通过机器学习揭示冷大气等离子体与氨基酸的相互作用机制

IF 2.9 3区 物理与天体物理 Q2 PHYSICS, APPLIED Plasma Processes and Polymers Pub Date : 2024-04-15 DOI:10.1002/ppap.202300230
Zhao‐Nan Chai, Xu‐Cheng Wang, Maksudbek Yusupov, Yuan‐Tao Zhang
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引用次数: 0

摘要

通过冷大气等离子体(CAP)与各种生物组织直接或间接的相互作用,等离子体医学在从伤口愈合到抗菌应用,甚至癌症治疗等各种医学领域都引起了极大的兴趣。尽管人们已经通过实验探索了 CAP 对氨基酸、肽和蛋白质的氧化作用,但对 CAP 治疗的基本机制仍然知之甚少。本研究引入了机器学习(ML)技术,基于反应分子动力学(MD)模拟获得的数据,在数百皮秒的时间尺度上有效地揭示了氨基酸与活性氧(ROS)的相互作用机理,该模拟以数天的巨大计算负荷探究了五种氨基酸与各种 ROS 的相互作用。氧化反应通常以 H-萃取开始,化学键的断裂和形成细节得以揭示;亚硝基化、羟基化和羰基化等修饰类型也可以观察到。此外,还通过改变模拟框中 ROS 的数量研究了 ROS 的剂量效应,结果表明与实验观测结果一致。为了克服反应式 MD 模拟中时间尺度和分子系统大小的限制,我们根据反应数据构建了一个具有五个隐藏层的深度神经网络(DNN),并采用该网络预测 ROS 剂量变化时氧化修饰的类型和发生概率(仅以秒为单位)。训练有素的 DNN 可以有效、准确地预测氧化过程和氧化产物,与反应 MD 模拟相比,计算效率提高了近十个数量级。这项研究表明,基于反应 MD 模拟或实验测量的数据,ML 技术在有效揭示等离子体医学的内在机制方面具有巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Unveiling the interaction mechanisms of cold atmospheric plasma and amino acids by machine learning
Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H‐abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well‐trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements.
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来源期刊
Plasma Processes and Polymers
Plasma Processes and Polymers 物理-高分子科学
CiteScore
6.60
自引率
11.40%
发文量
150
审稿时长
3 months
期刊介绍: Plasma Processes & Polymers focuses on the interdisciplinary field of low temperature plasma science, covering both experimental and theoretical aspects of fundamental and applied research in materials science, physics, chemistry and engineering in the area of plasma sources and plasma-based treatments.
期刊最新文献
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