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Interactions at the Silica-Peptide Interface: Influence of the Extent of Functionalization on the Conformational Ensemble.

Anna Sola-RabadaMonika MichaelisDaniel J OliverMartin J RoeLucio Colombi CiacchiHendrik HeinzCarole C Perry
Published in: Langmuir : the ACS journal of surfaces and colloids (2018)
In this contribution, the effect of silica particle size (28 and 210 nm) and surface chemistry (i.e., hydroxyl, methyl, or amino groups) on peptide binding response is studied with a specific emphasis on the effect of the extent of functionalization on binding. Exhaustive characterization of the silica surfaces was crucial for knowledge of the chemistry and topography of the solid surface under study and, thus, to understand their impact on adsorption and the conformational ensemble of the peptides. The extent of surface functionalization was shown to be particle-size dependent, a higher level of 3-aminopropyl functionality being obtained for smaller particles, whereas a higher degree of methyl group functionality was found for the larger particles. We demonstrated that peptide interactions at the aqueous interface were not only influenced by the surface chemistry but also by the extent of functionalization where a "switch" of peptide adsorption behavior was observed, whereas the changes in the conformational ensemble revealed by circular dichroism were independent of the extent of functionalization. In addition to electrostatic interactions and hydrogen bonding driving interaction at the silica-peptide interface, the data obtained suggested that stronger interactions such as hydrophobic and/or covalent interactions may moderate the interaction. The insights gained from this peptide-mineral study give a more comprehensive view of mechanisms concerning mineral-peptide interactions which may allow for the design and synthesis of novel (nano)materials with properties tailored for specific applications.
Keyphrases
  • molecular dynamics simulations
  • molecular dynamics
  • single molecule
  • healthcare
  • escherichia coli
  • ionic liquid
  • photodynamic therapy
  • deep learning
  • monte carlo