Density Functional Theory Computation and Machine Learning Studies of Interaction between Au3 Clusters and 20 Natural Amino Acid Molecules.

Abstract

The optimal adsorption sites and the binding energies of neutral Au3 clusters with 20 natural amino acids under the gas phase and water solvation were systematically investigated based on density functional theory (DFT) calculations. The calculation results showed that in the gas phase Au3 tends to bind with N atoms of amino groups in amino acids, except methionine, which tends to bind with Au3 through S atoms. Under water solvation, Au3 clusters tended to bind to N atoms of amino groups and N atoms of side chain amino groups in amino acids. However, methionine and cysteine bind more strongly to the gold atom through the S atom. Based on the binding energy data of Au3 clusters and 20 natural amino acids under water solvation calculated by DFT, a machine learning model (gradient boosted decision tree) was proposed to predict the optimal binding Gibbs free energy (ΔG) of the interaction between Au3 clusters and amino acids. The main factors affecting the strength of the interaction between Au3 and amino acids were uncovered by the feature importance analysis.