Structural basis for substrate selection by the SARS-CoV-2 replicase (preprint)

The SARS-CoV-2 RNA-dependent RNA polymerase coordinates viral RNA synthesis as part of an assembly known as the replication-transcription complex (RTC) 1 . Accordingly, the RTC is a target for clinically approved antiviral nucleoside analogs, including remdesivir 2 . Faithful synthesis of viral RNAs by the RTC requires recognition of the correct nucleotide triphosphate (NTP) for incorporation into the nascent RNA. To be effective inhibitors, antiviral nucleoside analogs must compete with the natural NTPs for incorporation. How the SARS-CoV-2 RTC discriminates between the natural NTPs, and how antiviral nucleoside analogs compete, has not been discerned in detail. Here, we use cryo-electron microscopy to visualize the RTC bound to each of the natural NTPs in states poised for incorporation. Furthermore, we investigate the RTC with the active metabolite of remdesivir, remdesivir triphosphate (RDV-TP), highlighting the structural basis for the selective incorporation of RDV-TP over its natural counterpart ATP 3,4 . Our results elucidate the suite of interactions required for NTP recognition, informing the rational design of antivirals. Our analysis also yields insights into nucleotide recognition by the nsp12 NiRAN, an enigmatic catalytic domain essential for viral propagation 5 . The NiRAN selectively binds GTP, strengthening proposals for the role of this domain in the formation of the 5’ RNA cap 6 .

Ensemble cryo-electron microscopy reveals conformational states of the nsp13 helicase in the SARS-CoV-2 helicase replication-transcription complex (preprint)

The SARS-CoV-2 nonstructural proteins coordinate genome replication and gene expression. Structural analyses revealed the basis for coupling of the essential nsp13 helicase with the RNA dependent RNA polymerase (RdRp) where the holo-RdRp and RNA substrate (the replication-transcription complex, or RTC) associated with two copies of nsp13 (nsp132-RTC). One copy of nsp13 interacts with the template RNA in an opposing polarity to the RdRp and is envisaged to drive the RdRp backwards on the RNA template (backtracking), prompting questions as to how the RdRp can efficiently synthesize RNA in the presence of nsp13. Here, we use cryo-electron microscopy and molecular dynamics simulations to analyze the nsp132-RTC, revealing four distinct conformational states of the helicases. The results suggest a mechanism for the nsp132-RTC to turn backtracking on and off, using an allosteric mechanism to switch between RNA synthesis or backtracking in response to stimuli at the RdRp active site.

Structural basis for backtracking by the SARS-CoV-2 replication-transcription complex (preprint)

Backtracking, the reverse motion of the transcriptase enzyme on the nucleic acid template, is a universal regulatory feature of transcription in cellular organisms but its role in viruses is not established. Here we present evidence that backtracking extends into the viral realm, where backtracking by the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) may aid viral transcription and replication. Structures of SARS-CoV-2 RdRp bound to the essential nsp13 helicase and RNA suggested the helicase facilitates backtracking. We use cryo-electron microscopy, RNA-protein crosslinking, and unbiased molecular dynamics simulations to characterize SARS-CoV-2 RdRp backtracking. The results establish that the single-stranded 3'-segment of the product-RNA generated by backtracking extrudes through the RdRp NTP-entry tunnel, that a mismatched nucleotide at the product-RNA 3'-end frays and enters the NTP-entry tunnel to initiate backtracking, and that nsp13 stimulates RdRp backtracking. Backtracking may aid proofreading, a crucial process for SARS-CoV-2 resistance against antivirals.

Native Mass Spectrometry-Based Screening for Optimal Sample Preparation in Single Particle Cryo-EM (preprint)

Recent advances in single particle cryogenic electron microscopy (cryo-EM) have enabled the structural determination of numerous protein assemblies at high resolution, yielding unprecedented insights into their function. However, despite its extraordinary capabilities, cryo-EM remains time-consuming and resource-intensive. It is therefore beneficial to have a means for rapidly assessing and optimizing the quality of samples prior to lengthy cryo-EM analyses. To do this, we have developed a native mass spectrometry (nMS) platform that provides rapid feedback on sample quality and highly streamlined biochemical screening. Because nMS enables accurate mass analysis of protein complexes, it is well-suited for routine evaluation of the composition, integrity, and homogeneity of samples prior to their plunge-freezing on EM grids. We demonstrate the utility of our nMS-based platform for facilitating cryo-EM studies using structural characterizations of exemplar bacterial transcription complexes as well as the replication-transcription assembly from the SARS-CoV-2 virus that is responsible for the COVID-19 pandemic.Funding: This work is supported by the Pels Foundation to The Rockefeller University, NIH grants P41 GM109824 and P41 GM103314 to BTC, R35 GM118130 to SAD, and R01 GM114450 to EAC. Access to the cryo-EM microscopes and support was through The Rockefeller University Evelyn Gruss Lipper Cryo-EM Resource Center and at The Simons Electron Microscopy Center (SEMC), National Resource for Automated Molecular Microscopy (NRAMM), and National Center for CryoEM Access and Training (NCCAT) at the NYSBC, supported by NIH NIGMS (P41 GM103310), NYSTAR, the Simons Foundation (SF349247), the NIH Common Fund Transformative High Resolution CryoElectron Microscopy program (U24 GM129539) and NY State Assembly Majority. Conflict of Interest: The authors declare there are no competing interests.

