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潘瑟 - - 对于核标结合,混合化和能量回归的蛋白质亲和力

Parisa Aletayeb¹, Akash Deep Biswas², Stefano Rocca¹

¹Universita degli Studi di Milano.

RNA (New York, N.Y.)

|December 3, 2025

概括

我们开发了PANTHER评分,这是一个机器学习模型,用于预测蛋白质-RNA结合的自由能量 (ΔG). 这种方法克服了数据的局限性,为生物分子研究和药物发现提供了可靠的工具.

科学领域:

计算生物学计算生物学
生物物理学的生物物理.
机器学习机器学习

背景情况:

蛋白质-RNA相互作用对于细胞功能至关重要.
由于数据稀缺和相互作用的复杂性,准确预测结合自由能量 (ΔG) 是具有挑战性的.

研究的目的:

为了开发一种机器学习模型,PANTHER得分,用于预测蛋白质-RNA结合的自由能量.
为了解决蛋白质-RNA相互作用研究实验数据的局限性.

主要方法:

使用了局部到全球的方法,从分子动力学模拟中得出局部相互作用能量.
机器学习模型被训练来预测局部相互作用能量,并集成到PANTHER得分中.
该模型在测试和外部应激集上进行了评估,包括110个具有实验 ΔG 的复合体.

主要成果:

随机森林回归实现了最高的预测性能,在测试组中产生了0.80的皮尔森相关系数 (r) 和1.79kcal/mol的MAE.
该模型在应力组 (r=0.64,MAE=1.63 kcal/mol) 上表现出强大的预测能力.
在基准测试中,PANTHER评分的表现优于现有的工具.

结论:

PANTHER评分是预测蛋白质-RNA结合亲缘关系的有效工具.
机器学习可以克服预测复杂的生物分子相互作用的数据限制.
这种方法通过提供准确的结合能量预测,促进生物分子研究和药物发现.

关键词:

有约束力的自由能源.药物发现药物发现机器学习模型机器学习模型蛋白质-RNA 相互作用在RNA治疗方面,RNA疗法.

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