Targeted in situ cross-linking mass spectrometry and integrative modeling reveal the architectures of three proteins from SARS-CoV-2

Mapping protein-protein interactions by mass spectrometry

Protein-protein interactions (PPIs) are essential for numerous biological activities, including signal transduction, transcription control, and metabolism. They play a pivotal role in the organization and function of the proteome, and their perturbation is associated with various diseases, such as cancer, neurodegeneration, and infectious diseases. Recent advances in mass spectrometry (MS)-based protein interactomics have significantly expanded our understanding of the PPIs in cells, with techniques that continue to improve in terms of sensitivity, and specificity providing new opportunities for the study of PPIs in diverse biological systems. These techniques differ depending on the type of interaction being studied, with each approach having its set of advantages, disadvantages, and applicability. This review highlights recent advances in enrichment methodologies for interactomes before MS analysis and compares their unique features and specifications. It emphasizes prospects for further improvement and their potential applications in advancing our knowledge of PPIs in various biological contexts.

SARS-CoV-2 NSP2 as a Potential Delivery Vehicle for Proteins

The development of biomolecule delivery systems is essential for the treatment of various diseases such as cancer, immunological diseases, and metabolic disorders. For the first time, we found that SARS-CoV-2-encoded nonstructural protein 2 (NSP2) can be secreted from the cells, where it is synthesized. Brefeldin A and H89, inhibitors of ER/Golgi secretion pathways, did not inhibit NSP2 secretion. NSP2 is likely secreted via an unconventional secretory pathway. Moreover, both secreted and purified NSP2 proteins were able to traverse the plasma membrane barrier and enter both immortalized human umbilical vein endothelial cells and tumor cell lines. After entry, the NSP2 protein was localized in only the cytoplasm. Cytochalasin D, a potent inhibitor of actin polymerization, inhibited the entry of NSP2. NSP2 can carry other molecules into cells. Burkholderia lethal factor 1, a monomeric toxin from the intracellular pathogen Burkholderia pseudomallei, has demonstrated antitumor activity by targeting host eukaryotic initiation translation factor 4A. An NSP2-BLF1 fusion protein was translocated across the cellular membranes of Huh7 cells and mediated cell killing. By using different approaches, including protein purification, chemical inhibition, and cell imaging, we confirm that NSP2 is able to deliver heterologous proteins into cells. NSP2 can act as a potential delivery vehicle for proteins.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

All-in-One Pseudo-MS3 Method for the Analysis of Gas-Phase Cleavable Protein Crosslinking Reactions

Crosslinking mass spectrometry (XL-MS) supports structure analysis of individual proteins and highly complex whole-cell interactomes. The identification of crosslinked peptides from enzymatic digests remains challenging, especially at the cell level. Empirical methods that use gas-phase cleavable crosslinkers can simplify the identification process by enabling an MS3-based strategy that turns crosslink identification into a simpler problem of detecting two separable peptides. However, the method is limited to select instrument platforms and is challenged by duty cycle constraints. Here, we revisit a pseudo-MS3 concept that incorporates in-source fragmentation, where a fast switch between gentle high-transmission source conditions and harsher in-source fragmentation settings liberates peptides for standard MS2-based peptide identification. We present an all-in-one method where retention time matches between the crosslink precursor and the liberated peptides establish linkage, and MS2 sequencing identifies the source-liberated peptides. We demonstrate that DC4, a very labile cleavable crosslinker, generates high-intensity peptides in-source. Crosslinks can be identified from these liberated peptides, as they are chromatographically well-resolved from monolinks. Using bovine serum albumin (BSA) as a crosslinking test case, we detect 27% more crosslinks with pseudo-MS3 over a best-in-class MS3 method. While performance is slightly lower for whole-cell lysates (generating two-thirds of the identifications of a standard method), we find that 60% of these hits are unique, highlighting the complementarity of the method.

Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem - finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Hybrid structural modeling of alloantibody binding to human leukocyte antigen with rapid and reproducible cross-linking mass spectrometry

Alloantibody recognition of donor human leukocyte antigen (HLA) is associated with poor clinical transplantation outcomes. However, the molecular and structural basis for the alloantibody-HLA interaction is not well understood. Here, we used a hybrid structural modeling approach on a previously studied alloantibody-HLA interacting pair with inputs from ab initio, in silico, and in vitro data. Highly reproducible cross-linking mass spectrometry data were obtained with both discovery- and targeted mass spectrometry-based approaches approaches. The cross-link information was then used together with predicted antibody Fv structure, predicted antibody paratope, and in silico-predicted interacting surface to model the antibody-HLA interaction. This hybrid structural modeling approach closely recapitulates the key interacting residues from a previously solved crystal structure of an alloantibody-HLA-A∗11:01 pair. These results suggest that a predictive-based hybrid structural modeling approach supplemented with cross-linking mass spectrometry data can provide functionally relevant structural models to understand the structural basis of antibody-HLA mismatch in transplantation.

A deep learning model for predicting optimal distance range in crosslinking mass spectrometry data

Macromolecular assemblies play an important role in all cellular processes. While there has recently been significant progress in protein structure prediction based on deep learning, large protein complexes cannot be predicted with these approaches. The integrative structure modeling approach characterizes multi-subunit complexes by computational integration of data from fast and accessible experimental techniques. Crosslinking mass spectrometry is one such technique that provides spatial information about the proximity of crosslinked residues. One of the challenges in interpreting crosslinking datasets is designing a scoring function that, given a structure, can quantify how well it fits the data. Most approaches set an upper bound on the distance between Cα atoms of crosslinked residues and calculate a fraction of satisfied crosslinks. However, the distance spanned by the crosslinker greatly depends on the neighborhood of the crosslinked residues. Here, we design a deep learning model for predicting the optimal distance range for a crosslinked residue pair based on the structures of their neighborhoods. We find that our model can predict the distance range with the area under the receiver-operator curve of 0.86 and 0.7 for intra- and inter-protein crosslinks, respectively. Our deep scoring function can be used in a range of structure modeling applications.

Determination of HER2 binding domain in antigen-antibody complexes based on chemical crosslinking mass spectrometry

Chemical crosslinking (XL) of non-covalent antigen-antibody complexes followed by mass spectrometric identification (MS) of inter-protein crosslinks can provide spatial constraints between relevant residues, which are valuable structural information associated with the molecular binding interface. To highlight the potential of XL/MS in the biopharmaceutical industry, we herein developed and validated an XL/MS workflow that employed a zero-length linker, 1,1'‑carbonyldiimidazole (CDI), and a widely used medium-length linker, disuccinimidyl sulfoxide (DSSO), for fast, accurate determination of antigen domains targeted by therapeutic antibodies. To avoid false identification, system suitability samples and negative samples were designed for all experiments, and all tandem mass spectra were manually examined. To validate the proposed XL/MS workflow, two complexes involving human epidermal growth factor receptor 2 Fc fusion protein (HER2Fc) with known crystal structures, including HER2Fc-pertuzumab and HER2Fc-trastuzumab, have been subjected to CDI and DSSO crosslinking. Crosslinks established by CDI and DSSO between HER2Fc and pertuzumab accurately revealed their interaction interface. CDI crosslinking contributes more than DSSO because of its short spacer arm and high reactivity towards hydroxyl groups, demonstrating its capacity in protein interaction analysis. The correct binding domain cannot be revealed solely based on DSSO in the HER2Fc-trastuzumab complex, because domain proximity revealed by this 7-atom spacer linker cannot be directly translated as binding interfaces. As the first successful XL/MS application in early-stage therapeutic antibody discovery, we analyzed the molecular binding interface between HER2Fc and H-mab, an innovant drug candidate whose paratopes have not been studied yet. We predict that H-mab probably targets HER2 Domain I. The proposed XL/MS workflow can serve as an accurate, fast, and low-cost method to study the interaction between antibodies and large multi-domain antigens. SIGNIFICANCE: This article described a fast, low-consumption approach based on chemical crosslinking mass spectrometry (XL/MS) using two linkers for binding domain determination in multidomain antigen-antibody complexes. Our results highlighted the higher importance of zero-length crosslinks established by CDI than 7-atom DSSO crosslinks, as residue proximity revealed by zero-length crosslinks is closely related to epitope-paratope interaction surfaces. Furthermore, the higher reactivity of CDI towards hydroxyl groups broadens the ranges of possible crosslinks, despite the necessity of delicate operation in CDI crosslinking. We suggest that all established CDI and DSSO crosslinks should be comprehensively considered for correct binding domain analysis because predictions solely based on DSSO might be ambiguous. We have determined the binding interface in the HER2-H-mab using CDI and DSSO, which is the first successful application of XL/MS in real-world early-stage biopharmaceutical development.

