Browse
Submit Data
Databases
API
Help

Dataset Information

35 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

PRECOGx: exploring GPCR signaling mechanisms with deep protein representations.

ABSTRACT: In this study we show that protein language models can encode structural and functional information of GPCR sequences that can be used to predict their signaling and functional repertoire. We used the ESM1b protein embeddings as features and the binding information known from publicly available studies to develop PRECOGx, a machine learning predictor to explore GPCR interactions with G protein and β-arrestin, which we made available through a new webserver (https://precogx.bioinfolab.sns.it/). PRECOGx outperformed its predecessor (e.g. PRECOG) in predicting GPCR-transducer couplings, being also able to consider all GPCR classes. The webserver also provides new functionalities, such as the projection of input sequences on a low-dimensional space describing essential features of the human GPCRome, which is used as a reference to track GPCR variants. Additionally, it allows inspection of the sequence and structural determinants responsible for coupling via the analysis of the most important attention maps used by the models as well as through predicted intramolecular contacts. We demonstrate applications of PRECOGx by predicting the impact of disease variants (ClinVar) and alternative splice forms from healthy tissues (GTEX) of human GPCRs, revealing the power to dissect system biasing mechanisms in both health and disease.

SUBMITTER: Matic M

PROVIDER: S-EPMC9252787 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Molecular Mechanisms of GPCR Signaling: A Structural Perspective.

Project description:G protein-coupled receptors (GPCRs) are cell surface receptors that respond to a wide variety of stimuli, from light, odorants, hormones, and neurotransmitters to proteins and extracellular calcium. GPCRs represent the largest family of signaling proteins targeted by many clinically used drugs. Recent studies shed light on the conformational changes that accompany GPCR activation and the structural state of the receptor necessary for the interactions with the three classes of proteins that preferentially bind active GPCRs, G proteins, G protein-coupled receptor kinases (GRKs), and arrestins. Importantly, structural and biophysical studies also revealed activation-related conformational changes in these three types of signal transducers. Here, we summarize what is already known and point out questions that still need to be answered. Clear understanding of the structural basis of signaling by GPCRs and their interaction partners would pave the way to designing signaling-biased proteins with scientific and therapeutic potential.

| S-EPMC5751122 | biostudies-literature

Anionic Phospholipids Control Mechanisms of GPCR-G Protein Recognition.

Project description:G protein-coupled receptors (GPCRs) are embedded in phospholipids that strongly influence drug-stimulated signaling. Anionic lipids are particularly important for GPCR signaling complex formation, but a mechanism for this role is not understood. Using NMR spectroscopy, we visualized the impact of anionic lipids on the function-related conformational equilibria of the human A 2A adenosine receptor (A 2A AR) in bilayers containing defined mixtures of zwitterionic and anionic phospholipids. Anionic lipids primed the receptor to form complexes with G proteins through a conformational selection process. Without anionic lipids, signaling complex formation proceeded through a less favorable induced fit mechanism. In computational models, anionic lipids mimicked interactions between a G protein and positively charged residues in A 2A AR at the receptor intracellular surface, stabilizing a pre-activated receptor conformation. Replacing these residues strikingly altered the receptor response to anionic lipids in experiments. High sequence conservation of the same residues among all GPCRs supports a general role for lipid-receptor charge complementarity in signaling.

| S-EPMC9882065 | biostudies-literature

Anionic phospholipids control mechanisms of GPCR-G protein recognition.

Project description:G protein-coupled receptors (GPCRs) are embedded in phospholipids that strongly influence drug-stimulated signaling. Anionic lipids are particularly important for GPCR signaling complex formation, but a mechanism for this role is not understood. Using NMR spectroscopy, we explore the impact of anionic lipids on the function-related conformational equilibria of the human A2A adenosine receptor (A2AAR) in bilayers containing defined mixtures of zwitterionic and anionic phospholipids. Anionic lipids prime the receptor to form complexes with G proteins through a conformational selection process. Without anionic lipids, signaling complex formation proceeds through a less favorable induced fit mechanism. In computational models, anionic lipids mimic interactions between a G protein and positively charged residues in A2AAR at the receptor intracellular surface, stabilizing a pre-activated receptor conformation. Replacing these residues strikingly alters the receptor response to anionic lipids in experiments. High sequence conservation of the same residues among all GPCRs supports a general role for lipid-receptor charge complementarity in signaling.

| S-EPMC9925817 | biostudies-literature

Molecular Deconvolution Platform to Establish Disease Mechanisms by Surveying GPCR Signaling.

