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.

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships1,2. Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome3, transcriptome4 and proteome5 data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes.

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.

Protein Interactomics by Two-Hybrid Methods.

Comprehensive identification of direct, physical interactions between biological macromolecules, such as protein-protein, protein-DNA, and protein-RNA interactions, is critical for our understanding of the function of gene products as well as the global organization and interworkings of various molecular machines within the cell. The accurate and comprehensive detection of direct interactions, however, remains a huge challenge due to the inherent structural complexity arising from various post-transcriptional and translational modifications coupled with huge heterogeneity in concentration, affinity, and subcellular location differences existing for any interacting molecules. This has created a need for developing multiple orthogonal and complementary assays for detecting various types of biological interactions. In this introduction, we discuss the methods developed for measuring different types of molecular interactions with an emphasis on direct protein-protein interactions, critical issues for generating high-quality interactome datasets, and the insights into biological networks and human diseases that current interaction mapping efforts provide. Further, we will discuss what future might lie ahead for the continued evolution of two-hybrid methods and the role of interactomics for expanding the advancement of biomedical science.

Modeling and high-throughput experimental data uncover the mechanisms underlying Fshb gene sensitivity to gonadotropin-releasing hormone pulse frequency.

Neuroendocrine control of reproduction by brain-secreted pulses of gonadotropin-releasing hormone (GnRH) represents a longstanding puzzle about extracellular signal decoding mechanisms. GnRH regulates the pituitary gonadotropin's follicle-stimulating hormone (FSH) and luteinizing hormone (LH), both of which are heterodimers specified by unique ß subunits (FSHß/LHß). Contrary to Lhb, Fshb gene induction has a preference for low-frequency GnRH pulses. To clarify the underlying regulatory mechanisms, we developed three biologically anchored mathematical models: 1) parallel activation of Fshb inhibitory factors (e.g. inhibin α and VGF nerve growth factor-inducible), 2) activation of a signaling component with a refractory period (e.g. G protein), and 3) inactivation of a factor needed for Fshb induction (e.g. growth differentiation factor 9). Simulations with all three models recapitulated the Fshb expression levels obtained in pituitary gonadotrope cells perifused with varying GnRH pulse frequencies. Notably, simulations altering average concentration, pulse duration, and pulse frequency revealed that the apparent frequency-dependent pattern of Fshb expression in model 1 actually resulted from variations in average GnRH concentration. In contrast, models 2 and 3 showed "true" pulse frequency sensing. To resolve which components of this GnRH signal induce Fshb, we developed a high-throughput parallel experimental system. We analyzed over 4,000 samples in experiments with varying near-physiological GnRH concentrations and pulse patterns. Whereas Egr1 and Fos genes responded only to variations in average GnRH concentration, Fshb levels were sensitive to both average concentration and true pulse frequency. These results provide a foundation for understanding the role of multiple regulatory factors in modulating Fshb gene activity.

Characterization of Gonadotrope Secretoproteome Identifies Neurosecretory Protein VGF-derived Peptide Suppression of Follicle-stimulating Hormone Gene Expression.

Reproductive function is controlled by the pulsatile release of hypothalamic gonadotropin-releasing hormone (GnRH), which regulates the expression of the gonadotropins luteinizing hormone and FSH in pituitary gonadotropes. Paradoxically, Fshb gene expression is maximally induced at lower frequency GnRH pulses, which provide a very low average concentration of GnRH stimulation. We studied the role of secreted factors in modulating gonadotropin gene expression. Inhibition of secretion specifically disrupted gonadotropin subunit gene regulation but left early gene induction intact. We characterized the gonadotrope secretoproteome and global mRNA expression at baseline and after Gαs knockdown, which has been found to increase Fshb gene expression (1). We identified 1077 secreted proteins or peptides, 19 of which showed mRNA regulation by GnRH or/and Gαs knockdown. Among several novel secreted factors implicated in Fshb gene regulation, we focused on the neurosecretory protein VGF. Vgf mRNA, whose gene has been implicated in fertility (2), exhibited high induction by GnRH and depended on Gαs In contrast with Fshb induction, Vgf induction occurred preferentially at high GnRH pulse frequency. We hypothesized that a VGF-derived peptide might regulate Fshb gene induction. siRNA knockdown or extracellular immunoneutralization of VGF augmented Fshb mRNA induction by GnRH. GnRH stimulated the secretion of the VGF-derived peptide NERP1. NERP1 caused a concentration-dependent decrease in Fshb gene induction. These findings implicate a VGF-derived peptide in selective regulation of the Fshb gene. Our results support the concept that signaling specificity from the cell membrane GnRH receptor to the nuclear Fshb gene involves integration of intracellular signaling and exosignaling regulatory motifs.

