将基于物理的蛋白质-DNA能量学与机器学习相结合,以预测可解释的转录因子-DNA结合
1Department of Physics and Astronomy, University of California, Irvine, California 92697, United States.
Journal of chemical information and modeling
|October 24, 2025
概括
这项研究结合了基于物理的模拟和机器学习,以预测转录因子如何与DNA结合. 这种新方法准确地预测了结合 afinities,提供了对基因调节和疾病机制的见解.
科学领域:
- 计算生物学是一种计算生物学.
- 生物物理学的生物物理.
- 基因组学就是基因组学.
背景情况:
- 转录因子 (TFs) 通过与特定的DNA序列结合来调节基因表达.
- TF-DNA结合亲和力和特异性的改变与癌症和发育障碍等疾病有关.
- 准确预测TF-DNA相互作用对于理解基因调节和疾病至关重要.
研究的目的:
- 开发一个结合基于物理的模拟和机器学习 (ML) 的计算框架,用于预测蛋白质-DNA结合的亲和性和特异性.
- 提高TF-DNA结合预测的准确性和可解释性.
- 调查TF-DNA结合亲和力和特异性的关键分子决定因素.
主要方法:
- 结合了全原子分子动力学 (MD) 模拟和分子力学-一般化出生表面积 (MMGBSA) 计算.
- 使用机器学习模型 (神经网络,随机森林,支持矢量机器) 进行预测.
- 利用基因组背景蛋白结合微阵列 (gcPBM) 的高质量实验数据进行模型培训和验证.
主要成果:
- 在预测DNA结合亲缘关系时,获得了大约0.73的皮尔森相关性和0.4的平均绝对误差,超过了传统的MMGBSA.
- 确定了TF-DNA界面互补性和疏水性相互作用作为结合的关键决定因素.
- 突出了对TF-DNA界面结的进一步物理特征的需要,以实现序列依赖.
结论:
- 开发的基于物理的ML框架为准确和可解释的蛋白质-DNA相互作用预测提供了强大的方法.
- 这种方法有可能对TF-DNA结合进行可扩展的预测,进步我们对基因调节和疾病的理解.
- 这些发现为改善针对TF-DNA相互作用的诊断和治疗铺平了道路.
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