Structure of tetrameric forms of the serotonin-gated 5-HT3A receptor ion channel.

Multimeric membrane proteins are produced in the endoplasmic reticulum and transported to their target membranes which, for ion channels, is typically the plasma membrane. Despite the availability of many fully assembled channel structures, our understanding of assembly intermediates, multimer assembly mechanisms, and potential functions of non-standard assemblies is limited. We demonstrate that the pentameric ligand-gated serotonin 5-HT3A receptor (5-HT3AR) can assemble to tetrameric forms and report the structures of the tetramers in plasma membranes of cell-derived microvesicles and in membrane memetics using cryo-electron microscopy and tomography. The tetrameric structures have near-symmetric transmembrane domains, and asymmetric extracellular domains, and can bind serotonin molecules. Computer simulations, based on our cryo-EM structures, were used to decipher the assembly pathway of pentameric 5-HT3R and suggest a potential functional role for the tetrameric receptors.

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.

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.

LuTHy: a double-readout bioluminescence-based two-hybrid technology for quantitative mapping of protein-protein interactions in mammalian cells.

Information on protein-protein interactions (PPIs) is of critical importance for studying complex biological systems and developing therapeutic strategies. Here, we present a double-readout bioluminescence-based two-hybrid technology, termed LuTHy, which provides two quantitative scores in one experimental procedure when testing binary interactions. PPIs are first monitored in cells by quantification of bioluminescence resonance energy transfer (BRET) and, following cell lysis, are again quantitatively assessed by luminescence-based co-precipitation (LuC). The double-readout procedure detects interactions with higher sensitivity than traditional single-readout methods and is broadly applicable, for example, for detecting the effects of small molecules or disease-causing mutations on PPIs. Applying LuTHy in a focused screen, we identified 42 interactions for the presynaptic chaperone CSPα, causative to adult-onset neuronal ceroid lipofuscinosis (ANCL), a progressive neurodegenerative disease. Nearly 50% of PPIs were found to be affected when studying the effect of the disease-causing missense mutations L115R and ∆L116 in CSPα with LuTHy. Our study presents a robust, sensitive research tool with high utility for investigating the molecular mechanisms by which disease-associated mutations impair protein activity in biological systems.