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.

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.

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 relationships. 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.

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.

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.

Modeling and high-throughput experimental data uncover the mechanisms underlying 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 β subunits (FSHβ/LHβ). Contrary to , gene induction has a preference for low-frequency GnRH pulses.

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.

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

Gonadotropin-releasing hormone (GnRH) is secreted in brief pulses from the hypothalamus and regulates follicle-stimulating hormone β-subunit (FSHβ) gene expression in pituitary gonadotropes in a frequency-sensitive manner. The mechanisms underlying its preferential and paradoxical induction of FSHβ by low frequency GnRH pulses are incompletely understood. Here, we identify growth differentiation factor 9 (GDF9) as a GnRH-suppressed autocrine inducer of FSHβ gene expression.

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.

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.

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 LβT2 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.

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 LβT2 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.

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.

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.

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.