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A Bespoke Force Field To Describe Biomolecule Adsorption at the Aqueous Boron Nitride Interface.

Akin BudiTiffany R Walsh
Published in: Langmuir : the ACS journal of surfaces and colloids (2019)
Reliable manipulation of the interface between 2D nanomaterials and biomolecules represents a current frontier in nanoscience. The ability to resolve the molecular-level structures of these biointerfaces would provide a fundamental data set that is needed to enable systematic and knowledge-based progress in this area. These structures are challenging to obtain via experiment alone, and molecular simulations offer a complementary approach to address this problem. Compared with graphene, the interface between hexagonal boron nitride (h-BN) and biomolecules is relatively understudied at present. While several force fields are currently available for modeling the h-BN/water interface, there is a lack of a suitable force field that can describe the interactions between h-BN, liquid water, and biomolecules. Here, we use density functional theory calculations to create a force field, BoNi-CHARMM, to describe biomolecular interactions at the aqueous h-BN interface. Verifying our force field presents an additional challenge, given the scarcity of available experimental data for these interfaces. We test our force field against experimental evidence regarding the water/surface contact angle and confirm that the force field provides experimentally consistent values. We also present preliminary data regarding predictions of the free energy of adsorption of a selection of amino acids at the aqueous h-BN interface, revealing arginine and tryptophan to be among the strongest binders. This force field provides an opportunity to initiate a systematic progression in our current understanding of how to capture the intermolecular interactions at the h-BN biointerface.
Keyphrases
  • single molecule
  • density functional theory
  • ionic liquid
  • electronic health record
  • healthcare
  • high resolution
  • amino acid
  • quantum dots
  • deep learning
  • gold nanoparticles
  • aqueous solution
  • data analysis