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 pathobiology of perturbed mutant huntingtin protein-protein interactions in Huntington's disease.

Mutations are at the root of many human diseases. Still, we largely do not exactly understand how they trigger pathogenesis. One, more recent, hypothesis has been that they comprehensively perturb protein-protein interaction (PPI) networks and significantly alter key biological processes. Under this premise, many rare genetic disorders with Mendelian inheritance, like Huntington's disease and several spinocerebellar ataxias, are likely to be caused by complex genotype-phenotype relationships involving abnormal PPIs. These altered PPI networks and their effects on cellular pathways are poorly understood at the molecular level. In this review, we focus on PPIs that are perturbed by the expanded pathogenic polyglutamine tract in huntingtin (HTT), the protein which, in its mutated form, leads to the autosomal dominant, neurodegenerative Huntington's disease. One aspect of perturbed mutant HTT interactions is the formation of abnormal protein species such as fibrils or large neuronal inclusions as a result of homotypic and heterotypic aberrant molecular interactions. This review focuses on abnormal PPIs that are associated with the assembly of mutant HTT aggregates in cells and their potential relevance in disease. Furthermore, the mechanisms and pathobiological processes that may contribute to phenotype development, neuronal dysfunction and toxicity in Huntington's disease brains are also discussed. This article is part of the Special Issue "Proteomics".

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.

Integrated transcriptome and proteome analysis reveals posttranscriptional regulation of ribosomal genes in human brain organoids.

During development of the human cerebral cortex, multipotent neural progenitors generate excitatory neurons and glial cells. Investigations of the transcriptome and epigenome have revealed important gene regulatory networks underlying this crucial developmental event. However, the posttranscriptional control of gene expression and protein abundance during human corticogenesis remains poorly understood. We addressed this issue by using human telencephalic brain organoids grown using a dual reporter cell line to isolate neural progenitors and neurons and performed cell class and developmental stage-specific transcriptome and proteome analysis. Integrating the two datasets revealed modules of gene expression during human corticogenesis. Investigation of one such module uncovered mTOR-mediated regulation of translation of the 5'TOP element-enriched translation machinery in early progenitor cells. We show that in early progenitors partial inhibition of the translation of ribosomal genes prevents precocious translation of differentiation markers. Overall, our multiomics approach proposes novel posttranscriptional regulatory mechanisms crucial for the fidelity of cortical development.

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.

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.

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.

Current Approaches Toward Quantitative Mapping of the Interactome.

Protein-protein interactions (PPIs) play a key role in many, if not all, cellular processes. Disease is often caused by perturbation of PPIs, as recently indicated by studies of missense mutations. To understand the associations of proteins and to unravel the global picture of PPIs in the cell, different experimental detection techniques for PPIs have been established. Genetic and biochemical methods such as the yeast two-hybrid system or affinity purification-based approaches are well suited to high-throughput, proteome-wide screening and are mainly used to obtain qualitative results. However, they have been criticized for not reflecting the cellular situation or the dynamic nature of PPIs. In this review, we provide an overview of various genetic methods that go beyond qualitative detection and allow quantitative measuring of PPIs in mammalian cells, such as dual luminescence-based co-immunoprecipitation, Förster resonance energy transfer or luminescence-based mammalian interactome mapping with bait control. We discuss the strengths and weaknesses of different techniques and their potential applications in biomedical research.

DULIP: A Dual Luminescence-Based Co-Immunoprecipitation Assay for Interactome Mapping in Mammalian Cells.

Mapping of protein-protein interactions (PPIs) is critical for understanding protein function and complex biological processes. Here, we present DULIP, a dual luminescence-based co-immunoprecipitation assay, for systematic PPI mapping in mammalian cells. DULIP is a second-generation luminescence-based PPI screening method for the systematic and quantitative analysis of co-immunoprecipitations using two different luciferase tags. Benchmarking studies with positive and negative PPI reference sets revealed that DULIP allows the detection of interactions with high sensitivity and specificity. Furthermore, the analysis of a PPI reference set with known binding affinities demonstrated that both low- and high-affinity interactions can be detected with DULIP assays. Finally, using the well-characterized interaction between Syntaxin-1 and Munc18, we found that DULIP is capable of detecting the effects of point mutations on interaction strength. Taken together, our studies demonstrate that DULIP is a sensitive and reliable method of great utility for systematic interactome research. It can be applied for interaction screening and validation of PPIs in mammalian cells. Moreover, DULIP permits the specific analysis of mutation-dependent binding patterns.

Spontaneous self-assembly of pathogenic huntingtin exon 1 protein into amyloid structures.

