通过使用可解释机器学习来释PROTAC诱导的降解准的全基因组未研究的蛋白质
1Department of Computer Science, Hunter College, The City University of New York, New York City, New York, United States of America.
PLoS computational biology
|August 17, 2023
概括
化向的嵌合体 (PROTACs) 可以降解向蛋白质. 一个新的机器学习模型PrePROTAC预测了全基因组的PROTAC目标,确定了600多个新型候选人和药物开发的关键残留物.
科学领域:
- 生物化学 生化学
- 分子生物学分子生物学
- 药理学 药理学是指药理学的学科.
背景情况:
- 向蛋白解酶的嵌合体 (PROTACs) 是双功能分子,通过招募E3链酶来诱导向蛋白质的降解.
- 对于抗药性基因来说,PROTACs提供了一种治疗策略,但针对性蛋白质的范围在很大程度上仍未被探索.
- 确定新的PROTAC目标对于推进不可治愈疾病的治疗方法至关重要.
研究的目的:
- 开发一种预测模型,用于识别易受CRBN的PROTAC介导降解的全基因组蛋白标.
- 提高PROTAC活动预测模型的可解释性.
- 为基于PROTAC的疗法发现新的蛋白质标.
主要方法:
- 开发PrePROTAC,一个可解释的机器学习模型,使用基于变压器的蛋白质序列描述器和随机森林分类.
- 嵌入SHapley添加剂扩展 (eSHAP) 的应用用于in silico突变发生,以确定影响PROTAC活动的关键残留物.
- 对CRBN结合蛋白的全基因组预测和针对新目标的PROTAC化合物的建议.
主要成果:
- 在基准研究中,PrePROTAC取得了高性能 (ROC-AUC为0.81,平均精度为0.84),并在外部测试集上证明了可概括性.
- 通过eSHAP方法,成功地确定了对PROTAC活动至关重要的关键残留物,与现有的生物知识相一致.
- 确定了600多种新型,未被充分研究的可能被CRBN降解的蛋白质,包括与阿尔茨海默病相关的三个点.
结论:
- PrePROTAC是一个强大的和可解释的工具,用于预测全基因组的PROTAC目标.
- 该研究扩大了潜在的PROTAC目标的范围,并提供了一种方法来识别PROTAC设计的关键残留物.
- 这项工作促进了对阿尔茨海默氏症等疾病的基于PROTAC的新型治疗方法的发现.
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