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WIREs Comput Mol Sci
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Organic–inorganic interface simulation for new material discoveries

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Organic–inorganic interactions are of high importance in several biological processes and in modern nanobiotechnological applications. Despite its significance in interface sciences, the basic mechanism of biomolecules’ specific binding to a surface is still not well understood. Current experimental methods have not reached the level either to follow the dynamics of interactions at the picosecond scale or to observe the surface morphology at the nanoscale level. The increasing interest in bio‐interfaces particularly for engineering applications demands proteins or peptides to be designed to recognize the inorganic surface with high specificity. Molecular simulation has been well adopted in the past couple of decades to decipher the protein–surface interactions at different levels of time and length scales. Several molecular simulation methods such as quantum mechanics, atomistic, and coarse grain simulations were employed in this domain of research, but the continuous improvements in interfacial force field (FF) development, availability of experimental data and new sampling methods make the atomistic simulation more attractive due to the offered accurate representation of protein adsorption behavior at the atomic level. However, the exactitude of such simulations entirely depends on the applied FF parameters, conformational sampling, and the solvation effects. In this overview, we briefly summarize the applicability of different simulation methods and of interface FFs. We also present the recent advances in the simulation of protein–surface interactions, and the challenges posed by the current simulation methods to reproduce the exact phenomenon. Future directions in this research field are also discussed. WIREs Comput Mol Sci 2017, 7:e1277. doi: 10.1002/wcms.1277 This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods
Three‐stage generic model of protein adsorption to the solid surface. (1) Peptide or protein moves from the bulk solution toward the interface by diffusion; (2) peptide anchored to the surface via hydrophilic group to the second water layer; and finally (3) complete adsorption of the peptide to the solid surface occurs through a stepwise rearrangement process.
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Fluorescent images of engineered peptides based on simulation results. The fluorescence of peptides PH1 and PH2 shows that the peptide affinity can be tuned from the knowledge obtained via molecular simulations. The scale bar is 100 µm. (Reprinted with permission from Ref . Copyright 2015 American Chemical Society)
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Simulations of proteins on different surfaces (a) ubiquitin protein docked on a neutral Au(1 1 1) surface in water (Reprinted with permission from Ref . Copyright 2012 American Chemical Society), (b) fibronectin upon adsorption on poly(ethylene glycol) based polyurethanes (PEG‐HDI and CO‐HDI) polymer surfaces (Reprinted with permission from Ref , Copyright 2012 American Chemical Society), (c) lysozyme on amorphous silica (Reprinted with permission from Ref . Copyright 2015 American Chemical Society), (c) bovine serum albumin on a hydrophobic graphite surface. (Reprinted with permission from Ref . Copyright 2011 American Chemical Society)
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Flow chart for parameterizing force fields.
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Computational techniques for a variety of length and time scales.
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Controlled design of peptides with desired facet specificity. (a) A nonfacet specific‐peptide was turned into Pt(1 1 1) facet‐specific peptide by mutating G with F amino acid. (b) A Pt(1 0 0) was turned into a Pt(1 1 1) specific peptide by changing L to F amino acids. Phenyl alanine (F) adheres to Pt(1 1 1) facet strongly. Nanocrystals were grown based on the peptide design (left side) and the corresponding Transmission Electron Microscope (TEM) images shown on the right side. (Reprinted with permission from Ref . Copyright 2013 American Chemical Society)
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Activity of silicon binding peptide, P2, and its mutants were calculated by MD simulation. Each amino acid position of the wild type (Wt) peptide P2 was replaced with methionine (M) to screen the affinity by MD simulation in explicit solvent (same conditions). The values are given as the percentage of the binding energy of the mutant with respect to that of the Wt peptide. Symbols (stars) are for illustration purposes to relate the sequence and its residual activity. (Reprinted with permission from Ref . Copyright 2014 American Chemical Society)
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MD snapshots (side views) of peptides P1–P3 on the n+‐Si surface in explicit solvent with buffering ions (137 mM Phosphate Buffer Saline (PBS), pH 7). (Reprinted with permission from Ref . Copyright 2014 American Chemical Society)
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Geometry of (a) Au(1 1 1) and (b) Au(1 0 0) with numerical values of interatomic spacing and angles. The surface of the atomic layer is represented in larger spheres and the lower atomic layers are represented in smaller spheres and crosses. Dotted lines indicate the favorable orientations for aromatic amino acids. (Reprinted with permission from Ref . Copyright 2009 American Chemical Society)
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Structure and Mechanism > Computational Biochemistry and Biophysics
Molecular and Statistical Mechanics > Molecular Dynamics and Monte-Carlo Methods
Structure and Mechanism > Computational Materials Science

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