Biochemical and HDX Mass Spectral Characterization of the SARS-CoV-2 Nsp1 Protein

A major challenge in defining the pathophysiology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is to better understand virally encoded multifunctional proteins and their interactions with host factors. Among the many proteins encoded by the positive-sense, single-stranded RNA genome, nonstructural protein 1 (Nsp1) stands out due to its impact on several stages of the viral replication cycle. Nsp1 is the major virulence factor that inhibits mRNA translation. Nsp1 also promotes host mRNA cleavage to modulate host and viral protein expression and to suppress host immune functions. To better define how this multifunctional protein can facilitate distinct functions, we characterize SARS-CoV-2 Nsp1 by using a combination of biophysical techniques, including light scattering, circular dichroism, hydrogen/deuterium exchange mass spectrometry (HDX-MS), and temperature-dependent HDX-MS. Our results reveal that the SARS-CoV-2 Nsp1 N- and C-terminus are unstructured in solution, and in the absence of other proteins, the C-terminus has an increased propensity to adopt a helical conformation. In addition, our data indicate that a short helix exists near the C-terminus and adjoins the region that binds the ribosome. Together, these findings provide insights into the dynamic nature of Nsp1 that impacts its functions during infection. Furthermore, our results will inform efforts to understand SARS-CoV-2 infection and antiviral development.

All Domains of SARS-CoV-2 nsp1 Determine Translational Shutoff and Cytotoxicity of the Protein

Replication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) strongly affects cellular metabolism and results in rapid development of the cytopathic effect (CPE). The hallmarks of virus-induced modifications are inhibition of translation of cellular mRNAs and redirection of the cellular translational machinery to the synthesis of virus-specific proteins. The multifunctional nonstructural protein 1 (nsp1) of SARS-CoV-2 is a major virulence factor and a key contributor to the development of translational shutoff. In this study, we applied a wide range of virological and structural approaches to further analyze nsp1 functions. The expression of this protein alone was found to be sufficient to cause CPE. However, we selected several nsp1 mutants exhibiting noncytopathic phenotypes. The attenuating mutations were detected in three clusters, located in the C-terminal helices, in one of the loops of the structured domain and in the junction of the disordered and structured fragment of nsp1. NMR-based analysis of the wild type nsp1 and its mutants did not confirm the existence of a stable β5-strand that was proposed by the X-ray structure. In solution, this protein appears to be present in a dynamic conformation, which is required for its functions in CPE development and viral replication. The NMR data also suggest a dynamic interaction between the N-terminal and C-terminal domains. The identified nsp1 mutations make this protein noncytotoxic and incapable of inducing translational shutoff, but they do not result in deleterious effects on viral cytopathogenicity. IMPORTANCE The nsp1 of SARS-CoV-2 is a multifunctional protein that modifies the intracellular environment for the needs of viral replication. It is responsible for the development of translational shutoff, and its expression alone is sufficient to cause a cytopathic effect (CPE). In this study, we selected a wide range of nsp1 mutants exhibiting noncytopathic phenotypes. The attenuating mutations, clustered in three different fragments of nsp1, were extensively characterized via virological and structural methods. Our data strongly suggest interactions between the nsp1 domains, which are required for the protein's functions in CPE development. Most of the mutations made nsp1 noncytotoxic and incapable of inducing translational shutoff. Most of them did not affect the viability of the viruses, but they did decrease the rates of replication in cells competent in type I IFN induction and signaling. These mutations, and their combinations, in particular, can be used for the development of SARS-CoV-2 variants with attenuated phenotypes.

