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

Abstract

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.

Predicting biomolecule adsorption on MoS2 nanosheets with high structural fidelity

“…However, the general trend of binding strength to MoS 2 is similar to some of these other materials, in which tryptophan, arginine and tyrosine are among the amino acids with the strongest binding, whereas alanine and aspartate support some of the weakest binding. However, there are notable contrasts with the energetic ranking of amino acid adsorption compared with h-BN, 37 in particular that Ile and Leu were ranked considerably stronger in relative binding strength on h-BN vs MoS 2 , whereas Lys was of the weakest amino acids on h-BN, yet relatively strong on MoS 2 . These data provide a baseline not only for designing new MoS 2 -binding peptides, but also gives insights into how to achieve 2D-materials-binding selectivity via sequence design.…”

“…36 An alternative but more elaborate strategy is to incorporate a greater number of hetero-atomic van der Waals parameters into the parameter set that can be focussed and tailored to address contributions that the mixing rules may not be sufficiently robust to recover. This bespoke type of force-field parametrisation strategy has been realised for several surface materials including boron nitride, 37 gold, [38][39][40] and graphene. 41 A general feature of this type of force-field is that the number of van der Waals parameters is much greater than those obtained from the mixing-rule strategy, but in turn might be able to better capture key structural interfacial properties of adsorbates on surfaces.…”

Modeling‐Led Materials‐Binding Peptide Design for Hexagonal Boron Nitride Interfaces

“…Long-ranged electrostatic interactions were treated using particle-mesh Ewald (PME), [55] with a cut-off at 11Å, whereas a force-switched cut-off, starting at 9Å and ending at 10 Å was used for the Lennard-Jones nonbonded interactions. A tested force field combination of CHARMM22* [56,57] for the amino acids or peptides, the TIPS3P water model, [58] and the BoNi-CHARMM force field [35] for the h-BN sheets was used for describing all possible inter-atomic interactions. During the simulations, all boron and nitrogen atoms in the h-BN surface were fixed in space.…”

“…Consistent with our predicted amino acid binding free energies, the aromatic and/or hydrophobic residues (leucine and tyrosine) are indicated to be strong binders whereas the charged residues in BP7 (aspartate and lysine) are poor binders. The four Energy values for amino acids taken from a previous study [35] are labelled with #.…”

“…[6][7][8][9][10] Alternatively, bio-related strategies based on specific, noncovalent interactions between biomolecules and material surfaces have become the focus of producing those nanomaterials in aqueous media with minimal defects and under more ambient condi-complete data set comprising the amino acid binding free energies at the aqueous h-BN interface is a barrier to fully understanding the binding mechanism at the atomic level. [35] Such amino acid binding data have provided clear guidance in the design of peptide sequences for binding to gold, [36] silver, [37,38] and graphene. [39] Moreover, the use of such amino acid data was a critical factor in recent work exploiting small data machine learning approaches based on Bayesian optimization to search for new materials-selective peptide sequences for Ag and Au surfaces.…”