This project is generating a test set for what we hope will increase the power of F@h by orders of magnitude, i.e. adaptive sampling. The idea is that instead of running many trajectories for a long time and seeing what happened at the end, we will run hundreds of trajectories for just 1 to a few gens - then pick frames from those that have advanced most on a given goal function (e.g. traveled furthest from their starting structure and closest to a target structure) and re-seed the next hundred of trajectories etc. - there’s a good amount of literature showing this can achieve very significant speedups. It will also let us make the same conclusions with much less data - speed the whole process up.
11715 is a short designed beta-hairpin peptide - trpzip2, which has unfolded, folded, and mis-registered states - i.e. it’s a good model of what we have with our cancer proteins where multiple conformational states have to be connected. We’re collecting ‘standard’ reference data here, and then will try our adaptive machinery for the first time to try to reproduce the ‘standard’ results.
List of Contributors
This project is managed by Rafal Wiewiora at Memorial Sloan Kettering Cancer Center.
Graduate Student at Chodera Lab, Memorial Sloan Kettering Cancer Center, New York.
Interested in studying conformational dynamics of proteins using Molecular Dynamics and experimental methods, to make rational drug design better, cheaper and faster. Working with histone methyltransferases - a family of epigenetic regulators implicated in many cancers, aging and drug addiction.
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