AIMC Topic: Receptors, G-Protein-Coupled

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Prediction of GPCR-Ligand Binding Using Machine Learning Algorithms.

Computational and mathematical methods in medicine
We propose a novel method that predicts binding of G-protein coupled receptors (GPCRs) and ligands. The proposed method uses hub and cycle structures of ligands and amino acid motif sequences of GPCRs, rather than the 3D structure of a receptor or si...

Classification of G-protein coupled receptors based on a rich generation of convolutional neural network, N-gram transformation and multiple sequence alignments.

Amino acids
Sequence classification is crucial in predicting the function of newly discovered sequences. In recent years, the prediction of the incremental large-scale and diversity of sequences has heavily relied on the involvement of machine-learning algorithm...

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PloS one
Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such ...

GGIP: Structure and sequence-based GPCR-GPCR interaction pair predictor.

Proteins
G Protein-Coupled Receptors (GPCRs) are important pharmaceutical targets. More than 30% of currently marketed pharmaceutical medicines target GPCRs. Numerous studies have reported that GPCRs function not only as monomers but also as homo- or hetero-d...

Deciphering the Complexity of Ligand-Protein Recognition Pathways Using Supervised Molecular Dynamics (SuMD) Simulations.

Journal of chemical information and modeling
Molecular recognition is a crucial issue when aiming to interpret the mechanism of known active substances as well as to develop novel active candidates. Unfortunately, simulating the binding process is still a challenging task because it requires cl...

Virtual screening of umami peptides during sufu ripening based on machine learning and molecular docking to umami receptor T1R1/T1R3.

Food chemistry
Umami peptides might significantly contribute to the taste of sufu. However, the inefficiencies of traditional identification methods had great limitations. This study explored a new approach for umami peptides characterization in sufu. Combining pep...

Spatiotemporal calcium signaling patterns underlying opposing effects of histamine and TAS2R agonists in airway smooth muscle.

American journal of physiology. Lung cellular and molecular physiology
Intracellular calcium (Ca) release via phospholipase C (PLC) following G-protein-coupled receptor (GPCR) activation is typically linked to membrane depolarization and airway smooth muscle (ASM) contraction. However, recent findings show that bitter t...

EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network.

BMC biology
BACKGROUND: Identifying GPCR-compound interactions (GCI) plays a significant role in drug discovery and chemogenomics. Machine learning, particularly deep learning, has become increasingly influential in this domain. Large molecular models, due to th...

Deep learning in GPCR drug discovery: benchmarking the path to accurate peptide binding.

Briefings in bioinformatics
Deep learning (DL) methods have drastically advanced structure-based drug discovery by directly predicting protein structures from sequences. Recently, these methods have become increasingly accurate in predicting complexes formed by multiple protein...