Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces

Abstract

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.

Nanostructure, osteopontin, and mechanical properties of calcitic avian eggshell

“…The influence of OPN on inducing nanostructure and modulating the dimensions of this nanostructure, and its incorporation into calcite, can be largely attributed to its strong binding to mineral occurring through the acidic peptide stretches in its primary amino acid sequence ( 38 ). We thus computationally modeled, using RosettaSurface ( 39 ), docking of the highly acidic polyaspartate sequence found in chicken OPN ( 99 DDDDDDDND 107 ) to acute and obtuse growth steps of calcite ( Fig. 5F ).…”

A computational method for selecting short peptide sequences for inorganic material binding

“…The score12 module has been used as a starting point for a protein–inorganic surface interaction scoring function . A distance‐dependent dielectric Coulomb interaction term has been added for considering protein side‐chain interactions with charged surface ions .…”

“…A distance‐dependent dielectric Coulomb interaction term has been added for considering protein side‐chain interactions with charged surface ions . The final scoring function in RosettaSurface represents the total interaction energy: where “ vdW ” stands for van der Waals, “ elec ” for electrostatic, “ Hbond ” for hydrogen bond and “ solv ” for solvation. In each case W represents a force field parameter and E an energy value.…”

Prediction and clarification of structures of (bio)molecules on surfaces

“…Finally, we mention that terms in physical and statistical energy functions can be combined, as is done in ROSETTA's all-atom score function used for protein structure prediction, which has been extended to consider biomolecule-surface interactions [118].…”