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Updated: May 10, 2025

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通过应用人工智能来分子建模蛋白质结构的形状灵活性的表征

Kirill Kopylov¹, Evgeny Kirilin², Vladimir Voevodin³

¹Lomonosov Moscow State University, Research Computing Center, Leninskie Gory 1-4, 119234 Moscow, Russia; Lomonosov Moscow State University, Belozersky Institute of Physicochemical Biology, Leninskie Gory 1-40, 119992 Moscow, Russia; Lomonosov Moscow State University, Faculty of Bioengineering and Bioinformatics, Leninskie Gory 1-73, 119991 Moscow, Russia.

Journal of structural biology

|April 26, 2025

概括

这项研究整合了人工智能 (AI) 和高性能计算 (HPC) 来建模蛋白质结构. 该方法准确地预测了蛋白质构造及其能量格局,推进了结构生物学.

科学领域:

结构生物学结构生物学
计算生物学计算生物学
生物物理学的生物物理.

背景情况:

人工智能 (AI) 已经推进了具有未解决结构的蛋白质区域的建模.
当前的人工智能方法产生了许多结构模型,但评估它们的能源景观需要基于物理的方法.
将快速模型生成与精确的功能性形状确定相结合至关重要.

研究的目的:

提出一种综合方法,结合分子建模,人工智能和高性能计算 (HPC).
探索灵活蛋白质区域的潜在能量景观,使用潜空间中的元动力学模拟.
通过已知的蛋白质折叠和形状可塑性模型来验证该方法.

主要方法:

利用人工智能驱动的建模工具 (例如,AlphaFold,RosettaFold) 进行灵活蛋白质区域的初始近似.
在潜在空间中使用元动力学模拟来探索能量景观.
集成的人工智能和HPC,用于有效地探索和分析分子构造.

主要成果:

成功模拟了Trp-蛋白的折叠和ubiquitin的形状可塑性.
鉴定了弗拉依赖的2-基双-3-单氧基酶 (EC 1.14.13.44) 的活性中心内移动区域的占主导地位的构造.
估计了与酶活性中心识别的构造变化相关的能量.

结论:

集成的人工智能,分子建模和HPC方法有效地模拟蛋白质结构动态和能量景观.
这种方法为优先考虑人工智能生成的蛋白质模型和理解功能性构造提供了一个强大的框架.
该方法通过准确确定功能相关的蛋白质状态来推进结构生物学.

关键词:

2-基双-3-单氧基酶基质道超球的潜空间是超球的潜空间.建模结构优先级的优先级.蛋白质的构成路径是蛋白质构成路径.未解决的地区建模.

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