From Stefan Doerr
Proteins are essential in the regulation of most of the processes in a cell. They do so by interacting with other molecules, including other proteins.
Protein-protein interactions have been the subject of study for a long time using both experimental and computational methods, however they have eluded the atomic-level analysis and accuracy that unbiased molecular dynamics simulations can provide.
While protein-ligand interactions have been simulated already in many studies including our own (http://www.pnas.org/content/108/25/10184.full, http://pubs.acs.org/doi/full/10.1021/ci4006063), these interactions are relatively simple compared to two proteins interacting. Due to the size of proteins and their potential interaction sites, protein-protein interactions happen on timescales that used to be out of our computational reach until now.
In this study, Frank Noe’s team in collaboration with our group has been able to produce simulations that investigate both the binding and unbinding processes of two proteins called Barnase/Barstar. This was only possible through the use of GPU based ACEMD simulations (https://www.acellera.com/products/molecular-dynamics-software-gpu-acemd/) and adaptive sampling methods such as those implemented in the HTMD software (https://www.acellera.com/products/high-throughput-molecular-dynamics/) and the analysis of powerful Markov model methods.
Thus we were able to obtain an atomic level description of interaction states, equilibrium populations and kinetics of the binding process of the two proteins. Additionally, investigations on the effect of mutations on this process were reported. All results were validated with reference to experimental studies previously performed.
This study is of great interest as it demonstrates the possibility of protein-protein investigation using MD and opens the gates to the investigation of other very important protein-protein interaction processes.
Plattner N. et al. Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nat. Chem. (2017).