Determining atomic resolution structures of multiple functional states of protein would greatly aid the development of drugs to target these states and regulate biomolecular function. However, this goal has been substantially hindered by a lack of high-resolution structures, particularly in critical functional states or in the presence of drugs.
Though computer simulations could provide solutions, efficient sampling of the conformational space is difficult due to long timescale at which most protein functions.
There are two strong reasons that these simulations are now feasible:
1) recent development of novel simulation techniques and artificial intelligent algorithms;
2) improvements to high-performance computing.
This project will devise a hybrid approach combining multiple computational and experimental techniques to first obtain structures for functional states of neuronal receptors, that are critical mediators of electrochemical signal transduction in neurons and other excitable cells, using a combination of artificial intelligence and advanced molecular dynamics (MD) simulation tools. Then, the binding and functional regulation of natural modulators to the identified receptor states will be extensively characterized using MD simulations and experiments by collaborators. The knowledge development in this project potential to design future state-dependent pharmaceutics that mimics the chemical structure, binding and function of these modulators.