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.

Despite the odds: formation of the SARS-CoV-2 methylation complex. (preprint)

Coronaviruses protect their single-stranded RNA genome with the methylated cap added during the replication. This capping process is carried out by several nonstructural proteins (nsp) encoded in the viral genome. The methylation itself is performed consecutively by two methyltransferases, nsp14 and nsp16, which interact with nsp10 protein acting as a co-factor. The nsp14 protein also carries the exonuclease domain, which also serves as a part of the proofreading system during the replication of the large RNA genome. The available crystal structures suggest that the concomitant interaction between these three proteins is impossible due to the structural clash, and it is generally accepted that the nsp16 and nsp14 bind with the nsp10 separately. Here, we show that nsp14, nsp10, and nsp16 form a methylation complex despite the odds. Due to spatial proximity, this interaction is beneficial for forming mature capped viral mRNA. Further, it modulates the exonuclease activity of nsp14, protecting the viral RNA at the replication site. Our findings show that nsp14 is more amenable to allosteric regulation and may serve as a molecular target for the therapy.