Using the Rosetta surface algorithm to predict protein structure at mineral surfaces

Determination of protein structure on mineral surfaces is necessary to understand biomineralization processes toward better treatment of biomineralization diseases and design of novel protein-synthesized materials. To date, limited atomic-resolution data have hindered experimental structure determination for proteins on mineral surfaces. Molecular simulation represents a complementary approach. In this chapter, we review RosettaSurface, a computational structure prediction-based algorithm designed to broadly sample conformational space to identify low-energy structures. We summarize the computational approaches, the published applications, and the new releases of the code in the Rosetta 3 framework. In addition, we provide a protocol capture to demonstrate the practical steps to employ RosettaSurface. As an example, we provide input files and output data analysis for a previously unstudied mineralization protein, osteocalcin. Finally, we summarize ongoing challenges in energy function optimization and conformational searching and suggest that the fusion between experiment and calculation is the best route forward.