Cross-linking mass spectrometry for mapping protein complex topologies in situ

Cross-linking mass spectrometry has become an established technology to provide structural information on the topology and dynamics of protein complexes. Readily accessible workflows can provide detailed data on simplified systems, such as purified complexes. However, using this technology to study the structure of protein complexes in situ, such as in organelles, cells, and even tissues, is still a technological frontier. The complexity of these systems remains a considerable challenge, but there have been dramatic improvements in sample handling, data acquisition, and data processing. Here, we summarise these developments and describe the paths towards comprehensive and comparative structural interactomes by cross-linking mass spectrometry.

An Outlook on Dental Practices to Avoid the Oral Transmission of COVID-19

The oral microbiome plays an important role in the maintenance of immune homeostasis, whereas its association with SARS-CoV-2 infection remains under investigation. Since the oral path is one of the transmission routes for COVID-19, we attempt to show the relationship between the oral microbiome, COVID-19 infection, and oral hygiene. We highlight the importance of oral hygiene to control the infection, especially for ICU cases with COVID-19. Moreover, we present the current strategies adapted by in-person dental clinics to overcome the spread of COVID-19. New emerging policies and protocols suggested during the pandemic and their future implementation to minimize virus transmission are also summarized in this review.

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.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

The SARS-CoV-2 protein NSP2 impairs the silencing capacity of the human 4EHP-GIGYF2 complex

There is an urgent need for a molecular understanding of how SARS-CoV-2 influences the machineries of the host cell. Herein, we focused our attention on the capacity of the SARS-CoV-2 protein NSP2 to bind the human 4EHP-GIGYF2 complex, a key factor involved in microRNA-mediated silencing of gene expression. Using in vitro interaction assays, our data demonstrate that NSP2 physically associates with both 4EHP and a central segment in GIGYF2 in the cytoplasm. We also provide functional evidence showing that NSP2 impairs the function of GIGYF2 in mediating translation repression using reporter-based assays. Collectively, these data reveal the potential impact of NSP2 on the post-transcriptional silencing of gene expression in human cells, pointing out 4EHP-GIGYF2 targeting as a possible strategy of SARS-CoV-2 to take over the silencing machinery and to suppress host defenses.

Present Impact of AlphaFold2 Revolution on Structural Biology, and an Illustration With the Structure Prediction of the Bacteriophage J-1 Host Adhesion Device

In 2021, the release of AlphaFold2 - the DeepMind's machine-learning protein structure prediction program - revolutionized structural biology. Results of the CASP14 contest were an immense surprise as AlphaFold2 successfully predicted 3D structures of nearly all submitted protein sequences. The AlphaFold2 craze has rapidly spread the life science community since structural biologists as well as untrained biologists have now the possibility to obtain high-confidence protein structures. This revolution is opening new avenues to address challenging biological questions. Moreover, AlphaFold2 is imposing itself as an essential step of any structural biology project, and requires us to revisit our structural biology workflows. On one hand, AlphaFold2 synergizes with experimental methods including X-ray crystallography and cryo-electron microscopy. On the other hand, it is, to date, the only method enabling structural analyses of large and flexible assemblies resistant to experimental approaches. We illustrate this valuable application of AlphaFold2 with the structure prediction of the whole host adhesion device from the Lactobacillus casei bacteriophage J-1. With the ongoing improvement of AlphaFold2 algorithms and notebooks, there is no doubt that AlphaFold2-driven biological stories will increasingly be reported, which questions the future directions of experimental structural biology.

Clinically observed deletions in SARS-CoV-2 Nsp1 affect its stability and ability to inhibit translation

Nonstructural protein 1 (Nsp1) of SARS‐CoV‐2 inhibits host cell translation through an interaction between its C‐terminal domain and the 40S ribosome. The N‐terminal domain (NTD) of Nsp1 is a target of recurring deletions, some of which are associated with altered COVID‐19 disease progression. Here, we characterize the efficiency of translational inhibition by clinically observed Nsp1 deletion variants. We show that a frequent deletion of residues 79–89 severely reduces the ability of Nsp1 to inhibit translation while not abrogating Nsp1 binding to the 40S. Notably, while the SARS‐CoV‐2 5′ untranslated region enhances translation of mRNA, it does not protect from Nsp1‐mediated inhibition. Finally, thermal stability measurements and structure predictions reveal a correlation between stability of the NTD and the efficiency of translation inhibition.

