Investigating plasticity within the interleukin-6 family with AlphaFold-Multimer

Computational and Structural Biotechnology Journal, Год журнала: 2025, Номер 27, С. 946 - 959

Опубликована: Янв. 1, 2025

Cytokines are important soluble mediators that involved in physiological and pathophysiological processes. Among them, members of the interleukin-6 (IL-6) family cytokines have gained remarkable attention, because especially name-giving cytokine IL-6 has been shown to be an excellent target treat inflammatory autoimmune diseases. The consists nine members, which activate their cells via combinations non-signaling α- and/or signal-transducing β-receptors. While some receptor exclusively used by a single cytokine, other multiple cytokines. Research recent years unraveled another level complexity: several cannot only signal canonical receptors, but can bind additional β-receptors, albeit with less affinity. examples such plasticity reported, systematic analysis this phenomenon is lacking. development artificial intelligence programs like AlphaFold allows computational protein complexes manner. Here, we develop pipeline for cytokine:cytokine interaction show AlphaFold-Multimer correctly predicts ligands family. However, does not provide sufficient insight conclusively predict alternative, low-affinity receptors within

Язык: Английский

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Virtual library docking for cannabinoid-1 receptor agonists with reduced side effects

Nature Communications, Год журнала: 2025, Номер 16(1)

Опубликована: Март 6, 2025

Virtual library docking can reveal unexpected chemotypes that complement the structures of biological targets. Seeking agonists for cannabinoid-1 receptor (CB1R), we dock 74 million tangible molecules and prioritize 46 high ranking ones de novo synthesis testing. Nine are active by radioligand competition, a 20% hit-rate. Structure-based optimization one most potent these (Ki = 0.7 µM) leads to '1350, 0.95 nM ligand full CB1R agonist Gi/o signaling. A cryo-EM structure '1350 in complex with CB1R-Gi1 confirms its predicted docked pose. The lead is strongly analgesic male mice, 2-20-fold therapeutic window over hypolocomotion, sedation, catalepsy no observable conditioned place preference. These findings suggest unique cannabinoid may disentangle characteristic side-effects from analgesia, supporting further development cannabinoids as pain therapeutics.

Язык: Английский

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UNC9426, a Potent and Orally Bioavailable TYRO3-Specific Inhibitor

Journal of Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Март 11, 2025

TYRO3 plays a critical role in platelet aggregation as response amplifier. Selective inhibition of may provide therapeutic benefits for treating thrombosis and related diseases without increasing bleeding risk. We employed structure-based approach discovered novel potent inhibitor UNC9426 (12) with an excellent Ambit selectivity score (S50 (1.0 μM) = 0.026) favorable pharmacokinetic properties mice. Treatment reduced time blocked TYRO3-dependent functions tumor cells macrophages, implicating its utility multiple indications.

Язык: Английский

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Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery

Biomolecules, Год журнала: 2025, Номер 15(3), С. 423 - 423

Опубликована: Март 17, 2025

Understanding protein structures can facilitate the development of therapeutic drugs. Traditionally, have been determined through experimental approaches such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy. While these methods are effective considered gold standard, they very resource-intensive time-consuming, ultimately limiting their scalability. However, with recent developments in computational biology artificial intelligence (AI), field prediction has revolutionized. Innovations like AlphaFold RoseTTAFold enable structure predictions to be made directly from amino acid sequences remarkable speed accuracy. Despite enormous enthusiasm associated newly developed AI-approaches, true potential structure-based drug discovery remains uncertain. In fact, although algorithms generally predict overall well, essential details for ligand docking, exact location side chains within binding pocket, not predicted necessary Additionally, docking methodologies more a hypothesis generator rather than precise predictor ligand–target interactions, thus, usually identify many false-positive hits among only few correctly interactions. this paper, we reviewing latest cutting-edge emphasis on GPCR target class assess role AI discovery.

Язык: Английский

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Can Deep Learning Blind Docking Methods be Used to Predict Allosteric Compounds?

Journal of Chemical Information and Modeling, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Allosteric compounds offer an alternative mode of inhibition to orthosteric with opportunities for selectivity and noncompetition. Structure-based drug design (SBDD) allosteric introduces complications compared their counterparts; multiple binding sites interest are considered, often is only observed in particular protein conformations. Blind docking methods show potential virtual screening ligands, deep learning methods, such as DiffDock, achieve state-of-the-art performance on protein-ligand complex prediction benchmarks traditional Vina Lin_F9. To this aim, we explore the utility a data-driven platform called minimum distance matrix representation (MDMR) retrospectively predict recently discovered inhibitors complexed Cyclin-Dependent Kinase (CDK) 2. In contrast other representations, it uses residue-residue (or residue-ligand) feature that prioritizes formation interactions. Analysis highlights variety conformations ligand modes, identify intermediate conformation heuristic-based kinase classification do not distinguish. Next, self- cross-docking assess whether can both modes if prospective success conditional selection receptor conformation, respectively. We find combined method, DiffDock followed by Lin_F9 Local Re-Docking (DiffDock + LRD), must be selected pose. summary, work value method outlines challenges SBDD compounds.

Язык: Английский

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