Li Wanxing, Liu Xuejing, Liu Yuanfa, Zheng Zhaojun
School of Food Science and Technology, Jiangnan University, Wuxi214122, China.
J Chem Inf Model. 2025 Jan 27;65(2):603-612. doi: 10.1021/acs.jcim.4c01713. Epub 2025 Jan 7.
Antioxidant peptides (AOPs) hold great promise for mitigating oxidative-stress-related diseases, but their discovery is hindered by inefficient and time-consuming traditional methods. To address this, we developed an innovative framework combining machine learning and quantum chemistry to accelerate AOP identification and analyze structure-activity relationships. A Bi-LSTM-based model, AOPP, achieved superior performance with accuracies of 0.9043 and 0.9267, precisions of 0.9767 and 0.9848, and Matthews correlation coefficients (MCCs) of 0.818 and 0.859 on two data sets, outperforming existing methods. Compared with XGBoost and LightGBM, AOPP demonstrated a 4.67% improvement in accuracy. Feature fusion significantly enhanced classification, as validated by UMAP visualization. Experimental validation of ten peptides confirmed the antioxidant activity, with LLA exhibiting the highest DPPH and ABTS scavenging rates (0.108 and 0.437 mmol/g, respectively). Quantum chemical calculations identified LLA's lowest HOMO-LUMO gap (Δ = 0.26 eV) and C-H as the key active site contributing to its superior antioxidant potential. This study highlights the synergy of machine learning and quantum chemistry, offering an efficient framework for AOP discovery with broad applications in therapeutics and functional foods.
抗氧化肽（AOPs）在减轻氧化应激相关疾病方面具有巨大潜力，但其发现受到低效且耗时的传统方法的阻碍。为了解决这一问题，我们开发了一种创新框架，将机器学习和量子化学相结合，以加速AOP的识别并分析结构-活性关系。基于双向长短期记忆网络（Bi-LSTM）的模型AOPP在两个数据集上表现出色，准确率分别为0.9043和0.9267，精确率分别为0.9767和0.9848，马修斯相关系数（MCCs）分别为0.818和0.859，优于现有方法。与XGBoost和LightGBM相比，AOPP的准确率提高了4.67%。通过UMAP可视化验证，特征融合显著增强了分类效果。对十种肽的实验验证证实了其抗氧化活性，LLA表现出最高的DPPH和ABTS清除率（分别为0.108和0.437 mmol/g）。量子化学计算确定LLA的最低HOMO-LUMO能隙（Δ = 0.26 eV）且C-H是其具有卓越抗氧化潜力的关键活性位点。本研究突出了机器学习和量子化学的协同作用，为AOP发现提供了一个高效框架，在治疗学和功能性食品中有广泛应用。
