
Central among the tools and approaches used for ligand discovery and design are Molecular Dynamics (MD) simulations, which follow the dynamic changes in molecular structure in response to the environmental condition, interactions with other proteins, and the effects of ligand binding. The need for, and successes of, MD simulations in providing this type of essential information are well documented, but so are the challenges presented by the size of the resulting datasets encoding the desired information. The difficulty of extracting information on mechanistically important state-to-state transitions in response to ligand binding and other interactions is compounded by these being rare events in the MD trajectories of complex molecular machines, such as G-protein-coupled receptors (GPCRs). To address this problem, we have developed a protocol for the efficient detection of such events. We show that the novel Rare Event Detection (RED) protocol reveals functionally relevant and pharmacologically discriminating responses to the binding of different ligands to the 5-HT2AR orthosteric site in terms of clearly defined, structurally coherent, and temporally ordered conformational transitions. This information from the RED protocol offers new insights into specific ligand-determined functional mechanisms encoded in the MD trajectories, which opens a new and rigorously reproducible path to understanding drug activity with application in drug discovery.
function-related conformational transitions, Protein Conformation, non-negative factorization, Organic chemistry, ligand-induced GPCR structure and dynamics, molecular dynamics simulations, Molecular Dynamics Simulation, inverse agonist, Ligands, pharmacological efficacy, Article, drug discovery, Receptors, G-Protein-Coupled, Machine Learning, QD241-441, Humans, functional selectivity, serotonin 5-HT<sub>2A</sub>R receptor
function-related conformational transitions, Protein Conformation, non-negative factorization, Organic chemistry, ligand-induced GPCR structure and dynamics, molecular dynamics simulations, Molecular Dynamics Simulation, inverse agonist, Ligands, pharmacological efficacy, Article, drug discovery, Receptors, G-Protein-Coupled, Machine Learning, QD241-441, Humans, functional selectivity, serotonin 5-HT<sub>2A</sub>R receptor
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 18 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
