A Parametric Rosetta Energy Function Analysis with LK Peptides on SAM Surfaces
Johns Hopkins University
While structures have been determined for many soluble proteins and an increasing number of membrane proteins, experimental structure determination methods are limited for complexes of proteins and solid surfaces. An economical alternative or complement to experimental structure determination is molecular simulation. Rosetta is a molecular simulation software suite that can model protein–surface interactions. While Rosetta has been thoroughly benchmarked on soluble protein modeling tasks, its ability to predict protein–surface interactions requires further work. In particular, the validity of the energy function is uncertain because it is a combination of independent parameters from energy functions developed separately for solution proteins and mineral surfaces. Therefore, I have assessed the performance of the RosettaSurface algorithm and tested the accuracy of its energy function by modeling the adsorption of leucine/lysine repeat peptides on methyl- and carboxy-terminated self-assembled monolayers. I investigated how RosettaSurface predictions for this system compared with experimental results, which showed that on both surfaces, LK-α peptides folded into helices and LK-β peptides held extended structures. Utilizing this model system, I performed a parametric analysis of Rosetta’s Talaris energy function and determined that the default energy function was less able to predict the extended LK-β structures, and that adjusting solvation parameters offered improved predictive accuracy. Simultaneously increasing lysine carbon hydrophilicity and methyl head group hydrophobicity yielded computational predictions most closely matching experimental results. The findings will improve RosettaSurface and other algorithms utilizing the Rosetta energy function.
Rosetta, RosettaSurface, Computational Biology, Biomineralization, LK Peptides