Prediction of Calcite Morphology from Computational and Experimental Studies of Mutations of a De Novo-Designed Peptide

Abstract

Many organisms use macromolecules, often proteins or peptides, to control the growth of inorganic crystals into complex materials. The ability to model peptide-mineral interactions accurately could allow for the design of novel peptides to produce materials with desired properties. Here, we tested a computational algorithm developed to predict the structure of peptides on mineral surfaces. Using this algorithm, we analyzed energetic and structural differences between a 16-residue peptide (bap4) designed to interact with a calcite growth plane and single- and double-point mutations of the charged residues. Currently, no experimental method is available to resolve the structures of proteins on solid surfaces, which precludes benchmarking for computational models. Therefore, to test the models, we chemically synthesized each peptide and analyzed its effects on calcite crystal growth. Whereas bap4 affected the crystal growth by producing heavily stepped corners and edges, point mutants had variable influences on morphology. Calculated residue-specific binding energies correlated with experimental observations; point mutations of residues predicted to be crucial to surface interactions produced morphologies most similar to unmodified calcite. These results suggest that peptide conformation plays a role in mineral interactions and that the computational model supplies valid energetic and structural data that can provide information about expected crystal morphology.

Simulations of inorganic–bioorganic interfaces to discover new materials: insights, comparisons to experiment, challenges, and opportunities

“…interactions with organic molecules, 104 self-assembled monolayers, 238,291,292 as well as polyelectrolytes. 237 The pK values of carbonic acid are pK 1 = 6.35 and pK 2 = 10.33 125 and indicate that, under mineralization conditions of pH 8 to 10, the majority of carbonic species in solution are hydrogen carbonate ions (HCO 3…”

“…) and only small amounts of carbonate ions (CO 3 2À ). 293 However, similar to simulation studies of apatites, prior computations have almost exclusively focused on carbonate terminated surfaces (CO 3 2À ) 104,294,295 which are only likely to be present at pH values higher than 11. Suitable pH resolved atomistic surface models for calcite still need to be developed and validated to achieve mechanistic understanding and predictions of biological assembly in consistency with experiment.…”

Residue‐centric modeling and design of saccharide and glycoconjugate structures

“…Rosetta has successfully designed unique sequences to match a fixed peptide backbone; [29,30] novel protein folds, [31,32] including with functional sites; [33] enzyme active sites; [34,35] protein-protein interfaces; [36] RNA sequences; [37] and peptides to modify mineral growth. [38,39] Rosetta has also been expanded to model non-canonical and non-peptide polymers. [40] How Rosetta Differs from Other Approaches In contrast to quantum or molecular mechanics/dynamics approaches, Rosetta is "residue-centric" [20] instead of "atomcentric".…”

“…Rosetta has solved the structures of proteins and RNA; been used to refine NMR, crystal, and cryo‐electron microscopy structures; modeled antibody loops; and docked both protein–protein and protein–ligand complexes. Rosetta has successfully designed unique sequences to match a fixed peptide backbone; novel protein folds, including with functional sites; enzyme active sites; protein–protein interfaces; RNA sequences; and peptides to modify mineral growth . Rosetta has also been expanded to model non‐canonical and non‐peptide polymers …”

Using the RosettaSurface Algorithm to Predict Protein Structure at Mineral Surfaces

“…The constraint-based docking framework employed by RosettaSurface establishes a feedback loop between experimental constraint determination and computational refinement, making it a valuable tool for the study of biomineralization (Masica, Ash, et al, 2010). As the fundamental components of the algorithm improve, design of biomineralization proteins will also improve (Masica, Schrier, Specht, & Gray, 2010; Schrier, Sayeg, & Gray, 2011). …”