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脚手架匹配器:一种基于CMA-ES的算法,用于识别热点对齐的形模拟脚手架

Erin R Claussen¹, P Douglas Renfrew², Christian L Müller^3,4,5

¹Department of Biological Sciences, University of Illinois at Chicago, Chicago, Illinois, USA.

|October 24, 2023

概括

新的Scaffold Matcher算法准确地将分子支架与蛋白质接口对齐. 这种方法使用Rosetta中的协差矩阵适应演化策略 (CMA-ES),优化了蛋白质-蛋白质相互作用抑制剂的药物设计.

科学领域:

计算生物学是一种计算生物学.
药物发现药物发现
蛋白质与蛋白质的相互作用

背景情况:

异常的蛋白质相互作用驱动疾病,需要向抑制剂.
二胺基支架是设计这些抑制剂的一个有前途的策略.
一个关键的挑战是将脚手架与天然蛋白质接口对齐.

研究的目的:

介绍Scaffold Matcher算法,用于将分子支架与蛋白质相互作用热点对齐.
实施和评估共变矩阵适应演化策略 (CMA-ES) 以在罗塞塔内部优化支架.
为了证明算法的实用性在设计潜在的蛋白相互作用抑制剂.

主要方法:

开发了Scaffold Matcher算法,以将分子支架与热点残留物对齐.
在Rosetta中集成CMA-ES,一种无衍生品优化算法,用于脚手架自由度优化.
使用FlexPepDock基准 (26个) 对其他罗塞塔算法进行CMA-ES评估.

主要成果:

CMA-ES超越了罗塞塔的默认最小化器,蒙特卡洛和遗传算法,为所有26个基准酸找到最低的能量构造.
成功地将CMA-ES的Scaffold Matcher应用于针对SARS-CoV-2主要蛋白酶的皮多米米特基支架.
证明CMA-ES是一种有效的优化方法,用于复杂的能源景观的宏分子建模.

结论:

与CMA-ES配合的Scaffold Matcher算法提供了一种有效的方法,用于将peptidomimetic支架与蛋白质接口对齐.
这种方法有助于识别初始形状,用于设计高亲和蛋白相互作用抑制剂.
集成CMA-ES为挑战分子建模问题提供了一个新的优化策略.

关键词:

在CMA-ES中,CMA-ES是指CMA.罗塞塔 (Rosetta) 是一个协差矩阵适应演变战略的演变策略.没有衍生品的优化优化.热点残留物热点残留物这是一种peptidomimetic.脚手架匹配器脚手架匹配器

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