Project description:Despite the wealth of genetic information available, mechanisms underlying pathological effects of disease-associated mutations in components of G protein-coupled receptor (GPCR) signaling cascades remain elusive. In this study, we developed a scalable approach for the functional analysis of clinical variants in GPCR pathways along with a complete analytical framework. We applied the strategy to evaluate an extensive set of dystonia-causing mutations in G protein Gαolf. Our quantitative analysis revealed diverse mechanisms by which pathogenic variants disrupt GPCR signaling, leading to a mechanism-based classification of dystonia. In light of significant clinical heterogeneity, the mechanistic analysis of individual disease-associated variants permits tailoring personalized intervention strategies, which makes it superior to the current phenotype-based approach. We propose that the platform developed in this study can be universally applied to evaluate disease mechanisms for conditions associated with genetic variation in all components of GPCR signaling.

| S-EPMC6077248 | biostudies-literature

GPCR: G protein complexes--the fundamental signaling assembly.

Project description:G protein coupled receptors (GPCR) constitute the largest group of cell surface receptors that transmit various signals across biological membranes through the binding and activation of heterotrimeric G proteins, which amplify the signal and activate downstream effectors leading to the biological responses. Thus, the first critical step in this signaling cascade is the interaction between receptor and its cognate G protein. Understanding this critical event at the molecular level is of high importance because abnormal function of GPCRs is associated with many diseases. Thus, these receptors are targets for drug development.

| S-EPMC3845202 | biostudies-literature

Informatic deconvolution of biased GPCR signaling mechanisms from in vivo pharmacological experimentation.

Project description:Ligands possessing different physico-chemical structures productively interact with G protein-coupled receptors generating distinct downstream signaling events due to their abilities to activate/select idiosyncratic receptor entities ('receptorsomes') from the full spectrum of potential receptor partners. We have employed multiple novel informatic approaches to identify and characterize the in vivo transcriptomic signature of an arrestin-signaling biased ligand, [D-Trp(12),Tyr(34)]-bPTH(7-34), acting at the parathyroid hormone type 1 receptor (PTH1R), across six different murine tissues after chronic drug exposure. We are able to demonstrate that [D-Trp(12),Tyr(34)]-bPTH(7-34) elicits a distinctive arrestin-signaling focused transcriptomic response that is more coherently regulated, in an arrestin signaling-dependent manner, across more tissues than that of the pluripotent endogenous PTH1R ligand, hPTH(1-34). This arrestin-focused response signature is strongly linked with the transcriptional regulation of cell growth and development. Our informatic deconvolution of a conserved arrestin-dependent transcriptomic signature from wild type mice demonstrates a conceptual framework within which the in vivo outcomes of biased receptor signaling may be further investigated or predicted.

| S-EPMC4646739 | biostudies-literature

Molecular Mechanisms of PTH/PTHrP Class B GPCR Signaling and Pharmacological Implications.

Project description:The classical paradigm of G protein-coupled receptor (GPCR) signaling via G proteins is grounded in a view that downstream responses are relatively transient and confined to the cell surface, but this notion has been revised in recent years following the identification of several receptors that engage in sustained signaling responses from subcellular compartments following internalization of the ligand-receptor complex. This phenomenon was initially discovered for the parathyroid hormone (PTH) type 1 receptor (PTH1R), a vital GPCR for maintaining normal calcium and phosphate levels in the body with the paradoxical ability to build or break down bone in response to PTH binding. The diverse biological processes regulated by this receptor are thought to depend on its capacity to mediate diverse modes of cyclic adenosine monophosphate (cAMP) signaling. These include transient signaling at the plasma membrane and sustained signaling from internalized PTH1R within early endosomes mediated by PTH. Here we discuss recent structural, cell signaling, and in vivo studies that unveil potential pharmacological outputs of the spatial versus temporal dimension of PTH1R signaling via cAMP. Notably, the combination of molecular dynamics simulations and elastic network model-based methods revealed how precise modulation of PTH signaling responses is achieved through structure-encoded allosteric coupling within the receptor and between the peptide hormone binding site and the G protein coupling interface. The implications of recent findings are now being explored for addressing key questions on how location bias in receptor signaling contributes to pharmacological functions, and how to drug a difficult target such as the PTH1R toward discovering nonpeptidic small molecule candidates for the treatment of metabolic bone and mineral diseases.

| S-EPMC10461325 | biostudies-literature

Deep geometric representations for modeling effects of mutations on protein-protein binding affinity.

