Elucidating ‘design’ principles for engineering synthetic protein networks

通过自然选择，细胞已经进化出蛋白质网络来执行细胞生长，分化和死亡等基本功能。进化已经集中在能够在细胞内和细胞外条件的可变性（例如代谢物的可用性以及蛋白质的浓度和特异性）的背景下精确地起作用的一般网络“设计”上。这些“设计”原则的发现将允许首先，更深入地了解自然生物系统，其次，深入了解工程合成蛋白质网络，用于编程细胞执行各种任务（例如生物传感器和生物修复应用）。虽然合成生物学家已经发现了许多转录网络的“设计”原则，蛋白质网络的不同之处在于更快的时间尺度（例如ms到s）和更宽的空间分布（例如膜形态），需要独特的“设计”原则。我的长期研究目标是通过计算和实验相结合的方法阐明工程蛋白质网络的一般“设计”原则。该提案的第一部分侧重于开发蛋白质开关的分子设计所需的计算工具，以及模拟它们与天然蛋白质网络的相互作用。对于分子设计，我们将进一步开发我们的计算工具，用于生成蛋白质构象空间，包括蛋白质骨架的灵活性和主题功能。该工具通过推迟昂贵的能量计算，在相关的时间尺度（例如ms到s）内快速采样蛋白质开关的构象空间。为了制定“设计”原则，我们将进一步开发我们的计算工具，用于模拟蛋白质网络，以包括空间上不同区域（例如膜形态）中的数百万个分子。该工具将模拟大规模的生物分子相互作用的复杂的空间上不同的区域，将使用一个基于的model.In第二部分中，我们将使用这些工具来第一次建模，然后通过实验验证“设计”的原则，在蛋白质网络中准确的信号传播的限制。蛋白质网络准确地传播它们的信号，尽管许多蛋白质对其底物的特异性很低。用于细胞死亡的半胱天冬酶蛋白网络是一个很好的模型系统，因为半胱天冬酶对它们的靶底物是众所周知的非特异性的，但是实现了不同的细胞死亡路径。此外，令人好奇的是，半胱天冬酶在许多空间上不同的区域。为了解释半胱天冬酶的奥秘，我们将制定和测试一个“设计”的原则，准确的信号传播，使用非特定的本地活动在某些本地基板驱动特定的全球活动。本课题组发明的光激活半胱天冬酶将在实验中产生时空定位的半胱天冬酶信号。我们对蛋白质网络“设计”原理的发现将使其在工程合成网络中的应用能够可靠地编程细胞以执行各种任务。想象一下，编程一个细胞，检测，封装和消耗环境毒素。一旦完成，细胞就会死亡，不会留下任何遗传痕迹。该提案将分别开发用于建模和测试“设计”原则的计算和实验工具，这将有助于实现这一梦想。

Through natural selection, cells have evolved protein networks to perform essential functions such as cell growth, differentiation and death. Evolution has converged on general network 'designs' capable of functioning precisely in the context of variability in intracellular and extracellular conditions (e.g. availability of metabolites and the concentrations and specificity of proteins). The uncovering of these ‘design’ principles will allow first, a deeper understanding of natural biological systems and second, insight into engineering synthetic protein networks for programming cells to perform a wide variety of tasks (e.g. biosensor and bioremediation applications). While synthetic biologists have uncovered many ‘design’ principles in transcriptional networks, protein networks differ by operating at faster time scales (e.g. ms to s) and wider spatial distribution (e.g. membrane morphologies), requiring unique ‘design’ principles.My long term research objective is to elucidate general ‘design’ principles for engineering protein networks through combined computational and experimental approaches. The first part of the proposal focuses on the development of computational tools needed for the molecular design of protein switches and the simulation of their interactions with natural protein networks. For molecular design, we will further develop our computational tool for generating protein conformational spaces to include protein backbone flexibility and thematic functions. The tool rapidly samples the conformational space of protein switches in the relevant time scales (e.g. ms to s) by deferring expensive energy calculations. For formulation of ‘design’ principles, we will further develop our computational tool for simulating protein networks to include millions of molecules in spatially distinct regions (e.g. membrane morphologies). The tool will simulate large scale biomolecular interactions within the constraints of complex spatially distinct regions that will be defined using a polygon-based model.In the second part, we will use these tools to first model and then experimentally validate 'design' principles related to accurate signal propagation in protein networks. Protein networks accurately propagate their signals despite the fact that many proteins have low specificity to their substrates. The caspase protein network for cell death is a good model system because caspases are notoriously non-specific to their target substrates, but yet distinct paths to cell death are achieved. Furthermore, caspases are curiously found in many spatially distinct regions. To explain the caspase mystery, we will formulate and test a ‘design’ principle for accurate signal propagation that uses non-specific local activity on certain local substrates to drive specific global activity. Spatiotemporally localized caspase signals will be generated experimentally by light-activated caspases invented by our group. Our discoveries of 'design' principles for protein networks will allow their application in engineering synthetic networks for reliably programming cells to perform a diverse set of tasks. Imagine programming a cell that detects, encapsulates and consumes environmental toxins. Once completed, the cell dies leaving no genetic trace. This proposal will develop the computational and experimental tools for modeling and testing 'design' principles, respectively, that will help realize this dream.