Dataset Information

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.

ABSTRACT: Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for in silico screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an attractive target against Parkinson's disease and cancer. The identified antagonists were tested here in a radio ligand binding assay. Among those, we found a promising ligand whose chemotype differs significantly from all so-far reported antagonists, with a binding affinity of 310 ± 23.4 nM. Thus, our protocol emerges as a powerful approach to identify promising ligand candidates with novel chemotypes while preserving antagonistic potential and affinity in the nanomolar range.

SUBMITTER: Goßen J

PROVIDER: S-EPMC10430665 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.

Goßen Jonas J Ribeiro Rui Pedro RP Bier Dirk D Neumaier Bernd B Carloni Paolo P Giorgetti Alejandro A Rossetti Giulia G

Chemical science 20230724 32

Identifying ligands targeting G protein coupled receptors (GPCRs) with novel chemotypes other than the physiological ligands is a challenge for <i>in silico</i> screening campaigns. Here we present an approach that identifies novel chemotype ligands by combining structural data with a random forest agonist/antagonist classifier and a signal-transduction kinetic model. As a test case, we apply this approach to identify novel antagonists of the human adenosine transmembrane receptor type 2A, an at ...[more]

PMID: 37592985

Similar Datasets

Project description:Tumor-related activities that seem to be operationally induced by the division of function, such as inflammation, neoangiogenesis, Warburg effect, immune response, extracellular matrix remodeling, cell proliferation rate, apoptosis, coagulation effects, present itself from a systems perspective as an enhancement of complexity. We hypothesized, that tumor systems-directed therapies might have the capability to use aggregated action effects, as adjustable sizes to therapeutically modulate the tumor systems' stability, homeostasis, and robustness. We performed a retrospective analysis of recently published data on 224 patients with advanced and heavily pre-treated (10% to 63%) vascular sarcoma, melanoma, renal clear cell, cholangiocellular, carcinoma, hormone-refractory prostate cancer, and multivisceral Langerhans' cell histiocytosis enrolled in nine multi-center phase II trials (11 centers). Each patient received a multi-targeted systems-directed therapy that consisted of metronomic low-dose chemotherapy, a COX-2 inhibitor, combined with one or two transcription modulators, pioglitazone +/- dexamethasone or IFN-alpha. These treatment schedules may attenuate the metastatic potential, tumor-associated inflammation, may exert site-specific activities, and induce long-term disease stabilization followed by prolonged objective response (3% to 48%) despite poor monoactivity of the respective drugs. Progression-free survival data are comparable with those of reductionist-designed standard first-line therapies. The differential response patterns indicate the therapies' systems biological activity. Understanding systems biology as adjustable size may break through the barrier of complex tumor-stroma-interactions in a therapeutically relevant way: Comparatively high efficacy at moderate toxicity. Structured systems-directed therapies in metastatic cancer may get a source for detecting the topology of tumor-associated complex aggregated action effects as adjustable sizes available for targeted biomodulatory therapies.

Dataset Information

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.

Publications

AI-based identification of therapeutic agents targeting GPCRs: introducing ligand type classifiers and systems biology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets