Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces
By M. Pacella, Da Chen Emily Koo, Robin Augustine Thottungal, Jeffrey J. Gray. 2013
21 citations recorded by the source corpus.
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.
Subfields: Biomaterials, Materials Chemistry, Molecular Biology
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Citations in the corpus
- Simulations of interfacial processes: recent advances in force field development (2019, methodology): “RosettaSurface uses a combination of Monte Carlo simulations and energy minimization techniques to determine low energy adsorbed structures of peptides on a surface using implicit solvent [27].”