Project description:Extracellular interactions between cell surface receptors are necessary for signaling and adhesion but identifying them remains technically challenging. We describe a cell-based genome-wide approach employing CRISPR activation to identify receptors for a defined ligand. We show receptors for high-affinity antibodies and low-affinity ligands can be unambiguously identified when used in pools or as individual binding probes. We apply this technique to identify ligands for the adhesion G-protein-coupled receptors and show that the Nogo myelin-associated inhibitory proteins are ligands for ADGRB1. This method will enable extracellular receptor-ligand identification on a genome-wide scale.
Project description:More than 800 G protein-coupled receptors (GPCRs) comprise the largest class of membrane receptors in humans. While there is ample biological understanding and many approved drugs for prototypic GPCRs, most GPCRs still lack well-defined biological ligands and drugs. Here, we report our efforts to tap the potential of understudied GPCRs by developing yeast-based technologies for high-throughput clustered regularly interspaced short palindromic repeats (CRISPR) engineering and GPCR ligand discovery. We refer to these technologies collectively as Dynamic Cyan Induction by Functional Integrated Receptors, or DCyFIR. A major advantage of DCyFIR is that GPCRs and other assay components are CRISPR-integrated directly into the yeast genome, making it possible to decode ligand specificity by profiling mixtures of GPCR-barcoded yeast strains in a single tube. To demonstrate the capabilities of DCyFIR, we engineered a yeast strain library of 30 human GPCRs and their 300 possible GPCR-Gα coupling combinations. Profiling of these 300 strains, using parallel (DCyFIRscreen) and multiplex (DCyFIRplex) DCyFIR modes, recapitulated known GPCR agonism with 100% accuracy, and identified unexpected interactions for the receptors ADRA2B, HCAR3, MTNR1A, S1PR1, and S1PR2. To demonstrate DCyFIR scalability, we profiled a library of 320 human metabolites and discovered several GPCR-metabolite interactions. Remarkably, many of these findings pertained to understudied pharmacologically dark receptors GPR4, GPR65, GPR68, and HCAR3. Experiments on select receptors in mammalian cells confirmed our yeast-based observations, including our discovery that kynurenic acid activates HCAR3 in addition to GPR35, its known receptor. Taken together, these findings demonstrate the power of DCyFIR for identifying ligand interactions with prototypic and understudied GPCRs.
Project description:CRISPR-mediated transcriptional activation (CRISPRa) is a powerful technology for inducing gene expression from endogenous loci with exciting applications in high throughput gain-of-function genomic screens and the engineering of cell-based models. However, current strategies for generating potent, stable, CRISPRa-competent cell lines present limitations for the broad utility of this approach. Here, we provide a high-efficiency, self-selecting CRISPRa enrichment strategy, which combined with piggyBac transposon technology enables rapid production of CRISPRa-ready cell populations compatible with a variety of downstream assays. We complement this with an optimized guide RNA scaffold that significantly enhances CRISPRa functionality. Finally, we describe a synthetic guide RNA tool set that enables transient, population-wide gene activation when used with the self-selecting CRISPRa system. Taken together, this versatile platform greatly enhances the potential for CRISPRa across a wide variety of cellular contexts.
Project description:Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value. In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. Cellinker is available at http://www.rna-society.org/cellinker/. Supplementary data are available at Bioinformatics online.
Project description:Targeted drug delivery relies on two physical processes: the selective binding of a therapeutic particle to receptors on a specific cell membrane, followed by transport of the particle across the membrane. In this article, we address some of the challenges in controlling the thermodynamics and dynamics of these two processes by combining a simple experimental system with a statistical mechanical model. Specifically, we characterize and model multivalent ligand-receptor binding between colloidal particles and fluid lipid bilayers, as well as the surface mobility of membrane-bound particles. We show that the mobility of the receptors within the fluid membrane is key to both the thermodynamics and dynamics of binding. First, we find that the particle-membrane binding free energy-or avidity-is a strongly nonlinear function of the ligand-receptor affinity. We attribute the nonlinearity to a combination of multivalency and recruitment of fluid receptors to the binding site. Our results also suggest that partial wrapping of the bound particles by the membrane enhances avidity further. Second, we demonstrate that the lateral mobility of membrane-bound particles is also strongly influenced by the recruitment of receptors. Specifically, we find that the lateral diffusion coefficient of a membrane-bound particle is dominated by the hydrodynamic drag against the aggregate of receptors within the membrane. These results provide one of the first direct validations of the working theoretical framework for multivalent interactions. They also highlight that the fluidity and elasticity of the membrane are as important as the ligand-receptor affinity in determining the binding and transport of small particles attached to membranes.