Translational Control of COVID-19 and Its Therapeutic Implication

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of COVID-19, which has broken out worldwide for more than two years. However, due to limited treatment, new cases of infection are still rising. Therefore, there is an urgent need to understand the basic molecular biology of SARS-CoV-2 to control this virus. SARS-CoV-2 replication and spread depend on the recruitment of host ribosomes to translate viral messenger RNA (mRNA). To ensure the translation of their own mRNAs, the SARS-CoV-2 has developed multiple strategies to globally inhibit the translation of host mRNAs and block the cellular innate immune response. This review provides a comprehensive picture of recent advancements in our understanding of the molecular basis and complexity of SARS-CoV-2 protein translation. Specifically, we summarize how this viral infection inhibits host mRNA translation to better utilize translation elements for translation of its own mRNA. Finally, we discuss the potential of translational components as targets for therapeutic interventions.

Promising Assays for Examining a Putative Role of Ribosomal Heterogeneity in COVID-19 Susceptibility and Severity

The heterogeneity of ribosomes, characterized by structural variations, arises from differences in types, numbers, and/or post-translational modifications of participating ribosomal proteins (RPs), ribosomal RNAs (rRNAs) sequence variants plus post-transcriptional modifications, and additional molecules essential for forming a translational machinery. The ribosomal heterogeneity within an individual organism or a single cell leads to preferential translations of selected messenger RNA (mRNA) transcripts over others, especially in response to environmental cues. The role of ribosomal heterogeneity in SARS-CoV-2 coronavirus infection, propagation, related symptoms, or vaccine responses is not known, and a technique to examine these has not yet been developed. Tools to detect ribosomal heterogeneity or to profile translating mRNAs independently cannot identify unique or specialized ribosome(s) along with corresponding mRNA substrate(s). Concurrent characterizations of RPs and/or rRNAs with mRNA substrate from a single ribosome would be critical to decipher the putative role of ribosomal heterogeneity in the COVID-19 disease, caused by the SARS-CoV-2, which hijacks the host ribosome to preferentially translate its RNA genome. Such a protocol should be able to provide a high-throughput screening of clinical samples in a large population that would reach a statistical power for determining the impact of a specialized ribosome to specific characteristics of the disease. These characteristics may include host susceptibility, viral infectivity and transmissibility, severity of symptoms, antiviral treatment responses, and vaccine immunogenicity including its side effect and efficacy. In this study, several state-of-the-art techniques, in particular, chemical probing of ribosomal components or rRNA structures, proximity ligation to generate rRNA-mRNA chimeras for sequencing, nanopore gating of individual ribosomes, nanopore RNA sequencing and/or structural analyses, single-ribosome mass spectrometry, and microfluidic droplets for separating ribosomes or indexing rRNAs/mRNAs, are discussed. The key elements for further improvement and proper integration of the above techniques to potentially arrive at a high-throughput protocol for examining individual ribosomes and their mRNA substrates in a clinical setting are also presented.

Label-free visual proteomics: Coupling MS- and EM-based approaches in structural biology

Combining diverse experimental structural and interactomic methods allows for the construction of comprehensible molecular encyclopedias of biological systems. Typically, this involves merging several independent approaches that provide complementary structural and functional information from multiple perspectives and at different resolution ranges. A particularly potent combination lies in coupling structural information from cryoelectron microscopy or tomography (cryo-EM or cryo-ET) with interactomic and structural information from mass spectrometry (MS)-based structural proteomics. Cryo-EM/ET allows for sub-nanometer visualization of biological specimens in purified and near-native states, while MS provides bioanalytical information for proteins and protein complexes without introducing additional labels. Here we highlight recent achievements in protein structure and interactome determination using cryo-EM/ET that benefit from additional MS analysis. We also give our perspective on how combining cryo-EM/ET and MS will continue bridging gaps between molecular and cellular studies by capturing and describing 3D snapshots of proteomes and interactomes.

