Department of Chemical and Biological Engineering, Wisconsin Institute for Discovery , University of Wisconsin-Madison, 330 N. Orchard Street, Madison, WI, 53715, USA.
Orig Life Evol Biosph. 2021 Mar;51(1):71-82. doi: 10.1007/s11084-021-09603-6. Epub 2021 Feb 10.
Theoretical models of the chemical origins of life depend on self-replication or autocatalysis, processes that arise from molecular interactions, recruitment, and cooperation. Such models often lack details about the molecules and reactions involved, giving little guidance to those seeking to detect signs of interaction, recruitment, or cooperation in the laboratory. Here, we develop minimal mathematical models of reactions involving specific chemical entities: amino acids and their condensation reactions to form de novo peptides. Reactions between two amino acids form a dipeptide product, which enriches linearly in time; subsequent recruitment of such products to form longer peptides exhibit super-linear growth. Such recruitment can be reciprocated: a peptide contributes to and benefits from the formation of one or more other peptides; in this manner, peptides can cooperate and thereby exhibit autocatalytic or exponential growth. We have started to test these predictions by quantitative analysis of de novo peptide synthesis conducted by wet-dry cycling of a five-amino acid mixture over 21 days. Using high-performance liquid chromatography, we tracked abundance changes for >60 unique peptide species. Some species were highly transient, with the emergence of up to 17 new species and the extinction of nine species between samplings, while other species persisted across many cycles. Of the persisting species, most exhibited super-linear growth, a sign of recruitment anticipated by our models. This work shows how mathematical modeling and quantitative analysis of kinetic data can guide the search for prebiotic chemistries that have the potential to cooperate and replicate.
生命化学起源的理论模型依赖于自我复制或自催化，这些过程源自分子相互作用、募集和合作。此类模型通常缺乏有关涉及的分子和反应的详细信息，这使得那些试图在实验室中检测相互作用、募集或合作迹象的人几乎没有指导。在这里，我们开发了涉及特定化学实体的反应的最小数学模型：氨基酸及其缩合反应以形成新肽。两个氨基酸之间的反应形成二肽产物，其随时间呈线性富集；随后，这些产物的募集形成更长的肽，表现出超线性生长。这种募集可以相互回报：肽有助于和受益于一个或多个其他肽的形成；通过这种方式，肽可以合作，从而表现出自催化或指数增长。我们已经开始通过对五种氨基酸混合物进行干湿循环 21 天进行新肽合成的定量分析来测试这些预测。我们使用高效液相色谱法跟踪了 >60 种独特肽类物质的丰度变化。一些物质存在高度瞬态性，在采样之间出现了多达 17 种新物质和 9 种物质的灭绝，而其他物质则在许多循环中持续存在。在持续存在的物质中，大多数表现出超线性生长，这是我们模型预期的募集的标志。这项工作表明，数学建模和对动力学数据的定量分析如何指导寻找具有合作和复制潜力的前生物化学物质的研究。
