bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Янв. 19, 2025

Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate signaling pathways. Defects PTMs have been linked numerous developmental disorders human diseases, including cancer, diabetes, heart, neurodegenerative metabolic diseases. are important targets drug discovery, as they can significantly influence various aspects interactions binding affinity. The structural consequences PTMs, such phosphorylation-induced conformational changes or effects on ligand affinity, historically challenging study large scale, primarily due reliance experimental methods. Recent advancements computational power artificial intelligence, particularly deep learning algorithms protein structure prediction tools like AlphaFold3, opened new possibilities for exploring context between drugs. These AI-driven methods enable accurate modeling structures PTM-modified regions simulation ligand-binding dynamics scale. In this work, we identified small molecule binding-associated that across all listed DrugDomain database, which developed recently. 6,131 were mapped domains from Evolutionary Classification Protein Domains (ECOD) database. Scientific contribution. Using recent AI-based approaches (AlphaFold3, RoseTTAFold All-Atom, Chai-1), generated 14,178 models with docked ligands. Our results demonstrate these predict PTM binding, but precise evaluation accuracy requires much larger benchmarking set. We also found phosphorylation NADPH-Cytochrome P450 Reductase, observed cervical lung causes significant disruption pocket, potentially impairing function. All data available database v1.1 ( http://prodata.swmed.edu/DrugDomain/ ) GitHub https://github.com/kirmedvedev/DrugDomain ). This resource is first our knowledge offering

Язык: Английский