Title : Computational study on binding affinities of acyclic ?-amino acids with graphene
Binding affinities of fifteen (15) naturally occurring acyclic α-amino acids individually with two finite size graphene sheets comprising 62 and 186 carbon atoms were investigated at M06-2X/6-31G(d) level. After performing conformational analysis for these fifteen amino acids using Merck Molecular Force Field (MMFF), geometries of all conformers were optimized at the HF/6-31G(d) level and then up to 300 conformers were chosen to be optimized at the density functional theory (DFT), M06-2X/6-31G(d) level using Spartan '18 program package. The most stable conformer for each of the two distinct hydrogen bonding backbone conformations was selected at the M06-2X/6-31G(d) level to build complexes with graphene by considering different possible binding modes. All complexes were fully optimized using M06-2X/6-31G(d) level. Binding energies with and without basis set superposition error (BSSE) correction were calculated and analyzed. Our study reveals the following: (1) type-2 conformation involving intramolecular hydrogen bonding of backbone hydrogen of carboxylic group (-COOH) with nitrogen of the backbone amino group is generally more stable than type-1 structures in majority of the amino acids, (2) multiple C-H…π, N-H…π, and O-H…π interactions contribute for high stabilization of the complexes, (3) the number of non-bonding interactions and their strength play crucial role in stabilizing the complexes, (4) type-2 conformation of amino acid tends to have higher binding affinity with graphene than type-1 in most cases, (5) the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) of graphene is not altered by the adsorption of amino acid irrespective of type-1 or type-2 conformations of any of the fifteen molecules. The data obtained from our computational study will be helpful for force field development and for future experiments on non-covalent interactions of amino acids with graphene.
Audience take away:
- To obtain fundamental knowledge on the number and types of inter- and intra-molecular interactions that affect the binding of amino acids with graphene.
- Thermodynamics of computational binding studies at DFT level will be useful to guide future experiments.
- Data from this study could be used for force field developments.
- This study will stimulate further research on interactions of biological molecules such as small and large peptides with graphene.