Project description:Modeling the impact of amino acid mutations on protein-protein interaction plays a crucial role in protein engineering and drug design. In this study, we develop GeoPPI, a novel structure-based deep-learning framework to predict the change of binding affinity upon mutations. Based on the three-dimensional structure of a protein, GeoPPI first learns a geometric representation that encodes topology features of the protein structure via a self-supervised learning scheme. These representations are then used as features for training gradient-boosting trees to predict the changes of protein-protein binding affinity upon mutations. We find that GeoPPI is able to learn meaningful features that characterize interactions between atoms in protein structures. In addition, through extensive experiments, we show that GeoPPI achieves new state-of-the-art performance in predicting the binding affinity changes upon both single- and multi-point mutations on six benchmark datasets. Moreover, we show that GeoPPI can accurately estimate the difference of binding affinities between a few recently identified SARS-CoV-2 antibodies and the receptor-binding domain (RBD) of the S protein. These results demonstrate the potential of GeoPPI as a powerful and useful computational tool in protein design and engineering. Our code and datasets are available at: https://github.com/Liuxg16/GeoPPI.

| S-EPMC8366979 | biostudies-literature

Contextualizing protein representations using deep learning on protein networks and single-cell data.

Project description:Understanding protein function and discovering molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across diverse biological contexts, such as tissues and cell types, remains a significant challenge for existing algorithms. We introduce Pinnacle, a flexible geometric deep learning approach that is trained on contextualized protein interaction networks to generate context-aware protein representations. Leveraging a human multi-organ single-cell transcriptomic atlas, Pinnacle provides 394,760 protein representations split across 156 cell type contexts from 24 tissues and organs. Pinnacle's contextualized representations of proteins reflect cellular and tissue organization and Pinnacle's tissue representations enable zero-shot retrieval of the tissue hierarchy. Pretrained Pinnacle protein representations can be adapted for downstream tasks: to enhance 3D structure-based protein representations (PD-1/PD-L1 and B7-1/CTLA-4) at cellular resolution and to study the genomic effects of drugs across cellular contexts. Pinnacle outperforms state-of-the-art, yet context-free, models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases, and can pinpoint cell type contexts that are more predictive of therapeutic targets than context-free models (29 out of 156 cell types in rheumatoid arthritis; 13 out of 152 cell types in inflammatory bowel diseases). Pinnacle is a network-based contextual AI model that dynamically adjusts its outputs based on biological contexts in which it operates.

| S-EPMC10370131 | biostudies-literature

Exploring knotting mechanisms in protein folding.

Project description:One of the most striking topological features to be found in a protein is that of a distinct knot formed by the path of the polypeptide backbone. Such knotted structures represent some of the smallest "self-tying" knots observed in Nature. Proteins containing a knot deep within their structure add an extra complication to the already challenging protein-folding problem; it is not obvious how, during the process of folding, a substantial length of polypeptide chain manages to spontaneously thread itself through a loop. Here, we probe the folding mechanism of YibK, a homodimeric alpha/beta-knot protein containing a deep trefoil knot at its carboxy terminus. By analyzing the effect of mutations made in the knotted region of the protein we show that the native structure in this area remains undeveloped until very late in the folding reaction. Single-site destabilizing mutations made in the knot structure significantly affect only the folding kinetics of a late-forming intermediate and the slow dimerization step. Furthermore, we find evidence to suggest that the heterogeneity observed in the denatured state is not caused by isomerization of the single cis proline bond as previously thought, but instead could be a result of the knotting mechanism. These results allow us to propose a folding model for YibK where the threading of the polypeptide chain and the formation of native structure in the knotted region of the protein occur independently as successive events.

| S-EPMC2596204 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data