New methods using rigorous machine learning for coarse-grained protein folding and dynamics
Accurately describing the conformational ensembles of proteins in solution has been a long-standing challenge in protein biophysics. The first difficulty is the necessity of evaluating the large and competing sources of energy and entropy that determine the thermodynamics and conformation of the protein. The second is the intensive sampling required to obtain the Boltzmann ensemble of conformations. We address the sampling challenge by defining a method to obtain an approximate free energy of the side chains for a given backbone configuration. This allows us to obtain backbone dynamics in a much smoother energy surface by greatly reducing the steric rattling and side chain repacking that exist on a variety of timescales in atomic molecular dynamics. Using only three backbone atoms per residue, we have developed a coarse-grained model for Langevin dynamics simulations that is capable of rapidly folding some small proteins with near-angstrom accuracy in hours on a home computer.^
Thesis, Dissertation, English, 2017
ProQuest Dissertations & Theses, Ann Arbor, 2017