Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network.
Kinetic Modeling and Parameter Estimation of a Prebiotic Peptide Reaction Network.
复制标题
益生元肽反应网络的动力学建模和参数估计。
DOI：
10.1007/s00239-023-10132-1
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发表时间：
2023
影响因子：
3.9
通讯作者：
Yin,John
中科院分区：
文献类型：
--
作者：
Boigenzahn,Hayley;González,LeonardoD;Thompson,JaronC;Zavala,VictorM;Yin,John
虽然我们对地球上的生命是如何从简单的有机前体中出现的理解是推测性的，但早期的前体可能包括氨基酸。氨基酸聚合成肽和肽之间的相互作用是感兴趣的，因为肽和蛋白质参与现存生物学中复杂的相互作用网络。然而，肽反应网络可能是具有挑战性的研究，因为多个物种和物种之间的系统水平的相互作用的潜力。我们开发并采用了一个计算网络模型来描述氨基酸之间形成二肽、三肽和四肽的反应。我们的实验开始与两个最简单的氨基酸，甘氨酸和丙氨酸，介导的三偏磷酸活化和干燥，以促进肽键的形成。在系统中的键形成和水解反应的参数估计被认为是约束不良，由于网络属性称为草率。在松散模型中，行为主要取决于参数组合的子集，但没有直接的方法来确定应该包括或排除哪些参数。尽管我们无法确定具体动力学参数的精确值，但我们可以对模型行为做出相当准确的预测。简而言之，我们的建模突出了理解复杂的益生元化学实验行为的挑战和机遇。
Although our understanding of how life emerged on Earth from simple organic precursors is speculative, early precursors likely included amino acids. The polymerization of amino acids into peptides and interactions between peptides are of interest because peptides and proteins participate in complex interaction networks in extant biology. However, peptide reaction networks can be challenging to study because of the potential for multiple species and systems-level interactions between species. We developed and employed a computational network model to describe reactions between amino acids to form di-, tri-, and tetra-peptides. Our experiments were initiated with two of the simplest amino acids, glycine and alanine, mediated by trimetaphosphate-activation and drying to promote peptide bond formation. The parameter estimates for bond formation and hydrolysis reactions in the system were found to be poorly constrained due to a network property known as sloppiness. In a sloppy model, the behavior mostly depends on only a subset of parameter combinations, but there is no straightforward way to determine which parameters should be included or excluded. Despite our inability to determine the exact values of specific kinetic parameters, we could make reasonably accurate predictions of model behavior. In short, our modeling has highlighted challenges and opportunities toward understanding the behaviors of complex prebiotic chemical experiments.