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使用机器学习和分子动力学模拟进行对接-HER2相互作用的基准测试

Duc Toan Truong^1,2, Quang Tung Dao³, Thi Thuy Mai Tran⁴

¹Laboratory for Chemical Computation and Modeling, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 70000, Vietnam.

|March 2, 2026

概括

研究人员确定了乳腺癌的HER2蛋白和抑制剂之间的关键相互作用. 一种新型化合物lig233的结合性明显强于lapatinib,为HER2抑制剂药物开发提供了新的途径.

科学领域:

生物化学生物化学
计算生物学计算生物学
药理学药理学是指药理学的学科.

背景情况:

HER2氨酸激酶是乳腺癌治疗中的关键标.
了解抑制剂-HER2相互作用对于开发有效的治疗方法至关重要.
现有疗法面临挑战,需要新的抑制剂策略.

研究的目的:

阐明抑制剂-HER2相互作用的分子机制.
为了识别新的强效抗HER2化合物.
建立一个对联体-HER2结合强度的基准.

主要方法:

机器学习预测回归模型.
原子分子动力学 (MD) 模拟.
雨抽样MD方法用于具有约束力的自由能量计算.
系统地挖掘800万种化学化合物.

主要成果:

确定了13种新的抗HER2候选化合物.
已确定与关键残留物 (Lys753, Leu796, Thr798, Asp863, Asp880) 的静电相互作用对于抑制剂强度至关重要.
化合物lig233显示出异常的结合自由能量 (-47 kcal/mol),超过了拉帕提尼布 (-21 kcal/mol).
233与Asp863和Asp880形成键,表明有强化学键.

结论:

对抑制剂-HER2结合机制的新见解.
Lig233代表了下一代HER2抑制剂的一个有前途的化合物.
这些发现可以指导实验开发更有效的乳腺癌疗法.

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