Growth differentiation factor 9 (GDF9) forms an incoherent feed-forward loop modulating follicle-stimulating hormone ß-subunit (FSHß) gene expression.

Gonadotropin-releasing hormone (GnRH) is secreted in brief pulses from the hypothalamus and regulates follicle-stimulating hormone ß-subunit (FSHß) gene expression in pituitary gonadotropes in a frequency-sensitive manner. The mechanisms underlying its preferential and paradoxical induction of FSHß by low frequency GnRH pulses are incompletely understood. Here, we identify growth differentiation factor 9 (GDF9) as a GnRH-suppressed autocrine inducer of FSHß gene expression. GDF9 gene transcription and expression were preferentially decreased by high frequency GnRH pulses. GnRH regulation of GDF9 was concentration-dependent and involved ERK and PKA. GDF9 knockdown or immunoneutralization reduced FSHß mRNA expression. Conversely, exogenous GDF9 induced FSHß expression in immortalized gonadotropes and in mouse primary pituitary cells. GDF9 exposure increased FSH secretion in rat primary pituitary cells. GDF9 induced Smad2/3 phosphorylation, which was impeded by ALK5 knockdown and by activin receptor-like kinase (ALK) receptor inhibitor SB-505124, which also suppressed FSHß expression. Smad2/3 knockdown indicated that FSHß induction by GDF9 involved Smad2 and Smad3. FSHß mRNA induction by GDF9 and GnRH was synergistic. We hypothesized that GDF9 contributes to a regulatory loop that tunes the GnRH frequency-response characteristics of the FSHß gene. To test this, we determined the effects of GDF9 knockdown on FSHß induction at different GnRH pulse frequencies using a parallel perifusion system. Reduction of GDF9 shifted the characteristic pattern of GnRH pulse frequency sensitivity. These results identify GDF9 as contributing to an incoherent feed-forward loop, comprising both intracellular and secreted components, that regulates FSHß expression in response to activation of cell surface GnRH receptors.

Outside the box signaling: secreted factors modulate GnRH receptor-mediated gonadotropin regulation.

Control of gene expression following activation of membrane receptors results from the regulation of intracellular signaling pathways and transcription factors. Accordingly, research to elucidate the regulatory control circuits and cellular data processing mechanisms focuses on intracellular mechanisms. While autocrine and paracrine signaling are acknowledged in endocrinology, secreted factors are not typically recognized as fundamental components of the pathways connecting cell surface receptors to gene control in the nucleus. Studies of the gonadotrope suggest that extracellular regulatory loops may play a central role in the regulation of gonadotropin gene expression by gonadotropin-releasing hormone (GnRH) receptor activation. We review emerging evidence for this phenomenon, which we refer to as exosignaling, in gonadotropin gene control and in other receptor-mediated signaling systems. We propose that basic signaling circuit modules controlling gene expression can be seamlessly distributed across intracellular and exosignaling components that together orchestrate the precise physiological control of gene expression.

G proteins and autocrine signaling differentially regulate gonadotropin subunit expression in pituitary gonadotrope.

Gonadotropin-releasing hormone (GnRH) acts at gonadotropes to direct the synthesis of the gonadotropins, follicle-stimulating hormone (FSH), and luteinizing hormone (LH). The frequency of GnRH pulses determines the pattern of gonadotropin synthesis. Several hypotheses for how the gonadotrope decodes GnRH frequency to regulate gonadotropin subunit genes differentially have been proposed. However, key regulators and underlying mechanisms remain uncertain. We investigated the role of individual G proteins by perturbations using siRNA or bacterial toxins. In LßT2 gonadotrope cells, FSHß gene induction depended predominantly on Gα(q/11), whereas LHß expression depended on Gα(s). Specifically reducing Gα(s) signaling also disinhibited FSHß expression, suggesting the presence of a Gα(s)-dependent signal that suppressed FSH biosynthesis. The presence of secreted factors influencing FSHß expression levels was tested by studying the effects of conditioned media from Gα(s) knockdown and cholera toxin-treated cells on FSHß expression. These studies and related Transwell culture experiments implicate Gα(s)-dependent secreted factors in regulating both FSHß and LHß gene expression. siRNA studies identify inhibinα as a Gα(s)-dependent GnRH-induced autocrine regulatory factor that contributes to feedback suppression of FSHß expression. These results uncover differential regulation of the gonadotropin genes by Gα(q/11) and by Gα(s) and implicate autocrine and gonadotrope-gonadotrope paracrine regulatory loops in the differential induction of gonadotropin genes.

