Peng Jiao, Wang Li, Wang Pu, Pei Yong
Department of Chemistry, Key Laboratory for Green Organic Synthesis and Application of Hunan Province, Key Laboratory of Environmentally Friendly Chemistry and Applications of Ministry of Education, Xiangtan University, Xiangtan, Hunan 411105, China.
School of Minerals Processing and Bioengineering, Central South University, Changsha, Hunan 410083, China.
ACS Omega. 2023 Jun 12;8(25):23024-23031. doi: 10.1021/acsomega.3c02195. eCollection 2023 Jun 27.
The optimal adsorption sites and the binding energies of neutral Au 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 Au tends to bind with N atoms of amino groups in amino acids, except methionine, which tends to bind with Au through S atoms. Under water solvation, Au 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 Au 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 (Δ) of the interaction between Au clusters and amino acids. The main factors affecting the strength of the interaction between Au and amino acids were uncovered by the feature importance analysis.
基于密度泛函理论（DFT）计算，系统研究了气相和水溶剂化条件下中性金团簇与20种天然氨基酸的最佳吸附位点和结合能。计算结果表明，在气相中，除甲硫氨酸倾向于通过硫原子与金结合外，金倾向于与氨基酸中氨基的氮原子结合。在水溶剂化条件下，金团簇倾向于与氨基酸中氨基的氮原子和侧链氨基的氮原子结合。然而，甲硫氨酸和半胱氨酸通过硫原子与金原子的结合更强。基于DFT计算得到的水溶剂化条件下金团簇与20种天然氨基酸的结合能数据，提出了一种机器学习模型（梯度提升决策树）来预测金团簇与氨基酸相互作用的最佳结合吉布斯自由能（Δ）。通过特征重要性分析揭示了影响金与氨基酸相互作用强度的主要因素。
