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.

Physiological Functions of the Volume-Regulated Anion Channel VRAC/LRRC8 and the Proton-Activated Chloride Channel ASOR/TMEM206.

Volume-regulated anion channels (VRACs) and the acid-sensitive outwardly rectifying anion channel (ASOR) mediate flux of chloride and small organic anions. Although known for a long time, they were only recently identified at the molecular level. VRACs are heteromers consisting of LRRC8 proteins A to E. Combining the essential LRRC8A with different LRRC8 paralogues changes key properties of VRAC such as conductance or substrate selectivity, which is how VRACs are involved in multiple physiological functions including regulatory volume decrease, cell proliferation and migration, cell death, purinergic signalling, fat and glucose metabolism, insulin signalling, and spermiogenesis. VRACs are also involved in pathological conditions, such as the neurotoxic release of glutamate and aspartate. Certain VRACs are also permeable to larger, organic anions, including antibiotics and anti-cancer drugs, making them an interesting therapeutic target. ASOR, also named proton-activated chloride channel (PAC), is formed by TMEM206 homotrimers on the plasma membrane and on endosomal compartments where it mediates chloride flux in response to extracytosolic acidification and plays a role in the shrinking and maturation of macropinosomes. ASOR has been shown to underlie neuronal swelling which causes cell death after stroke as well as promoting the metastasis of certain cancers, making them intriguing therapeutic targets as well.

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.

The polyphenol EGCG directly targets intracellular amyloid-ß aggregates and promotes their lysosomal degradation.

The accumulation of amyloidogenic protein aggregates in neurons is a pathogenic hallmark of a large number of neurodegenerative diseases including Alzheimer's disease (AD). Small molecules targeting such structures and promoting their degradation may have therapeutic potential for the treatment of AD. Here, we searched for natural chemical compounds that decrease the abundance of stable, proteotoxic ß-sheet-rich amyloid-ß (Aß) aggregates in cells. We found that the polyphenol (-)-epigallocatechin gallate (EGCG) functions as a potent chemical aggregate degrader in SH-EP cells. We further demonstrate that a novel, fluorescently labeled EGCG derivative (EGC-dihydroxybenzoate (DHB)-Rhodamine) also shows cellular activity. It directly targets intracellular Aß42 aggregates and competes with EGCG for Aß42 aggregate binding in vitro. Mechanistic investigations indicated a lysosomal accumulation of Aß42 aggregates in SH-EP cells and showed that lysosomal cathepsin activity is critical for efficient EGCG-mediated aggregate clearance. In fact, EGCG treatment leads to an increased abundance of active cathepsin B isoforms and increased enzymatic activity in our SH-EP cell model. Our findings suggest that intracellular Aß42 aggregates are cleared through the endo-lysosomal system. We show that EGCG directly targets intracellular Aß42 aggregates and facilitates their lysosomal degradation. Small molecules, which bind to protein aggregates and increase their lysosomal degradation could have therapeutic potential for the treatment of amyloid diseases.

CellFIE: CRISPR- and Cell Fusion-based Two-hybrid Interaction Mapping of Endogenous Proteins.

Numerous genetic methods facilitate the detection of binary protein-protein interactions (PPIs) by exogenous overexpression, which can lead to false results. Here, we describe CellFIE, a CRISPR- and cell fusion-based PPI detection method, which enables the mapping of interactions between endogenously tagged two-hybrid proteins. We demonstrate the specificity and reproducibility of CellFIE in a matrix mapping approach, validating the interactions of VCP with ASPL and UBXD1, and the self-interaction of TDP-43 under endogenous conditions. Furthermore, we show that CellFIE can be used to quantify changes of endogenous PPIs upon stress induction or drug treatment. For the first time, CellFIE facilitates systematic mapping of interactions between endogenously tagged proteins and represents a novel tool to characterize PPIs in live cells under dynamic conditions.

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.

Quantitative interaction mapping reveals an extended UBX domain in ASPL that disrupts functional p97 hexamers.

Interaction mapping is a powerful strategy to elucidate the biological function of protein assemblies and their regulators. Here, we report the generation of a quantitative interaction network, directly linking 14 human proteins to the AAA+ ATPase p97, an essential hexameric protein with multiple cellular functions. We show that the high-affinity interacting protein ASPL efficiently promotes p97 hexamer disassembly, resulting in the formation of stable p97:ASPL heterotetramers. High-resolution structural and biochemical studies indicate that an extended UBX domain (eUBX) in ASPL is critical for p97 hexamer disassembly and facilitates the assembly of p97:ASPL heterotetramers. This spontaneous process is accompanied by a reorientation of the D2 ATPase domain in p97 and a loss of its activity. Finally, we demonstrate that overproduction of ASPL disrupts p97 hexamer function in ERAD and that engineered eUBX polypeptides can induce cell death, providing a rationale for developing anti-cancer polypeptide inhibitors that may target p97 activity.