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使用BiLSTM和分子动力学设计具有粉样蛋白-β结合和清除潜力的新

Vinod Kumar Yata¹, Om Pritam Das², Jarmani Dansana³

¹Department of Biotechnology, School of Allied and Healthcare Sciences, Malla Reddy University, Hyderabad, Telangana, India.

Frontiers in artificial intelligence

|December 5, 2025

概括

我们开发了一个新的AI框架,使用双向长期短期记忆 (BiLSTM) 来设计针对阿尔茨海默病的粉样β (Aβ) 的功能性. 人工智能生成的ADNP7显示出强大的结合亲和力和稳定性,显示出治疗发现的潜力.

科学领域:

生物化学生物化学
计算生物学计算生物学
人工智能的人工智能

背景情况:

生成型人工智能正在推进生物分子设计,但创建功能性,针对特定目标的是具有挑战性的.
胺ββ (Aβ) 是阿尔茨海默氏症病理学的核心.
现有的方法难以产生具有可靠的结合亲和力和稳定性的.

研究的目的:

引入和验证一个新的两阶段双向长期短期记忆 (BiLSTM) 框架,用于新的体设计.
为了生成针对阿尔茨海默病的关键参与者Aβ42的功能性.
建立一个可通用的AI驱动的管道,用于治疗性的发现.

主要方法:

在蛋白质上训练了一个BiLSTM AI管道,用基因本体学术语对Aβ相互作用进行注释.
在经过验证的片碎片上对模型进行了微调,以捕获动机.
应用生物物理过器,序列相似性分析和结构建模 (AlphaFold2) 进行候选选择.
通过分子对接 (pyDockWEB) 和分子动力学模拟验证了结合亲和力和稳定性.

主要成果:

产生了1000个序列,入围了11个人工智能设计的新 (ADNP1-ADNP11).
确定了ADNP7作为具有有利对接得分 (-63.33 kcal/mol) 的顶级候选者,并预测与Aβ聚合区域结合.
通过MM/PBSA分析,分子动力学模拟证实了具有强结合自由能量 (-50.6 kcal/mol) 的复杂稳定性.

结论:

精心调整的BiLSTM架构成功地产生了新的,稳定的,对治疗标具有高预测的结合亲和力.
由人工智能驱动的管道证明了在药物发现和合成生物学中功能性体设计的概括性.
计算验证侧重于结合;需要实验测试来确认Aβ清除潜力.

关键词:

这就是为什么BiLSTM.粉样化物-β-β 的存在.深度学习是一种深度学习.基因本体学注释注释分子动力学分子动力学质设计的设计.

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