PolyQ (polyglutamine) diseases such as HD (Huntington's disease) or SCA1 (spinocerebellar ataxia type 1) are neurodegenerative disorders caused by abnormally elongated polyQ tracts in human proteins. PolyQ expansions promote misfolding and aggregation of disease-causing proteins, leading to the appearance of nuclear and cytoplasmic inclusion bodies in patient neurons. Several lines of experimental evidence indicate that this process is critical for disease pathogenesis. However, the molecular mechanisms underlying spontaneous polyQ-containing aggregate formation and the perturbation of neuronal processes are still largely unclear. The present chapter reviews the current literature regarding misfolding and aggregation of polyQ-containing disease proteins. We specifically focus on studies that have investigated the amyloidogenesis of polyQ-containing HTTex1 (huntingtin exon 1) fragments. These protein fragments are disease-relevant and play a critical role in HD pathogenesis. We outline potential mechanisms behind mutant HTTex1 aggregation and toxicity, as well as proteins and small molecules that can modify HTTex1 amyloidogenesis in vitro and in vivo. The potential implications of such studies for the development of novel therapeutic strategies are discussed.

Polyglutamine-rich suppressors of huntingtin toxicity act upstream of Hsp70 and Sti1 in spatial quality control of amyloid-like proteins.

Protein conformational maladies such as Huntington Disease are characterized by accumulation of intracellular and extracellular protein inclusions containing amyloid-like proteins. There is an inverse correlation between proteotoxicity and aggregation, so facilitated protein aggregation appears cytoprotective. To define mechanisms for protective protein aggregation, a screen for suppressors of nuclear huntingtin (Htt103Q) toxicity was conducted. Nuclear Htt103Q is highly toxic and less aggregation prone than its cytosolic form, so we identified suppressors of cytotoxicity caused by Htt103Q tagged with a nuclear localization signal (NLS). High copy suppressors of Htt103Q-NLS toxicity include the polyQ-domain containing proteins Nab3, Pop2, and Cbk1, and each suppresses Htt toxicity via a different mechanism. Htt103Q-NLS appears to inactivate the essential functions of Nab3 in RNA processing in the nucleus. Function of Pop2 and Cbk1 is not impaired by nuclear Htt103Q, as their respective polyQ-rich domains are sufficient to suppress Htt103Q toxicity. Pop2 is a subunit of an RNA processing complex and is localized throughout the cytoplasm. Expression of just the Pop2 polyQ domain and an adjacent proline-rich stretch is sufficient to suppress Htt103Q toxicity. The proline-rich domain in Pop2 resembles an aggresome targeting signal, so Pop2 may act in trans to positively impact spatial quality control of Htt103Q. Cbk1 accumulates in discrete perinuclear foci and overexpression of the Cbk1 polyQ domain concentrates diffuse Htt103Q into these foci, which correlates with suppression of Htt toxicity. Protective action of Pop2 and Cbk1 in spatial quality control is dependent upon the Hsp70 co-chaperone Sti1, which packages amyloid-like proteins into benign foci. Protein:protein interactions between Htt103Q and its intracellular neighbors lead to toxic and protective outcomes. A subset of polyQ-rich proteins buffer amyloid toxicity by funneling toxic aggregation intermediates to the Hsp70/Sti1 system for spatial organization into benign species.

The Hsp70/90 cochaperone, Sti1, suppresses proteotoxicity by regulating spatial quality control of amyloid-like proteins.

Conformational diseases are associated with the conversion of normal proteins into aggregation-prone toxic conformers with structures similar to that of ß-amyloid. Spatial distribution of amyloid-like proteins into intracellular quality control centers can be beneficial, but cellular mechanisms for protective aggregation remain unclear. We used a high-copy suppressor screen in yeast to identify roles for the Hsp70 system in spatial organization of toxic polyglutamine-expanded Huntingtin (Huntingtin with 103Q glutamine stretch [Htt103Q]) into benign assemblies. Under toxic conditions, Htt103Q accumulates in unassembled states and speckled cytosolic foci. Subtle modulation of Sti1 activity reciprocally affects Htt toxicity and the packaging of Htt103Q into foci. Loss of Sti1 exacerbates Htt toxicity and hinders foci formation, whereas elevation of Sti1 suppresses Htt toxicity while organizing small Htt103Q foci into larger assemblies. Sti1 also suppresses cytotoxicity of the glutamine-rich yeast prion [RNQ+] while reorganizing speckled Rnq1-monomeric red fluorescent protein into distinct foci. Sti1-inducible foci are perinuclear and contain proteins that are bound by the amyloid indicator dye thioflavin-T. Sti1 is an Hsp70 cochaperone that regulates the spatial organization of amyloid-like proteins in the cytosol and thereby buffers proteotoxicity caused by amyloid-like proteins.