DeePNAP:一种深度学习方法,可以从它们的序列中预测蛋白质-核酸结合的亲和力
Uddeshya Pandey1, Sasi M Behara1, Siddhant Sharma1
1Department of Biology, Indian Institute of Science Education and Research Tirupati, Tirupati 517507, India.
Journal of chemical information and modeling
|March 8, 2024
概括
DeePNAP仅使用序列数据来预测蛋白质-核酸相互作用的结合亲和力和突变诱导的自由能量变化. 这种机器学习模型为各种生物系统提供了高精度和通用性.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 分子相互作用 分子相互作用
背景情况:
- 预测蛋白质核酸 (PNA) 结合亲和力对于理解PNA相互作用 (PNAI) 至关重要.
- 现有的模型往往需要结构信息,并且仅限于特定的PNAI,由于结构数据稀缺,阻碍了概括性.
- 目前的工具通常预测单个参数,限制了它们的多功能性.
研究的目的:
- 开发一个多功能机器学习模型,DeePNAP,仅从序列预测PNA结合亲和力和突变效应.
- 克服依赖于结构数据和有限的PNAI范围的现有方法的局限性.
- 为快速准确预测PNAI参数提供一个工具.
主要方法:
- 利用来自ProNAB数据库的14,401条条目的大型异质数据集,包括野生类型和突变PNA复合体.
- 开发了DeepNAP,这是一个机器学习模型,使用基于序列的功能进行预测.
- 通过使用相关系数和K_D和ΔΔG预测的根平均平方误差来验证模型的性能.
主要成果:
- DeePNAP 完全从 PNA 序列中准确预测结合亲和力 (K_D) 和自由能量变化 (ΔΔG).
- 该模型显示了高相关系数和低根平均平方误差,表明强大的预测能力和通用性.
- 在真核生物和原核生物中实现了广泛的PNAI的精确预测.
结论:
- DeePNAP提供了一个强大的,基于序列的方法来预测PNA结合亲和力和突变效应,克服结构数据的限制.
- 该模型的通用性和多功能性使其成为PNAI研究的宝贵工具.
- 为DeePNAP提供了一个Web界面,以促进快速预测和更深入地了解生物系统中的PNAI.
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