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支架匹配器:一种基于协方差矩阵自适应进化策略(CMA-ES)的算法,用于识别热点对齐的拟肽支架。

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

作者信息

Claussen Erin R, Renfrew P Douglas, Müller Christian L, Drew Kevin

机构信息

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

Center for Computational Biology, Flatiron Institute, New York, New York, USA.

出版信息

Proteins. 2024 Mar;92(3):343-355. doi: 10.1002/prot.26619. Epub 2023 Oct 24.

Abstract

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

摘要

设计蛋白质相互作用抑制剂是解决导致疾病的异常蛋白质相互作用的一种有前景的方法。设计抑制剂的一种策略是使用模拟天然相互作用界面的拟肽支架。然而,将拟肽用作蛋白质相互作用抑制剂的一个核心挑战是确定分子支架如何最好地与它试图模拟的界面残基对齐。在这里,我们提出了支架匹配器算法,该算法将给定的分子支架与蛋白质相互作用界面的热点残基对齐。为了优化分子支架的自由度,我们实施了协方差矩阵自适应进化策略(CMA-ES),这是Rosetta中一种先进的无导数优化算法。为了评估CMA-ES的性能,我们使用了来自FlexPepDock基准测试的26种肽,并与Rosetta中的其他三种算法进行了比较,具体来说,是Rosetta的默认最小化器、一种小主链扰动的蒙特卡罗协议和一种遗传算法。我们测试了这些算法在将分子支架与天然肽上的一系列热点残基(即约束)对齐的能力方面的性能。在这4种方法中,CMA-ES能够为所有26种基准肽找到最低能量构象。此外,作为概念验证,我们将带有CMA-ES的支架匹配算法应用于将一种拟肽寡氧代哌嗪支架与严重急性呼吸综合征冠状病毒2(SARS-CoV-2)主要蛋白酶底物的热点残基对齐。我们将CMA-ES在Rosetta中的实现允许在具有粗糙能量景观的大分子建模问题上使用替代优化方法。最后,我们的支架匹配器算法允许识别相互作用抑制剂的初始构象,这些构象可以进一步设计和优化为高亲和力试剂。

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