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开发和验证AI/ML衍生拼接交换型寡核酸的开发和验证

Alyssa D Fronk¹, Miguel A Manzanares¹, Paulina Zheng¹

¹Envisagenics, Inc., Long Island City, NY, 11101, USA.

Molecular systems biology

|April 25, 2024

概括

人工智能和机器学习 (AI/ML) 识别有效的拼接交换寡核酸 (SSO) 来调节替代拼接. 这种方法提高了药物发现的透明度,并且在治疗三阴性乳腺癌方面表现有前途.

科学领域:

分子生物学分子生物学
生物信息学是一种生物信息学.
基因组学就是基因组学.

背景情况:

替代拼接 (AS) 是调节基因表达的一个关键机制.
拼接切换型寡核酸 (SSO) 是一种通过与前mRNA结合来调节AS的治疗剂.
目前用于识别功能性SSO的方法可能耗时且缺乏透明度.

研究的目的:

证明人工智能/机器学习 (AI/ML) 在识别功能性和治疗性SSO方面的实用性.
通过预测目标拼接因素,提高AI/ML驱动药物发现的透明度.
为了对AI/ML设计的SSO进行前性验证,用于三阴性乳腺癌 (TNBC) 的新型治疗点.

主要方法:

训练有素的XGBoost模型使用拼接因子 (SF) 的mRNA前结合概况和拼接酶组合数据.
利用Shapley和袋外分析来识别SSO结合点,并预测目标SF.
向已知SSO追溯应用AI/ML方法,并向TNBC细胞中的NEDD4L第13个异构体前性应用AI/ML方法.

主要成果:

人工智能/ML模型成功地确定了功能SSO结合点,并预测了目标SF,从而提高了发现过程中的透明度.
追溯分析通过确定以前已知的功能SSO的SF来验证AI/ML方法.
一个前性设计的SSO针对NEDD4L外形13减少了TNBC细胞的增殖和迁移,通过降低TGFβ通路的调节.

结论:

AI/ML提供了一种强大而透明的方法来识别功能性的SSO.
这种AI/ML策略可以加速药物发现并产生生物学见解.
用AI/ML设计的SSO准NEDD4L第13个外显子为TNBC提供了一个潜在的治疗策略.

关键词:

替代拼接是一种替代拼接.机器学习机器学习拼接切换的寡核酸.拼接因素拼接因素三重阴性乳腺癌是什么?

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