SARS-CoV-2 and the Host Cell: A Tale of Interactions

The ability of a virus to spread between individuals, its replication capacity and the clinical course of the infection are macroscopic consequences of a multifaceted molecular interaction of viral components with the host cell. The heavy impact of COVID-19 on the world population, economics and sanitary systems calls for therapeutic and prophylactic solutions that require a deep characterization of the interactions occurring between virus and host cells. Unveiling how SARS-CoV-2 engages with host factors throughout its life cycle is therefore fundamental to understand the pathogenic mechanisms underlying the viral infection and to design antiviral therapies and prophylactic strategies. Two years into the SARS-CoV-2 pandemic, this review provides an overview of the interplay between SARS-CoV-2 and the host cell, with focus on the machinery and compartments pivotal for virus replication and the antiviral cellular response. Starting with the interaction with the cell surface, following the virus replicative cycle through the characterization of the entry pathways, the survival and replication in the cytoplasm, to the mechanisms of egress from the infected cell, this review unravels the complex network of interactions between SARS-CoV-2 and the host cell, highlighting the knowledge that has the potential to set the basis for the development of innovative antiviral strategies.

Extended ensemble simulations of a SARS-CoV-2 nsp1-5'-UTR complex

Nonstructural protein 1 (nsp1) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a 180-residue protein that blocks translation of host mRNAs in SARS-CoV-2-infected cells. Although it is known that SARS-CoV-2’s own RNA evades nsp1’s host translation shutoff, the molecular mechanism underlying the evasion was poorly understood. We performed an extended ensemble molecular dynamics simulation to investigate the mechanism of the viral RNA evasion. Simulation results suggested that the stem loop structure of the SARS-CoV-2 RNA 5’-untranslated region (SL1) binds to both nsp1’s N-terminal globular region and intrinsically disordered region. The consistency of the results was assessed by modeling nsp1-40S ribosome structure based on reported nsp1 experiments, including the X-ray crystallographic structure analysis, the cryo-EM electron density map, and cross-linking experiments. The SL1 binding region predicted from the simulation was open to the solvent, yet the ribosome could interact with SL1. Cluster analysis of the binding mode and detailed analysis of the binding poses suggest residues Arg124, Lys47, Arg43, and Asn126 may be involved in the SL1 recognition mechanism, consistent with the existing mutational analysis.

The pandemic of COVID-19 is still rampant all over the world as of 2021 June. SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the causative pathogen of COVID-19, encodes a protein called nsp1 (nonstructural protein 1), which modulates and hijacks the ribosome of the infected host cells. With nsp1, infected human cells selectively translate SARS-CoV-2’s RNA, which increases the virus reproduction efficiency while evading the host immunity. Though it has been known that nsp1 recognizes characteristic stem-loop structure at 5’-end of SARS-CoV-2’s RNA (called SL1), the molecular mechanism underlying the recognition has been poorly understood. We investigated the mechanism of selective translation using the all-atom molecular dynamics simulation of nsp1-SL1 complex. Our simulation results suggest that the binding between nsp1 and SL1 is multi-modal. The results also imply that both the N-terminal globular part and the C-terminal flexible tail of nsp1 are involved in the binding. The residues involved in nsp1-SL1 binding coincides with the known mutant analyses of SARS-CoV-1 and SARS-CoV-2, as well as experimental evidence about nsp1-ribosome interactions.

Leveraging crosslinking mass spectrometry in structural and cell biology

Crosslinking mass spectrometry (crosslinking-MS) is a versatile tool providing structural insights into protein conformation and protein-protein interactions. Its medium-resolution residue-residue distance restraints have been used to validate protein structures proposed by other methods and have helped derive models of protein complexes by integrative structural biology approaches. The use of crosslinking-MS in integrative approaches is underpinned by progress in estimating error rates in crosslinking-MS data and in combining these data with other information. The flexible and high-throughput nature of crosslinking-MS has allowed it to complement the ongoing resolution revolution in electron microscopy by providing system-wide residue-residue distance restraints, especially for flexible regions or systems. Here, we review how crosslinking-MS information has been leveraged in structural model validation and integrative modeling. Crosslinking-MS has also been a key technology for cell biology studies and structural systems biology where, in conjunction with cryoelectron tomography, it can provide structural and mechanistic insights directly in situ.