DRLiPS:一种使用机器学习预测可用药物的RNA-小分子结合口袋的新方法
Sowmya Ramaswamy Krishnan1,2, Arijit Roy2, Limsoon Wong3
1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Nucleic acids research
|April 2, 2025
概括
一种新型号,可药性RNA-连接物结合口袋选择器 (DRLiPS),可以预测可药性RNA结合位点在核酸 (RNA) 目标上. 这促进了RNA向药物发现,通过识别与疾病相关的RNA分子用于治疗干预.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 分子生物学分子生物学
背景情况:
- 核糖核酸 (RNA) 在细胞信息传输中起着至关重要的作用.
- 鉴定用于疾病治疗的可用药物的RNA标是具有挑战性的,因为非编码RNA的复杂性.
- 现有的蛋白质可药性预测方法不适合RNA,因为它们的结合机制不同.
研究的目的:
- 开发一种基于结构的模型,用于预测RNA结合部位的药用性.
- 解决当前方法在确定RNA中心治疗点方面的局限性.
- 为推进RNA向药物发现提供计算工具.
主要方法:
- 开发了可用于药物治疗的RNA - 连接物结合口袋选择器 (DRLiPS) 模型.
- 利用一种新的策略来采样消极结合点,使用骨干模式搜索,详尽的口袋预测和盲目对接.
- 策划了一个外部盲测试数据集,包括实验和建模的apo状态RNA结构.
主要成果:
- 在外部测试数据集上,DRLiPS获得了0.70的F1得分,0.61的精度,0.89的特异性和0.73的回忆.
- 该模型的性能优于现有的方法,如DrugPred_RNA和RNACavityMiner.
- 选择的特征很好地概括到apo和holoRNA状态,并可以预测突变对药物适应性的影响.
结论:
- DRLiPS是一种有效的基于结构的模型,用于预测RNA结合部位的可用性.
- 该模型展示了可通用性,并优于现有的工具,有助于识别新型RNA药物标.
- DRLiPS可以帮助优化RNA吸附体用于小分子识别,并为研究人员提供免费可访问的平台.
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