(14d) Free Energy Analysis of Biomolecule Adsorption to Graphene-Cu(111) and Defective Graphene-Cu(111) Interfaces: Molecular Insights into Biofilm Formation and Adhesion

Graphene and graphene derivatives are potential candidates to be used as biofilm inhibiting coatings for metal surfaces due to their potential antibacterial properties.[1-2] Chemical vapor deposition (CVD), which involves the catalytic deposition of carbon atoms on a transition metal (such as copper), is both an alternative to mechanical exfoliation of graphite and a promising process for large scalable synthesis of graphene. However, CVD usually results in a number of structural defects and surface impurities. While defect-free graphene growth using CVD is still under investigation and these defects remain largely undesired, studies have shown defective graphene surfaces have properties distinct from the pristine layers. What remains to be known is what effect these surface defects have on biofilm adhesion and formation, since no foundational atomistic-level information is available on whether these defects produce positive or negative surface adsorption characteristics relevant to biofilm adhesion and formation.[3]

Recent work has hypothesized that biofilm formation and adhesion may be related to the adsorption of key, early protein molecules including exopolysaccharides (EPS) to metal surfaces.[4] Nevertheless, studies related to molecular mechanics of these early protein molecule-metal surface interaction and adsorptive performance of graphene and graphene derivative as surface coatings is rather scarce. As a first step towards exploring this hypothesis, we investigate the adsorption behaviors of 20 proteinogenic amino acids (as model compounds for microbes), the building blocks of proteins, on moire´superlattice of graphene on Cu(111), along with pristine and defect-induced graphene surfaces using molecular dynamics (MD) simulations. All the simulations were conducted in vacuum and in the presence of explicit water as solvent to deduce the effect of solvation on the adsorption behavior.

Specifically, the molecular dynamics simulations are conducted using the LAMMPS molecular dynamics simulation software package and the Adaptive Intermolecular Reactive Empirical Bond Order (AIREBO) and Assisted Model Building with Energy Refinement (AMBER) potentials.[5]–[7] The adsorption energies and the binding free energies of the amino acids on graphene-coated-Cu(111) and pristine/multi-layer/defective graphene modified surfaces are evaluated to assess the effect of surface defects on the adsorption phenomena. The adaptive biasing force technique has been used to map the complex free-energy landscapes produced by the combination of intra- and inter-molecular reaction coordinates.[8–9]

The results of this molecular-level study should aid in developing a larger, fundamental understanding of the interaction, adsorption, and adhesion of proteins and microbes to metals and two-dimensional surfaces (with and without defects), such as found in industrial and biomedical applications.

[1] S. V. Agarwalla et al., “Hydrophobicity of graphene as a driving force for inhibiting biofilm formation of pathogenic bacteria and fungi,” Dent. Mater., vol. 35, no. 3, pp. 403–413, 2019.

[2] M. Cacaci, C. Martini, C. Guarino, R. Torelli, F. Bugli, and M. Sanguinetti, “Graphene oxide coatings as tools to prevent microbial biofilm formation on medical device,” in Advances in Experimental Medicine and Biology, vol. 1282, Springer, 2020, pp. 21–35.

[3] S. P. Singh, S. Ramanan, Y. Kaufman, and C. J. Arnusch, “Laser-Induced Graphene Biofilm Inhibition: Texture Does Matter,” ACS Appl. Nano Mater., vol. 1, no. 4, pp. 1713–1720, 2018.

[4] J. Wang, K. M. Goh, D. R. Salem, and R. K. Sani, “Genome analysis of a thermophilic exopolysaccharide-producing bacterium - Geobacillus sp. WSUCF1,” Sci. Rep., vol. 9, no. 1, pp. 1–12, 2019.

[5] S. Plimpton, “Short-Range Molecular Dynamics,” J. Comput. Phys., vol. 117, no. 6, pp. 1–42, 1997.

[6] E. Darian and P. M. Gannett, “Application of molecular dynamics simulations to spin-labeled oligonucleotides,” J. Biomol. Struct. Dyn., vol. 22, no. 5, pp. 579–593, 2005.

[7] S. J. Stuart, A. B. Tutein, and J. A. Harrison, “A reactive potential for hydrocarbons with intermolecular interactions,” J. Chem. Phys., vol. 112, no. 14, pp. 6472–6486, 2000.

[8] E. Darve, D. Rodríguez-Gómez, and A. Pohorille, “Adaptive biasing force method for scalar and vector free energy calculations,” J. Chem. Phys., vol. 128, no. 14, 2008

[9] J. Hénin, G. Fiorin, C. Chipot, and M. L. Klein, “Exploring Multidimensional Free Energy Landscapes Using Time-Dependent Biases on Collective Variables,” J. Chem. Theory Comput., vol. 6, no. 1, pp. 35–47, 2010