Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, WI, 53706, USA.
Wisconsin Institute for Discovery, University of Wisconsin-Madison, 330 N. Orchard Street, Madison, WI, 53715, USA.
J Mol Evol. 2023 Oct;91(5):730-744. doi: 10.1007/s00239-023-10132-1. Epub 2023 Oct 5.
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.
尽管我们对于生命如何从简单的有机前体在地球上出现的理解还只是推测，但早期的前体可能包括氨基酸。氨基酸聚合形成肽以及肽之间的相互作用是很有趣的，因为肽和蛋白质参与了现存生物学中复杂的相互作用网络。然而，由于可能存在多种物质和物种之间的系统级相互作用，肽反应网络的研究具有一定的挑战性。我们开发并采用了一种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。我们的实验以两种最简单的氨基酸甘氨酸和丙氨酸为起始物质，通过三偏磷酸盐激活和干燥来促进肽键形成。由于网络的一个特性称为“松散性”，该系统中键形成和水解反应的参数估计值受到了严重限制。在一个松散的模型中，行为主要取决于参数组合的一个子集，但没有直接的方法来确定应该包括或排除哪些参数。尽管我们无法确定特定动力学参数的确切值，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前生物化学实验行为的挑战和机遇。
