Computational methods to examine conformational changes and ligand-binding properties: Examples in neurobiology. (1st May 2019) - Dämgen, Marc A.; Biggin, Philip C.
Highlights: Molecular dynamics simulations are increasingly used in molecular neurobiology. Simulations can give unique information that complements experiment very well. We show examples of three methods; Markov state models, funnel metadynamics and steered MD. Abstract: Many proteins that are central to key aspects of neurobiology undergo conformational changes as part of their function, usually in response to a stimulus. Often, these proteins are embedded within a membrane, which creates particular experimental challenges to surmount. This has resulted in computational methods providing a valuable complementary tool for some time now, especially in the development of working models at atomic resolution. Indeed, molecular dynamics (MD) simulations are now routinely applied to new structures, either as part of the initial analysis or as part of an automated pipeline. Such simulations have proven extremely useful in terms of characterizing the inherent underlying conformational dynamics or providing insight into the interactions with the surrounding lipid molecules. However, MD simulations are capable of providing much more sophisticated information, including fundamental kinetic and thermodynamic properties of transitions between states and a description of how those transitions are influenced by the presence of ligands. There is a very large array of advanced simulation methods that can provide this information, but in this short review we limit ourselves to some selected examples of techniques that have given particular insight into proteins associated with molecular neurobiology. In this review, we highlight the use of i) Markov State Modelling to examine sodium dynamics in the dopamine transporter, ii) Metadynamics to explore neurotransmitter binding to a ligand-gated ion channel and iii) Steered MD to investigate conformational change in ionotropic glutamate receptors.