@article{TEXTUAL,
      recid = {6368},
      author = {Jumper, John M. and Faruk, Nabil F. and Freed, Karl F. and  Sosnick, Tobin R.},
      title = {Trajectory-based training enables protein simulations with  accurate folding and Boltzmann ensembles in cpu-hours},
      journal = {PLOS Computational Biology},
      address = {2018-12-27},
      number = {TEXTUAL},
      abstract = {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.},
      url = {http://knowledge.uchicago.edu/record/6368},
}