Dataset Information

A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing.

ABSTRACT: G protein-coupled receptors (GPCRs) are central to how mammalian cells sense and respond to chemicals. Mammalian olfactory receptors (ORs), the largest family of GPCRs, mediate the sense of smell through activation by small molecules, though for most bonafide ligands, they have not been identified. Here, we introduce a platform to screen large chemical panels against multiplexed GPCR libraries using next-generation sequencing of barcoded genetic reporters in stably engineered human cell lines. We mapped 39 mammalian ORs against 181 odorants and identified 79 interactions that have not been reported to our knowledge, including ligands for 15 previously orphaned receptors. This multiplexed receptor assay allows the cost-effective mapping of large chemical libraries to receptor repertoires at scale.

SUBMITTER: Jones EM

PROVIDER: S-EPMC6907015 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing.

Jones Eric M EM Jajoo Rishi R Cancilla Daniel D Lubock Nathan B NB Wang Jeffrey J Satyadi Megan M Chong Rockie R de March Claire C Bloom Joshua S JS Matsunami Hiroaki H Kosuri Sriram S

Cell systems 20190320 3

G protein-coupled receptors (GPCRs) are central to how mammalian cells sense and respond to chemicals. Mammalian olfactory receptors (ORs), the largest family of GPCRs, mediate the sense of smell through activation by small molecules, though for most bonafide ligands, they have not been identified. Here, we introduce a platform to screen large chemical panels against multiplexed GPCR libraries using next-generation sequencing of barcoded genetic reporters in stably engineered human cell lines. W ...[more]

PMID: 30904378

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Project description:Protein-ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: [Formula: see text] is the base implementation that employs distinct featurization to enhance domain-awareness, while [Formula: see text] is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein's 3D structure with 0.979 test accuracy for [Formula: see text] and 0.958 for [Formula: see text] for predicting activity of a protein-ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and [Formula: see text] crucial for compound's potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on [Formula: see text] with [Formula: see text] and [Formula: see text], respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of [Formula: see text] on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

Project description:BackgroundDespite biomarker development advances, early detection of aggressive prostate cancer (PCa) remains challenging. We previously developed a clinical-grade urine test (Michigan Prostate Score [MiPS]) for individualized aggressive PCa risk prediction. MiPS combines serum prostate-specific antigen (PSA), the TMPRSS2:ERG (T2:ERG) gene fusion, and PCA3 lncRNA in whole urine after digital rectal examination (DRE).ObjectiveTo improve on MiPS with a novel next-generation sequencing (NGS) multibiomarker urine assay for early detection of aggressive PCa.Design, setting, and participantsPreclinical development and validation of a post-DRE urine RNA NGS assay (Urine Prostate Seq [UPSeq]) assessing 84 PCa transcriptomic biomarkers, including T2:ERG, PCA3, additional PCa fusions/isoforms, mRNAs, lncRNAs, and expressed mutations. Our UPSeq model was trained on 73 patients and validated on a held-out set of 36 patients representing the spectrum of disease (benign to grade group [GG] 5 PCa).Outcome measurements and statistical analysisThe area under the receiver operating characteristic curve (AUC) of UPSeq was compared with PSA, MiPS, and other existing models/biomarkers for predicting GG ≥3 PCa.Results and limitationsUPSeq demonstrated high analytical accuracy and concordance with MiPS, and was able to detect expressed germline HOXB13 and somatic SPOP mutations. In an extreme design cohort (n = 109; benign/GG 1 vs GG ≥3 PCa, stratified to exclude GG 2 cancer in order to capture signal difference between extreme ends of disease), UPSeq showed differential expression for T2:ERG.T1E4 (1.2 vs 78.8 median normalized reads, p < 0.00001) and PCA3 (1024 vs 2521, p = 0.02), additional T2:ERG splice isoforms, and other candidate biomarkers. Using machine learning, we developed a 15-transcript model on the training set (n = 73) that outperformed serum PSA and sequencing-derived MiPS in predicting GG ≥3 PCa in the held-out validation set (n = 36; AUC 0.82 vs 0.69 and 0.69, respectively).ConclusionsThese results support the potential utility of our novel urine-based RNA NGS assay to supplement PSA for improved early detection of aggressive PCa.Patient summaryWe have developed a new urine-based test for the detection of aggressive prostate cancer, which promises improvement upon current biomarker tests.

Dataset Information

A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing.

Publications

A Scalable, Multiplexed Assay for Decoding GPCR-Ligand Interactions with RNA Sequencing.

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