Molecular simulation insights on the in vacuo adsorption of amino acids on graphene oxide surfaces with varying surface oxygen densities
Source abstract
Abstract
In this fundamental study, a series of molecular dynamics simulations were performed in vacuo to investigate the energetics and select geometries of 20 standard amino acids (AAs) on pristine graphene (PG) and graphene oxide (GO) surfaces as a function of graphene surface oxygen density. These interactions are of key interest to graphene/biomolecular systems. Our results indicate that aromatic AAs exhibit the strongest total interactions with the PG surfaces due to π-π stacking. Tryptophan (Trp) has the highest aromaticity due to its indole side chain and, hence, has the strongest interaction among all AAs (−16.66 kcal/mol). Aliphatic, polar, and charged AAs show various levels of affinity to the PG sheets depending on the strength of their side chain hydrophobic interactions. For example, arginine (Arg) with its guanidinium side chain exhibits the strongest interaction with the PG sheets (−13.81 kcal/mol) following aromatic AAs. Also, glycine (Gly; a polar AA) has the weakest interaction with the PG sheets (−7.29 kcal/mol). When oxygen-containing functional groups are added to the graphene sheets, the π-π stacking in aromatic AAs becomes disrupted and perfect parallelism of the aromatic rings is lost. Moreover, hydrogen bonding and/or electrostatic interactions become more pronounced. Charged AAs exhibit the strongest interactions with the GO surfaces. In general, the AA-GO interactions increase with increasing surface oxygen density, and the effect is more pronounced at higher O/C ratios. This study provides a quantitative measure of AA-graphene interactions for the design and tuning of biomolecular systems suitable for biosensing, drug delivery, and gene delivery applications.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Graphene and Nanomaterials Applications · Graphene research and applications · Advanced biosensing and bioanalysis techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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