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随机游走酶

Random-walk enzymes.

作者信息

Mak Chi H, Pham Phuong, Afif Samir A, Goodman Myron F

机构信息

Department of Chemistry, University of Southern California, Los Angeles, California 90089, USA.

Center for Applied Mathematical Sciences, University of Southern California, Los Angeles, California 90089, USA.

出版信息

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Sep;92(3):032717. doi: 10.1103/PhysRevE.92.032717. Epub 2015 Sep 17.

DOI:10.1103/PhysRevE.92.032717
PMID:26465508
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4672870/
Abstract

Enzymes that rely on random walk to search for substrate targets in a heterogeneously dispersed medium can leave behind complex spatial profiles of their catalyzed conversions. The catalytic signatures of these random-walk enzymes are the result of two coupled stochastic processes: scanning and catalysis. Here we develop analytical models to understand the conversion profiles produced by these enzymes, comparing an intrusive model, in which scanning and catalysis are tightly coupled, against a loosely coupled passive model. Diagrammatic theory and path-integral solutions of these models revealed clearly distinct predictions. Comparison to experimental data from catalyzed deaminations deposited on single-stranded DNA by the enzyme activation-induced deoxycytidine deaminase (AID) demonstrates that catalysis and diffusion are strongly intertwined, where the chemical conversions give rise to new stochastic trajectories that were absent if the substrate DNA was homogeneous. The C→U deamination profiles in both analytical predictions and experiments exhibit a strong contextual dependence, where the conversion rate of each target site is strongly contingent on the identities of other surrounding targets, with the intrusive model showing an excellent fit to the data. These methods can be applied to deduce sequence-dependent catalytic signatures of other DNA modification enzymes, with potential applications to cancer, gene regulation, and epigenetics.

摘要

依赖随机游走在异质分散介质中寻找底物靶点的酶,可能会留下其催化转化的复杂空间分布。这些随机游走酶的催化特征是两个耦合随机过程的结果:扫描和催化。在这里,我们开发分析模型以理解这些酶产生的转化分布,将一种侵入性模型(其中扫描和催化紧密耦合)与一种松散耦合的被动模型进行比较。这些模型的图解理论和路径积分解揭示了明显不同的预测结果。与由激活诱导的脱氧胞苷脱氨酶(AID)沉积在单链DNA上的催化脱氨实验数据进行比较表明,催化和扩散紧密交织,其中化学转化产生了新的随机轨迹,而如果底物DNA是均匀的则不会出现这些轨迹。分析预测和实验中的C→U脱氨分布都表现出强烈的上下文依赖性,其中每个靶点的转化率强烈取决于其他周围靶点的身份,侵入性模型与数据显示出极佳的拟合度。这些方法可用于推断其他DNA修饰酶的序列依赖性催化特征,在癌症、基因调控和表观遗传学方面具有潜在应用。

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本文引用的文献

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Clustered and genome-wide transient mutagenesis in human cancers: Hypermutation without permanent mutators or loss of fitness.人类癌症中的聚集性和全基因组瞬时诱变:无永久性诱变剂或适应性丧失的超突变
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A mathematical model for scanning and catalysis on single-stranded DNA, illustrated with activation-induced deoxycytidine deaminase.用于单链 DNA 扫描和催化的数学模型,以激活诱导的脱氧胞苷脱氨酶为例。
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An APOBEC cytidine deaminase mutagenesis pattern is widespread in human cancers.APOBEC 胞嘧啶脱氨酶致突变模式广泛存在于人类癌症中。
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AID and Apobec3G haphazard deamination and mutational diversity.AID 和 Apobec3G 偶然脱氨酶和突变多样性。
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Sequence-dependent sliding kinetics of p53.p53 序列依赖性滑动动力学
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