Nanobodies against SARS-CoV-2 non-structural protein Nsp9 inhibit viral replication by targeting innate immunity (preprint)

Nanobodies are emerging as critical tools for drug design. Several have been recently created to serve as inhibitors of SARS-Cov-2 entry in the host cell by targeting surface-exposed Spike protein. However, due to the high frequency of mutations that affect Spike, these nanobodies may not target it to their full potential and as a consequence, inhibition of viral entry may not be efficient. Here we have established a pipeline that instead targets highly conserved viral proteins that are made only after viral entry into the host cell when the SARS-Cov-2 RNA-based genome is translated. As proof of principle, we designed nanobodies against the SARS-CoV-2 non-structural protein Nsp9, required for viral genome replication. To find out if this strategy efficiently blocks viral replication, one of these anti-Nsp9 nanobodies, 2NSP23, previously characterized using immunoassays and NMR spectroscopy for epitope mapping, was encapsulated into lipid nanoparticles (LNP) as mRNA. We show that this nanobody, hereby referred to as LNP-mRNA-2NSP23, is internalized and translated in HEK293 cells. We next infected HEK293-ACE2 cells with multiple SARS-CoV-2 variants and subjected them to LNP-mRNA-2NSP23 treatment. Analysis of total RNA isolated from infected cells treated or untreated with LNP-mRNA-2NSP23 using qPCR and RNA deep sequencing shows that the LNP-mRNA-2NSP23 nanobody protects HEK293-ACE2 cells and suppresses replication of several SARS-CoV-2 variants. These observations indicate that following translation, the nanobody 2NSP23 inhibits viral replication by targeting Nsp9 in living cells. We speculate that LNP-mRNA-2NSP23 may be translated into an innovative technology to generate novel antiviral drugs highly efficient across coronaviruses.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor (preprint)

Protein-protein interactions (PPIs) offer great opportunities to expand the druggable proteome and therapeutically tackle various diseases, but remain challenging targets for drug discovery. Here, we provide a comprehensive pipeline that combines experimental and computational tools to identify and validate PPI targets and perform early-stage drug discovery. We have developed a machine learning approach that prioritizes interactions by analyzing quantitative data from binary PPI assays and AlphaFold-Multimer predictions. Using the quantitative assay LuTHy together with our machine learning algorithm, we identified high-confidence interactions among SARS-CoV-2 proteins for which we predicted three-dimensional structures using AlphaFold Multimer. We employed VirtualFlow to target the contact interface of the NSP10-NSP16 SARS-CoV-2 methyltransferase complex by ultra-large virtual drug screening. Thereby, we identified a compound that binds to NSP10 and inhibits its interaction with NSP16, while also disrupting the methyltransferase activity of the complex, and SARS-CoV-2 replication. Overall, this pipeline will help to prioritize PPI targets to accelerate the discovery of early-stage drug candidates targeting protein complexes and pathways.