Development of an Interaction Model of the Protein–Nanocluster Interface by Machine Learning–Assisted Clustering of Amino Acids

ABSTRACT The interactions that occur in the interface of proteins and ligand‐stabilised metal nanoclusters are crucial to understand the adsorption process of biomolecules on the surface of these nanomaterials. Despite the relevance of the adsorption phenomena for biological applications, such as bioimaging, biosensing and targeted drug delivery, efforts to model the interactions observed in the interface of those systems are still scarce in the literature. In this work, a model of the interactions observed in the peptide–Au 38 (p‐MBA) 24 interface was developed, employing clustering analysis, an unsupervised machine learning technique. The accuracy of this model was evaluated by simulating the peptide–Au 38 (p‐MBA) 24 interaction using molecular dynamics simulations and density functional theory calculations. The insights derived from this model can also be applied to the context of protein–AuNC interactions, given that the model was developed to provide a generalisable approach. The developed model was able to predict the amino acids that could interact well or poorly with the gold nanoclusters (AuNC), defining the specific chemical groups responsible for the effect. The results obtained in this study can lead efforts to accelerate discoveries in the fields that rely on the understanding of the interaction observed in the protein–AuNC interface.