使用机器学习预测RNA-小分子相互作用的结合亲和力的可靠方法
Sowmya R Krishnan1,2, Arijit Roy2, M Michael Gromiha1,3,4
1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Briefings in bioinformatics
|January 23, 2024
概括
研究人员开发了机器学习模型,以预测小分子和各种核糖核酸 (RNA) 类型之间的结合亲和力. 这些模型加速了新型RNA药物标和疾病治疗抑制剂的发现.
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) 对于细胞调节至关重要,它们的调节失调与各种人类疾病有关.
- 越来越多的RNA被认为是治疗干预的潜在药物标.
- 识别新型RNA标及其小分子抑制剂对于推进疾病治疗至关重要.
研究的目的:
- 开发机器学习模型,用于预测小分子与六种特定RNA亚型的结合亲和力.
- 加速与疾病相关的RNA标和小分子抑制剂的识别.
- 为这些预测模型提供免费访问的Web服务器.
主要方法:
- 为六种RNA亚型量身定制的机器学习模型的开发:aptamers,miRNAs,重复,核糖体RNAs,核糖体开关和病毒RNAs.
- 分析RNA序列组成,灵活性和RNA结合联体的极性质作为关键预测特征.
- 使用刀测试进行评估,并使用外部盲测数据集进行验证.
主要成果:
- 开发的模型实现了0.83的平均皮尔森相关性 (r) 和0.66.6的平均绝对误差.
- 这些模型即使对几个RNA亚型的数据有限,也证明了可靠性.
- 性能超过了外部验证数据集上现有的定量结构-活动关系 (QSAR) 模型.
结论:
- 机器学习模型可以可靠地预测RNA-小分子结合亲和力.
- 这些模型是加速发现RNA向疗法的宝贵工具.
- 结合RNA-小分子的Affinity Predictor网络服务器可供公众使用.
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