Official Project Description
This project is an attempt at implementing adaptive sampling in Folding@home.
Adaptive sampling is a way of enhancing sampling of protein conformational space by selectively launching simulations from the most "valuable" work units. Identifying druggable states or exploring conformational state space relevant to disease is an existing challenge.
The embarassingly parallel nature of Folding@home allows us to massively scale up our exploration.
However, the underlying methods still rely on luck to a large extent – we must discover the states in work units as the dataset grows in size and more work units are run.
This can be an incredibly inefficient process, wasting work units on regions of state space that are irrelevant or uninteresting to the question at hand.
Adaptive Sampling is a way to tackle this inefficiency.
Using iterative rounds, where we collect the work units so far and select the "best/most valuable" conformational state worth exploring.
New simulations and work units are launched from these most valuable work units, hopefully more efficiently exploring state space.
This project is identical in calculation to 16497, exploring conformations of MET kinase, involved in non-small-cell lung carcinoma, but acting as a test bed.