We have developed a knowledge-based potential that includes the degree of side chain exposure to lipids and correctly accounts for bilayer thickness. We also calculate depth-dependent energies for unsatisfied backbone hydrogen bond donors and acceptors, which are found to be relatively small, ~2 RT. The potential is incorporated into our Upside MD algorithm that can fold de novo some soluble proteins in CPU-hours. Our model has an authentic polypeptide backbone and Ramachandran maps along with a 6-position side chain bead. Because we include the energetics of backbone exposure, helices are allowed to unfold within the bilayer during the simulations. In 100 AFM unfolding simulations of bacteriorhodopsin, we reproduce the characteristic experimental features including the unfolding of individual and pairs of helices, and even the back-and-forth unfolding of single helical turns with comparable or more resolution than in experiment. We also emulate unfolding experiments with magnetic tweezers by pulling laterally on GlpG, a 6 helix bundle. We observe more unfolding intermediates and pathways than observed in experimental studies. The origin of these differences lies in how force is applied and the effective spring constant. For unfolding simulations of GlpG by magnetic tweezers, the force remains nearly constant after the initial "rip" due to the weak spatial dependence of the magnetic field, and only a few intermediates are observed. However, for unfolding simulations by AFM where the cantilever’s force immediately drops to near-zero after a rip, over a dozen intermediates are observed. Hence, the experimental mode strongly affects the amount of detail one observes and the apparent folding cooperativity. These factors should be considered when designing experiments and interpreting forced-unfolding data.

New membrane burial potential used in force-induced unfolding simulations of membrane proteins

2019 Spring National Meeting

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