A Benchmarking Study of Peptide–Biomineral Interactions

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.