Quantum chemical calculations using M06-2X/6-31G(d) level have been performed to understand the interactions between two α-amino acids (histidine and proline) individually with graphene. Two finite size graphene sheets containing 62 and 183 carbon atoms were considered and their edges were terminated with hydrogen atoms. For each of the two amino acids, we have performed the conformational analysis using MMFF force field implemented in Spartan ’16. From the conformers, we have chosen 3-4 most stable and 1-2 least stable conformers for proline and histidine to build the complexes with graphene. In case of forming the complexes, we have taken into account different possible orientations. The complexes were fully optimized at the M06-2X/6-31G(d) level using Gaussian 16 program package. Binding energies with and without basis set superposition error (BSSE) were calculated. The solvation effect on binding energies was evaluated by polarizable continuum model (PCM). HOMO-LUMO energy gaps were calculated at the TPSSh/6-31G(d)//M06-2X/6-31G(d) level. Our aim is to understand the binding affinity of histidine and proline with graphene in the gas and aqueous phases, and the effect of varying the graphene sheet on the binding affinity. In case of histidine-graphene complexes, the competition between π-π and C-H/N-H…π interactions exists. It should be noted that histidine exhibits higher binding energy than proline. This could be attributed to the presence of π-electrons and two nitrogen atoms in the five-membered ring of histidine. The most stable complex of proline-graphene shows multiple C-H…π interactions along with N-H…π interactions. Graphene size effect is negligible for both amino acids. Inclusion of solvent (water) reduces the binding energy slightly but the trend is retained. The binding energy is generally decreased in aqueous phase. Binding of proline and histidine does not change the band gap of graphene.

DFT study on binding of histidine and proline with graphene in gas and aqueous phases

256th National Meeting (2018)

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