Artificial Metabolons: Models for proximity-driven control over multienzyme pathw

描述（由申请人提供）：构成代谢途径的连续酶通常在细胞内彼此密切相关。这种共定位提供了一种代谢区室化的手段，并且被认为对细胞功能至关重要。然而，因为这些多酶复合物（“代谢子”）是相当具有挑战性的研究在体内或分离而不破坏在体外，连续酶的动力学后果的接近已难以表征。我们将测试的假设，代谢途径可以通过改变酶的定位和协会进行调节。为此，我们将采用“自下而上”的方法，通过构建实验模型系统，在该系统中，控制酶的接近度以模拟稳定或瞬时的相互作用。这些人工代谢子的结果将与（i）计算模型和（ii）嘌呤体（激发模型的生物代谢子之一）进行比较。提出了两个目标：目标1。代谢区室模型。我们将从从头嘌呤生物合成途径的连续酶的支架在单和多层的几何形状，表征这些人工代谢的结构和动力学，并比较实验动力学结果，非本地化的控制和预测计算模型。目标二。实验和计算模型细胞中代谢区室化的研究。与Aim 1相似的代谢区室模型将被纳入微尺度细胞模型，旨在捕获细胞内环境的关键特征，包括受阻扩散、有限体积和有限数量的底物和酶分子。微体积的实验结果将与目标1的本体溶液数据和计算模型进行比较。总之，这项工作将提供新的见解，空间组织在多酶途径的可能优势。我们的研究结果将补充在体内和体外研究的生物代谢，并将提供信息的共定位可能的动力学优势。这项工作的影响将包括改善一般的代谢，特别是嘌呤体酶的共定位的理解。最终，这种理解可能会导致控制这些途径的全新方法。例如，从头嘌呤生物合成途径是抗癌药物设计的重要目标;因此，本文提出的工作的成功可能导致基于破坏酶复合物形成的新癌症治疗。我们预计，共定位将成为药物设计的重要目标，作为特定酶的抑制剂。公共卫生相关性：项目叙述这项工作将提供新的洞察空间组织在多酶途径的可能优势。例如，十步从头嘌呤生物合成途径是抗癌药物设计的重要靶标。从本文提出的模型系统中获得的知识将有助于指导该途径的体内工作，这可能最终导致基于破坏酶复合物形成的新癌症治疗。

DESCRIPTION (provided by applicant): The sequential enzymes that make up metabolic pathways often exist in close association with one another within the cell. Such co-localization provides a means of metabolic compartmentation, and is thought to be crucial for cell function. However, because these multienzyme complexes ("metabolons") are quite challenging to study in vivo or to isolate without disruption in vitro, the kinetic consequences of proximity for sequential enzymes have been difficult to characterize. We will test the hypothesis that metabolic pathways can be regulated by altering enzyme localization and association. To do this, we will employ a "bottom up" approach, by constructing experimental model systems in which enzyme proximity is controlled to mimic stable or transient interactions. Results from these artificial metabolons will be compared with (i) computational models and (ii) the purinosome, one of the biological metabolons that inspires the models. Two Aims are proposed: Aim 1. Models for metabolic compartmentation. We will attach sequential enzymes from the de novo purine biosynthesis pathway to scaffolds in mono- and multilayered geometries, characterize the structure and kinetics of these artificial metabolons, and compare the experimental kinetic results to non-localized controls and to predictions from computational models. Aim 2. Investigation of metabolic compartmentation in experimental and computational model cells. Metabolic compartmentation models similar to those of Aim 1 will be incorporated within microscale cell models designed to capture key features of the intracellular environment, including hindered diffusion, limited volume, and finite numbers of substrate and enzyme molecules. Experimental results in microvolumes will be compared with bulk solution data from Aim 1 and with computational models. Together, this work will provide new insight into the possible advantages of spatial organization in multienzyme pathways. Our findings will complement in vivo and in vitro studies of biological metabolons and will provide information on possible kinetic advantages of co-localization. Impacts of this work will include improved understanding of metabolons generally, and of purinosome enzyme co-localization in particular. Ultimately, this understanding may lead to entirely new approaches for controlling these pathways. For example, the de novo purine biosynthesis pathway is an important target for anticancer drug design; success of the work proposed here could therefore lead to new cancer treatments based on disrupting the formation of enzyme complexes. We anticipate that co-localization will become as important a target for drug design as inhibitors for specific enzymes. PUBLIC HEALTH RELEVANCE: Project Narrative This work will provide new insight into the possible advantages of spatial organization in multienzyme pathways. For example, the ten-step de novo purine biosynthesis pathway is an important target for anticancer drug design. Knowledge gained from the model systems proposed here will help guide in vivo work on this pathway, which could ultimately lead to new cancer treatments based on disrupting the formation of enzyme complexes.