10.5061/DRYAD.H9F8SB7
Jumper, John M.
University of Chicago
Faruk, Nabil F.
University of Chicago
Freed, Karl F.
University of Chicago
Sosnick, Tobin R.
University of Chicago
Data from: Trajectory-based training enables protein simulations with
accurate folding and Boltzmann ensembles in cpu-hours
Dryad
dataset
2018
Molecular dynamics
Protein folding
National Science Foundation
https://ror.org/021nxhr62
MCB-1517221
2018-12-28T14:11:52Z
2018-12-28T14:11:52Z
en
https://doi.org/10.1371/journal.pcbi.1006578
2828823219 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
An ongoing challenge in protein chemistry is to identify the underlying
interaction energies that capture protein dynamics. The traditional
trade-off in biomolecular simulation between accuracy and computational
efficiency is predicated on the assumption that detailed force fields are
typically well-parameterized, obtaining a significant fraction of possible
accuracy. We re-examine this trade-off in the more realistic regime in
which parameterization is a greater source of error than the level of
detail in the force field. To address parameterization of coarse-grained
force fields, we use the contrastive divergence technique from machine
learning to train from simulations of 450 proteins. In our procedure, the
computational efficiency of the model enables high accuracy through the
precise tuning of the Boltzmann ensemble. This method is applied to our
recently developed Upside model, where the free energy for side chains is
rapidly calculated at every time-step, allowing for a smooth energy
landscape without steric rattling of the side chains. After this
contrastive divergence training, the model is able to de novo fold
proteins up to 100 residues on a single core in days. This improved Upside
model provides a starting point both for investigation of folding dynamics
and as an inexpensive Bayesian prior for protein physics that can be
integrated with additional experimental or bioinformatic data.
TrajectoriesContains folders for each of the proteins tested with
trajectories from both native and denovo initial structures. The
trajectories are of the lowest temperature replicas, which were the ones
used in the bulk of the analysis. Consult the README for additional
details.trajectories.tar.gz