Optimized amplification and single-cell analysis identify GnRH-mediated activation of Rap1b in primary rat gonadotropes.

Identifying the early gene program induced by GnRH would help understand how GnRH-activated signaling pathways modulate gonadotrope secretory response. We previously analyzed GnRH-induced early genes in LßT2 cells, however these lack GnRH self-potentiation, a physiological attribute of gonadotropes. To minimize cellular heterogeneity, rat primary pituitary cultures were enriched for gonadotropes by 40-60% using a sedimentation gradient. Given the limited number of gonadotropes, RNA was amplified prior to microarray analysis. Thirty-three genes were up-regulated 40 min after GnRH stimulation. Real-time PCR confirmed regulation of several transcripts including fosB, c-fos, egr-2 and rap1b, a small GTPase and member of the Ras family. GnRH stimulated rap1b gene expression in gonadotropes, measured by a sensitive single cell assay. Immunocytochemistry revealed increased Rap1 protein in GnRH-stimulated gonadotropes. These data establish rap1b as a novel gene rapidly induced by GnRH and a candidate to modulate gonadotropin secretion in rat gonadotropes.

Differential gene expression in patients with amyotrophic lateral sclerosis.

Our objective was to analyze gene expression pattern in muscles from patients with amyotrophic lateral sclerosis (ALS) and multifocal motor neuropathy (MMN) compared to controls. Biopsied skeletal muscles from three ALS, three MMN and three control subjects had total RNA extracted and subjected to genome-wide gene expression analysis using Affymetrix GeneChip Exon 1.0 ST array. The most significant expression pattern differences were confirmed with RT-PCR in four additional ALS patients. Results showed that over 3000 genes were identified across the groups using q < 10%. Among 50 genes that were overexpressed only in the ALS group were: leucine-rich repeat kinase-2, follistatin, collagen type XIX alpha-1, ceramide kinase-like, sestrin-3 and CXorf64. No genes were significantly overexpressed in MMN alone. Underexpressed genes only in ALS included actinin α3, fructose-1,6-bisphosphatase-2 and homeobox C10; whereas only in MMN: hemoglobin A1 and CXorf64. Ankyrin repeat domain-1 was overexpressed in both groups. Underexpressed genes in both groups included myosin light chain kinase-2, enolase-3 and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-1. Validation analysis using RT-PCR confirmed the data for leucine-rich repeat kinase-2, follistatin, collagen type XIX alpha-1, ceramide kinase-like, sestrin-3 and CXorf64. In conclusion, there is differential tissue-specific gene expression in patients with ALS relative to MMN and controls. Further studies are necessary to evaluate the identified genes in larger patient groups and different tissues.

Characterization of a MAPK scaffolding protein logic gate in gonadotropes.