Project description:Secreted proteins, which include cytokines, hormones, and growth factors, are extracellular ligands that control key signaling pathways mediating cell-cell communication within and between tissues and organs. Many drugs target secreted ligands and their cell surface receptors. Still, there are hundreds of secreted human proteins that either have no identified receptors ('orphans') or are likely to act through cell surface receptors that have not yet been characterized. Discovery of secreted ligand-receptor interactions by high-throughput screening has been problematic, because the most commonly used high-throughput methods for protein-protein interaction (PPI) screening are not optimized for extracellular interactions. Cell-based screening is a promising technology for the deorphanization of ligand-receptor interactions, because multimerized ligands can enrich for cells expressing low affinity cell surface receptors, and such methods do not require purification of receptor extracellular domains. Here, we present a proteo-genomic cell-based CRISPR activation (CRISPRa) enrichment screening platform employing customized pooled cell surface receptor sgRNA libraries in combination with a magnetic bead selection-based enrichment workflow for rapid, parallel ligand-receptor deorphanization. We curated 80 potentially high-value orphan secreted proteins and ultimately screened 20 secreted ligands against two cell sgRNA libraries with targeted expression of all single-pass (TM1) or multi-pass transmembrane (TM2+) receptors by CRISPRa. We identified previously unknown interactions in 12 of these screens, and validated several of them using surface plasmon resonance and/or cell binding assays. The newly deorphanized ligands include three receptor protein tyrosine phosphatase (RPTP) ligands and a chemokine-like protein that binds to killer immunoglobulin-like receptors (KIRs). These new interactions provide a resource for future investigations of interactions between the human-secreted and membrane proteomes.
Project description:G protein-coupled receptors (GPCRs) are a large family of integral membrane proteins responsible for cellular signal transductions. Identification of therapeutic compounds to regulate physiological processes is an important first step of drug discovery. We proposed MAGELLAN, a novel hierarchical virtual-screening (VS) pipeline, which starts with low-resolution protein structure prediction and structure-based binding-site identification, followed by homologous GPCR detections through structure and orthosteric binding-site comparisons. Ligand profiles constructed from the homologous ligand-GPCR complexes are then used to thread through compound databases for VS. The pipeline was first tested in a large-scale retrospective screening experiment against 224 human Class A GPCRs, where MAGELLAN achieved a median enrichment factor (EF) of 14.38, significantly higher than that using individual ligand profiles. Next, MAGELLAN was examined on 5 and 20 GPCRs from two public VS databases (DUD-E and GPCR-Bench) and resulted in an average EF of 9.75 and 13.70, respectively, which compare favorably with other state-of-the-art docking- and ligand-based methods, including AutoDock Vina (with EF = 1.48/3.16 in DUD-E and GPCR-Bench), DOCK 6 (2.12/3.47 in DUD-E and GPCR-Bench), PoLi (2.2 in DUD-E), and FINDSITECcomb2.0 (2.90 in DUD-E). Detailed data analyses show that the major advantage of MAGELLAN is attributed to the power of ligand profiling, which integrates complementary methods for ligand-GPCR interaction recognition and thus significantly improves the coverage and sensitivity of VS models. Finally, cases studies on opioid and motilin receptors show that new connections between functionally related GPCRs can be visualized in the minimum spanning tree built on the similarities of predicted ligand-binding ensembles, suggesting a novel use of MAGELLAN for GPCR deorphanization.
Project description:Molecular dynamics simulation is a widely employed computational technique for studying the dynamic behavior of molecular systems over time. By simulating macromolecular biological systems consisting of a drug, a receptor and a solvated environment with thousands of water molecules, MD allows for realistic ligand-receptor binding interactions (lrbi) to be studied. In this study, we present MD-ligand-receptor (MDLR), a state-of-the-art software designed to explore the intricate interactions between ligands and receptors over time using molecular dynamics trajectories. Unlike traditional static analysis tools, MDLR goes beyond simply taking a snapshot of ligand-receptor binding interactions (lrbi), uncovering long-lasting molecular interactions and predicting the time-dependent inhibitory activity of specific drugs. With MDLR, researchers can gain insights into the dynamic behavior of complex ligand-receptor systems. Our pipeline is optimized for high-performance computing, capable of efficiently processing vast molecular dynamics trajectories on multicore Linux servers or even multinode HPC clusters. In the latter case, MDLR allows the user to analyze large trajectories in a very short time. To facilitate the exploration and visualization of lrbi, we provide an intuitive Python notebook (Jupyter), which allows users to examine and interpret the results through various graphical representations.
Project description:T cell activation takes place in the context of a spatial and kinetic reorganization of cell surface proteins and signaling molecules at the contact site with an antigen presenting cell, termed the immunological synapse. Coordination of the activation, recruitment, and signaling from T cell receptor (TCR) in conjunction with adhesion and costimulatory receptors regulates both the initiation and duration of signaling that is required for T cell activation. The costimulatory receptor, CD28, is an essential signaling molecule that determines the quality and quantity of T cell immune responses. Although the functional consequences of CD28 engagement are well described, the molecular mechanisms that regulate CD28 function are largely unknown. Using a micropipet adhesion frequency assay, we show that TCR signaling enhances the direct binding between CD28 and its ligand, CD80. Although CD28 is expressed as a homodimer, soluble recombinant CD28 can only bind ligand monovalently. Our data suggest that the increase in CD28-CD28 binding is mediated through a change in CD28 valency. Molecular dynamic simulations and in vitro mutagenesis indicate that mutations at the base of the CD28 homodimer interface, distal to the ligand-binding site, can induce a change in the orientation of the dimer that allows for bivalent ligand binding. When expressed in T cells, this mutation allows for high avidity CD28-CD80 interactions without TCR signaling. Molecular dynamic simulations also suggest that wild type CD28 can stably adopt a bivalent conformation. These results support a model whereby inside-out signaling from the TCR can enhance CD28 ligand interactions by inducing a change in the CD28 dimer interface to allow for bivalent ligand binding and ultimately the transduction of CD28 costimulatory signals that are required for T cell activation.