Nanoscale programing of cellular and physiological phenotypes
Nanoscale programing of cellular and physiological phenotypes
批准号:
10320006
负责人:
NIKOLAY DOKHOLYAN
金额:
$73.88万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-01-01 至 2024-12-31
关键词:
AddressAlgorithmsBindingBiologicalBiologyBiotechnologyCellsCodeCommunicationComplexComputersDevelopmentDistalDockingEvaluationGrowthHuman BiologyIn SituLanguageLibrariesLightMethodsModelingMolecularMolecular ComputationsMolecular ConformationOutputPathway interactionsPhenotypePhysiologicalPrecision therapeuticsProcessProtein EngineeringProteinsRNAReactionRegulationResearchSignal PathwaySignal TransductionSiliconSiteSpeedStimulusStructureTechnologyTemperatureVertebral columnWritingalgorithm developmentbasebiological systemscell motilitydesignflexibilityhuman diseaseinsightnanoscalenew technologynovelprogramsprototyperational designsmall moleculesupercomputersynthetic biologytool
中文摘要
摘要
近年来,蛋白质设计的出现使我们能够开发出一种“纳米级”的蛋白质。
程序设计语言”,其中分子充当操作数,其构象状态充当
逻辑门通过蛋白质将这些操作数组合成更大的分子和分子复合物
工程将允许我们使用纳米级计算代理(NCAs)编写和执行“代码”。这些试剂
将响应任何给定的输入并返回期望的输出信号。虽然“计算”的速度将
虽然它比基于无机硅的计算机慢得多,但一个单元可以包含比
目前存在的任何超级计算机的CPU数量。利用自然进化
过程将允许代码在计算过程中“进化”,从而实现全新的算法。
发展理念NCA将彻底改变生物系统的研究,使人们能够更深入地了解
人类生物学和疾病,并促进原位精确治疗发展。由于NCA可以
扩展到生物系统中看不到的新反应和过程,这一领域的发展将激发
具有广泛影响的生物技术应用的增长,包括通常不考虑的领域
与生物学有关。与传统的合成生物学方法不同,
NCA是基于单链蛋白质的自主车辆。NCA提供了一个正交
和用于控制细胞表型的补充手段。在过去的12年里,我们的团队已经发展到
通过设计可以被光和小分子控制的蛋白质来实现这一目标。我们
设计的功能原型已经在细胞运动领域提供了有价值的见解。这里我们
计划(i)进一步扩大NCA输入的库,(ii)包括其他生物分子,如RNA,
我们的NCAs库,(iii)扩展在纳米级“编写”算法的方法组合。的
该建议的主要目标是:(1)扩展用于调节蛋白质的输入库。我们计划
利用/设计通过构象变化响应pH和温度的蛋白质,
目标蛋白的活性。(2)扩展我们的方法来建模和调节RNA分子。没有工具
目前存在用于小分子与RNA结合的计算评估(对接问题)。建模
由于主链的柔性,RNA的结构和动力学具有挑战性。我们计划开发一个平台,
解决RNA结构和小分子对接问题。(3)开发合理设计工具
蛋白质的变构网络在蛋白质中“重新连接”变构网络的技术还不存在。
利用我们映射变构的方法,我们计划建立一个搜索算法,
远端蛋白质位点之间的通讯途径。应对这些挑战将提供一个重要的
活细胞编程技术的飞跃。虽然本提案中概述的研究方向是
我们和其他人雄心勃勃,为这项技术的可行性和可实现性奠定了基础。
英文摘要
ABSTRACT
The advent of protein design in recent years has brought us within reach of developing a “nanoscale
programing language,” in which molecules serve as operands with their conformational states functioning as
logical gates. Combining these operands into larger molecules and molecular complexes through protein
engineering will allow us to write and execute “code” using nanoscale computing agents (NCAs). These agents
would respond to any given input and return a desired output signal. While the speed of the “computation” will
be significantly slower than that of inorganic silicon-based computers, one cell can contain more NCAs than
the number of CPUs in any supercomputer currently in existence. The ability to utilize natural evolutionary
processes would allow code to “evolve” in the course of computation, thus enabling radically new algorithmic
developments. NCAs will revolutionize the studies of biological systems, enable a deeper understanding of
human biology and disease, and facilitate development of in situ precision therapeutics. Since NCAs can be
extended to novel reactions and processes not seen in biological systems, growth of this field will spark the
growth of biotechnological applications with wide-ranging impact, including to fields not typically considered
relevant to biology. Unlike traditional approaches in synthetic biology that are based on rewiring of signaling
pathways in cells, NCAs are autonomous vehicles based on single chain proteins. NCAs offer an orthogonal
and complementary means for controlling cellular phenotypes. In the past 12 years, our group has developed
technology toward this end, by engineering proteins that can be controlled by light and small molecules. We
designed functional prototypes that have already offered valuable insights in the cellular motility field. Here, we
plan to (i) further expand the repertoire of NCA inputs, (ii) include other biological molecules, such as RNA, in
our library of NCAs, and (iii) expand the portfolio of methods for “writing” algorithms at the nanoscale level. The
main objectives of this proposal are: (1) Extend the repertoire of inputs for regulation of proteins. We plan
to utilize/design proteins that respond to pH and temperature via conformational change in order to modulate
the activities of target proteins. (2) Extend our approaches to model and regulate RNA molecules. No tools
currently exist for computational evaluation of small molecule binding to RNA (the docking problem). Modeling
the structure and dynamics of RNA is challenging due to backbone flexibility. We plan to develop a platform to
address both the RNA structure and small molecule docking problems. (3) Develop tools to rationally design
allosteric networks in proteins. The technology to “rewire” allosteric networks in proteins does not exist yet.
Capitalizing on our method for mapping allostery, we plan to build a search algorithm that will iteratively rewire
communication pathways between distal protein sites. Addressing these challenges will provide a significant
leap in technology for programming living cells. While the research directions outlined in this proposal are
ambitious, we and others have created the basis for this technology to be feasible and within reach.
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