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使用机器学习和虚拟蛋白质分解,采矿牛奶蛋白用于DPP-4抑制

Yiyun Zhang¹, Yiqing Zhu¹, Xin Bao¹

¹National Engineering and Technology Research Center for Fruits and Vegetables, College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, P.R. China.

Research (Washington, D.C.)

|June 18, 2024

概括

这项研究引入了一种结合机器学习和虚拟蛋白质分解的新方法,用于从牛奶蛋白中发现二二酶-IV (DPP-4) 抑制剂. 该方法成功地确定了强大的DPP-4抑制,GPVRGPF和HPHPHL,用于潜在的糖尿病治疗.

科学领域:

生物化学生物化学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

双基酸酶-IV (DPP-4) 酶抑制剂是治疗2型糖尿病的关键治疗类.
来自牛奶蛋白质的生物活性显示出作为天然DPP-4抑制剂的希望.

研究的目的:

开发和验证一种结合机器学习和虚拟蛋白质分解策略,用于识别DPP-4抑制.
从牛奶蛋白中发现和描述新的DPP-4抑制.

主要方法:

培训和评估五种机器学习模型 (GBDT,XGBoost,LightGBM,CatBoost,RF) 用于DPP-4抑制活性预测.
牛奶蛋白质的虚拟蛋白质分解,然后使用表现最好的模型 (LightGBM) 进行in silico选.
在体外验证,分子对接和分子动力学模拟已识别的.

主要成果:

在预测DPP-4抑制潜力方面,LightGBM获得了0.92±0.01的高AUC.
酸GPVRGPF和HPHPHL表现出显著的DPP-4抑制活性.
追溯GPVRGPF到β-氨酸 (chymotrypsin水解),和HPHPHL到 κ-氨酸 (茎胺/papain水解).

结论:

机器学习和虚拟蛋白质分解的整合为发现生物活性提供了一个有效的管道.
这一策略有助于预测生成的最佳酶解水解参数.
已识别的具有发展为新型DPP-4抑制糖尿病药物的潜力.

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Protein-protein Interfaces

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