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蛋白质复合体的计算设计:结合亲和力的影响

Fathima Ridha¹, K Harini¹, N R Siva Shanmugam¹

¹Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India. gromiha@iitm.ac.in.

Chemical communications (Cambridge, England)

|November 28, 2025

概括

预测和设计生物分子结合亲和关系对于功能至关重要. 本综述探讨了使用人工智能和机器学习进行蛋白质-蛋白质,蛋白质-核酸和蛋白质-碳水化合物相互作用的计算策略.

科学领域:

生物化学和分子生物学
计算生物学计算生物学
在生命科学领域的人工智能

背景情况:

分子相互作用,包括蛋白质-蛋白质,蛋白质-核酸和蛋白质-碳水化合物结合,是细胞过程的基础.
结合亲和性和特异性是由生物分子复合体的3D结构和动态决定的.
尽管人工智能驱动的结构预测取得了进展,但准确的预测和结合 afinities 的工程仍然是一个重大挑战.

研究的目的:

审查用于预测和设计生物分子复合体中的结合亲和关系的计算策略.
突出机器学习和深度学习在亲和模型中的作用.
讨论功能生物分子组件的亲和导向设计的未来方向.

主要方法:

对用于亲和力预测和理性设计的新兴计算策略的审查.
讨论基于结构和基于序列的亲和模型,利用机器学习和深度学习.
评估当前的数据库,基准和用于预测突变效应的工具.

主要成果:

人工智能和深度学习正在推进基于结构和基于序列的亲和模型.
数据库和基准对于评估预测准确性至关重要.
现有工具可以预测突变对结合亲和力的影响.

结论:

计算策略,特别是人工智能驱动的方法,是预测和设计生物分子结合 afinities 的关键.
人工智能,高通量选和数据驱动建模的整合对设计功能生物分子组件具有前景.
需要进一步的研究来克服目前在亲和力预测和设计方面的挑战.

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