A Benchmarking Study of Peptide–Biomineral Interactions
datasetposted on 30.11.2017, 00:00 authored by Michael S. Pacella, Jeffrey J. Gray
A long-standing goal in the field of biomineralization has been to achieve a molecular-level mechanistic understanding of how proteins participate in the nucleation and growth of inorganic crystals (both in vitro and in vivo). Computational methods offer an approach to explore these interactions and propose mechanisms at the atomic scale; however, to have confidence in the predictions of a computational method, the method must first be validated against a benchmark experimental data set of protein–mineral interactions. Relatively little work has been done to test the ability of computation to reproduce experimental results on mineral systems with biologically relevant additives present. The goal of this work is to develop a standard and varied benchmark to test whether a computational method is able to match experimental results at the length and time scales of biomineral–peptide interactions. We compare the results of the RosettaSurface algorithm to an experimental benchmark of kinetic and thermodynamic measurements on peptide–biomineral interactions taken from atomic force microscopy. The RosettaSurface algorithm successfully identifies which mineral face and step edges will bind peptides the strongest; however, the algorithm struggles to predict the correct rank order of binding for multiple peptides to the same face or step edge.