PROJECT #17604 RESEARCH FOR CANCER
FOLDING PERFORMANCE PROFILE

PROJECT SUMMARY

This project is an attempt at implementing umbrella sampling in Folding@home.

Umbrella sampling is a way of "pulling" the protein to new configurations by attaching a spring to specific atoms to move into a certain configuration. 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 and slow process.

To help speed up state discovery and exploration, we can place 'springs' at regularly spaced intervals in our configuration space, and pull any independent simulation to one of these springs.

This "spring pulled simulation" is called Umbrella sampling (because the shape of the space explored around the spring looks like an parabola/umbrella).

With FAH, we can run multiple umbrellas at once, pulling each individual RUN to a unique point in conformational space independently of other RUNs.

In doing so we are able to massively scale up our sampling and discovery of unique states in a protein's conformational landscape. 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..

PROJECT INFO

Manager(s): Sukrit Singh

Institution: Memorial Sloan-Kettering Cancer-Center

Project URL: http://sukritsingh.github.io/

PROJECT WORK UNIT SUMMARY

Atoms: 59,897

Core: OPENMM_22

Status: Public