Dr. Marimuthu Krishnan and his student Keshavan Seshadri published a paper on Molecular Dynamics and Machine Learning Study of Adrenaline Dynamics in the Binding Pocket of GPCR in Journal of Chemical Information and Modeling, in  Machine learning and Deep Learning  section. The Journal of Chemical Information and Modeling is a  high impact journal (IF: 6.12).

Research work as explained by the authors:

G-protein coupled receptors (GPCRs) are the most prominent family of membrane proteins that serve as major targets for one-third of the drugs produced. A detailed understanding of the molecular mechanism of drug-induced activation and inhibition of GPCRs is crucial for the rational design of novel therapeutics. The binding of the neurotransmitter adrenaline to the β2-adrenergic receptor (β2AR) is known to induce a flight or fight cellular response, but much remains to be understood about binding-induced dynamical changes in β2AR and adrenaline. In this article, we examine the potential of mean force (PMF) for the unbinding of adrenaline from the orthosteric binding site of β2AR and the associated dynamics using umbrella sampling and molecular dynamics (MD) simulations. The calculated PMF reveals a global energy minimum, which corresponds to the crystal structure of β2AR–adrenaline complex, and a meta-stable state in which the adrenaline is moved slightly deeper into the binding pocket with a different orientation compared to that in the crystal structure. The orientational and conformational changes in adrenaline during the transition between these two states and the underlying driving forces of this transition are also explored. Based on the clustering of MD configurations and machine learning-based statistical analyses of time series of relevant collective variables, the structures and stabilizing interactions of these two states of the β2AR–adrenaline complex are also investigated.

Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.3c00401

July 2023