AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.

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 or 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.

The HTLV-1 viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape.

Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

Identification of small molecule antivirals against HTLV-1 by targeting the hDLG1-Tax-1 protein-protein interaction.

Human T-cell leukemia virus type-1 (HTLV-1) is the first pathogenic retrovirus discovered in human. Although HTLV-1-induced diseases are well-characterized and linked to the encoded Tax-1 oncoprotein, there is currently no strategy to target Tax-1 functions with small molecules. Here, we analyzed the binding of Tax-1 to the human homolog of the drosophila discs large tumor suppressor (hDLG1/SAP97), a multi-domain scaffolding protein involved in Tax-1-transformation ability. We have solved the structures of the PDZ binding motif (PBM) of Tax-1 in complex with the PDZ1 and PDZ2 domains of hDLG1 and assessed the binding of 10 million molecules by virtual screening. Among the 19 experimentally confirmed compounds, one systematically inhibited the Tax-1-hDLG1 interaction in different biophysical and cellular assays, as well as HTLV-1 cell-to-cell transmission in a T-cell model. Thus, our work demonstrates that interactions involving Tax-1 PDZ-domains are amenable to small-molecule inhibition, which provides a framework for the design of targeted therapies for HTLV-1-induced diseases.

AI-guided pipeline for protein-protein interaction drug discovery identifies a SARS-CoV-2 inhibitor.

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.

Alternative glycosylation controls endoplasmic reticulum dynamics and tubular extension in mammalian cells.

The endoplasmic reticulum (ER) is a central eukaryotic organelle with a tubular network made of hairpin proteins linked by hydrolysis of guanosine triphosphate nucleotides. Among posttranslational modifications initiated at the ER level, glycosylation is the most common reaction. However, our understanding of the impact of glycosylation on the ER structure remains unclear. Here, we show that exostosin-1 (EXT1) glycosyltransferase, an enzyme involved in N-glycosylation, is a key regulator of ER morphology and dynamics. We have integrated multiomics and superresolution imaging to characterize the broad effect of EXT1 inactivation, including the ER shape-dynamics-function relationships in mammalian cells. We have observed that inactivating EXT1 induces cell enlargement and enhances metabolic switches such as protein secretion. In particular, suppressing EXT1 in mouse thymocytes causes developmental dysfunctions associated with the ER network extension. Last, our data illuminate the physical and functional aspects of the ER proteome-glycome-lipidome structure axis, with implications in biotechnology and medicine.

Maximizing binary interactome mapping with a minimal number of assays.

Complementary assays are required to comprehensively map complex biological entities such as genomes, proteomes and interactome networks. However, how various assays can be optimally combined to approach completeness while maintaining high precision often remains unclear. Here, we propose a framework for binary protein-protein interaction (PPI) mapping based on optimally combining assays and/or assay versions to maximize detection of true positive interactions, while avoiding detection of random protein pairs. We have engineered a novel NanoLuc two-hybrid (N2H) system that integrates 12 different versions, differing by protein expression systems and tagging configurations. The resulting union of N2H versions recovers as many PPIs as 10 distinct assays combined. Thus, to further improve PPI mapping, developing alternative versions of existing assays might be as productive as designing completely new assays. Our findings should be applicable to systematic mapping of other biological landscapes.