploit an expensive global search on each image to estimate the missing orientations. Here, a novel end-to-end supervised learning method is introduced to directly recover the missing orientations from 2D cryo-EM images. A neural network is used to approximate the mapping from images to orientations. A robust loss function is proposed for optimizing the parameters of the network, which can handle both asymmetric and symmetric 3D structures. Experiments on synthetic data sets with various symmetry types confirm that the neural network is capable of recovering orientations from 2D cryo-EM images, and the results on a real cryo-EM data set further demonstrate its potential under more challenging imaging conditions.