In the pituitary gonadotropes, both protein kinase C (PKC) and MAPK/ERK signaling cascades are activated by GnRH. Phosphoprotein-enriched in astrocytes 15 (PEA-15) is a cytosolic ERK scaffolding protein, which is expressed in LßT2 gonadotrope cells. Pharmacological inhibition of PKC and small interfering RNA-mediated silencing of Gαq/11 revealed that GnRH induces accumulation of phosphorylated PEA-15 in a PKC-dependent manner. To investigate the potential role of PEA-15 in GnRH signaling, we examined the regulation of ERK subcellular localization and the activation of ribosomal S6 kinase, a substrate of ERK. Results obtained by cellular fractionation/Western blot analysis and immunohistochemistry revealed that GnRH-induced accumulation of phosphorylated ERK in the nucleus was attenuated when PEA-15 expression was reduced. Conversely, in the absence of GnRH stimulation, PEA-15 anchors ERK in the cytosol. Our data suggest that GnRH-induced nuclear translocation of ERK requires its release from PEA-15, which occurs upon PEA-15 phosphorylation by PKC. Additional gene-silencing experiments in GnRH-stimulated cells demonstrated that ribosomal S6 kinase activation was dependent on both PEA-15 and PKC. Furthermore, small interfering RNA-mediated knockdown of PEA-15 caused a reduction in GnRH-stimulated expression of early response genes Egr2 and c-Jun, as well as gonadotropin FSHß-subunit gene expression. PEA-15 knockdown increased LHß and common α-glycoprotein subunit mRNAs, suggesting a possible role in differential regulation of gonadotropin subunit gene expression. We propose that PEA-15 represents a novel point of convergence of the PKC and MAPK/ERK pathways under GnRH stimulation. PKC, ERK, and PEA-15 form an AND logic gate that shapes the response of the gonadotrope cell to GnRH.

Research resource: Gonadotropin-releasing hormone receptor-mediated signaling network in LbetaT2 cells: a pathway-based web-accessible knowledgebase.

The GnRH receptor (GnRHR), expressed at the cell surface of the anterior pituitary gonadotrope, is critical for normal secretion of gonadotropins LH and FSH, pubertal development, and reproduction. The signaling network downstream of the GnRHR and the molecular bases of the regulation of gonadotropin expression have been the subject of intense research. The murine LbetaT2 cell line represents a mature gonadotrope and therefore is an important model for the study of GnRHR-signaling pathways and modulation of the gonadotrope cell by physiological regulators. In order to facilitate access to the information contained in this complex and evolving literature, we have developed a pathway-based knowledgebase that is web hosted. At present, using 106 relevant primary publications, we curated a comprehensive knowledgebase of the GnRHR signaling in the LbetaT2 cell in the form of a process diagram. Positive and negative controls of gonadotropin gene expression, which included GnRH itself, hypothalamic factors, gonadal steroids and peptides, as well as other hormones, were illustrated. The knowledgebase contains 187 entities and 206 reactions. It was assembled using CellDesigner software, which provides an annotated graphic representation of interactions, stored in Systems Biology Mark-up Language. We then utilized Biological Pathway Publisher, a software suite previously developed in our laboratory, to host the knowledgebase in a web-accessible format as a public resource. In addition, the network entities were linked to a public wiki, providing a forum for discussion, updating, and error correction. The GnRHR-signaling network is openly accessible at http://tsb.mssm.edu/pathwayPublisher/GnRHR_Pathway/GnRHR_Pathway_ index.html.

Plato's cave algorithm: inferring functional signaling networks from early gene expression shadows.

Improving the ability to reverse engineer biochemical networks is a major goal of systems biology. Lesions in signaling networks lead to alterations in gene expression, which in principle should allow network reconstruction. However, the information about the activity levels of signaling proteins conveyed in overall gene expression is limited by the complexity of gene expression dynamics and of regulatory network topology. Two observations provide the basis for overcoming this limitation: a. genes induced without de-novo protein synthesis (early genes) show a linear accumulation of product in the first hour after the change in the cell's state; b. The signaling components in the network largely function in the linear range of their stimulus-response curves. Therefore, unlike most genes or most time points, expression profiles of early genes at an early time point provide direct biochemical assays that represent the activity levels of upstream signaling components. Such expression data provide the basis for an efficient algorithm (Plato's Cave algorithm; PLACA) to reverse engineer functional signaling networks. Unlike conventional reverse engineering algorithms that use steady state values, PLACA uses stimulated early gene expression measurements associated with systematic perturbations of signaling components, without measuring the signaling components themselves. Besides the reverse engineered network, PLACA also identifies the genes detecting the functional interaction, thereby facilitating validation of the predicted functional network. Using simulated datasets, the algorithm is shown to be robust to experimental noise. Using experimental data obtained from gonadotropes, PLACA reverse engineered the interaction network of six perturbed signaling components. The network recapitulated many known interactions and identified novel functional interactions that were validated by further experiment. PLACA uses the results of experiments that are feasible for any signaling network to predict the functional topology of the network and to identify novel relationships.