Structural basis for helicase-polymerase coupling in the SARS-CoV-2 replication-transcription complex (preprint)

SARS-CoV-2 is the causative agent of the 2019-2020 pandemic. The SARS-CoV-2 genome is replicated-transcribed by the RNA-dependent RNA polymerase holoenzyme (subunits nsp7/nsp82/nsp12) along with a cast of accessory factors. One of these factors is the nsp13 helicase. Both the holo-RdRp and nsp13 are essential for viral replication and are targets for treating the disease COVID-19. Here we present cryo-electron microscopic structures of the SARS-CoV-2 holo-RdRp with an RNA template-product in complex with two molecules of the nsp13 helicase. The Nidovirus-order-specific N-terminal domains of each nsp13 interact with the N-terminal extension of each copy of nsp8. One nsp13 also contacts the nsp12-thumb. The structure places the nucleic acid-binding ATPase domains of the helicase directly in front of the replicating-transcribing holo-RdRp, constraining models for nsp13 function. We also observe ADP-Mg2+ bound in the nsp12 N-terminal nidovirus RdRp-associated nucleotidyltransferase domain, detailing a new pocket for anti-viral therapeutic development.

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs (preprint)

This protocol predicts blueprints for vaccine design that contain a broad repertoire of T-cell epitopes optimized for the global population. The protocol first requires a screening of the SARS-CoV-2 proteome using immunogenicity predictors to generate comprehensive epitope maps. Then, these epitope maps are used as input to Monte Carlo simulations designed to identify statistically significant “epitope hotspot” regions in the virus that are most likely to be immunogenic. The epitope hotspots that share significant homology with proteins in the human proteome are removed to reduce the chance of inducing off-target autoimmune responses. Finally, a database of the actual HLA genotypes of citizens is used to develop a “digital twin” type simulation to model how effective different combinations of hotspots would work in a diverse human population. The approach identifies an optimal constellation of epitope hotspots that could provide maximum coverage in the human population. 

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2: toward universal blueprints for vaccine designs (preprint)

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The goals of this study were to use artificial intelligence (AI) to predict blueprints for designing universal vaccines against SARS-CoV-2, that contain a sufficiently broad repertoire of T-cell epitopes capable of providing coverage and protection across the global population. To help achieve these aims, we profiled the entire SARS-CoV-2 proteome across the most frequent 100 HLA-A, HLA-B and HLA-DR alleles in the human population, using host-infected cell surface antigen presentation and immunogenicity predictors from the NEC Immune Profiler suite of tools, and generated comprehensive epitope maps. We then used these epitope maps as input for a Monte Carlo simulation designed to identify statistically significant "epitope hotspot" regions in the virus that are most likely to be immunogenic across a broad spectrum of HLA types. We then removed epitope hotspots that shared significant homology with proteins in the human proteome to reduce the chance of inducing off-target autoimmune responses. We also analyzed the antigen presentation and immunogenic landscape of all the nonsynonymous mutations across 3400 different sequences of the virus, to identify a trend whereby SARS-COV-2 mutations are predicted to have reduced potential to be presented by host-infected cells, and consequently detected by the host immune system. A sequence conservation analysis then removed epitope hotspots that occurred in less-conserved regions of the viral proteome. Finally, we used a database of the HLA genotypes of approximately 22 000 individuals to develop a "digital twin" type simulation to model how effective different combinations of hotspots would work in a diverse human population, and used the approach to identify an optimal constellation of epitopes hotspots that could provide maximum coverage in the global population. By combining the antigen presentation to the infected-host cell surface and immunogenicity predictions of the NEC Immune Profiler with a robust Monte Carlo and digital twin simulation, we have managed to profile the entire SARS-CoV-2 proteome and identify a subset of epitope hotspots that could be harnessed in a vaccine formulation to provide a broad coverage across the global population.