Prediction and Design of Nucleic Acid Recognition by Repeat Proteins

重复蛋白核酸识别的预测和设计

基本信息

  • 批准号:
    8492692
  • 负责人:
  • 金额:
    $ 26.4万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2013
  • 资助国家:
    美国
  • 起止时间:
    2013-09-12 至 2015-07-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Atomically detailed molecular modeling is a powerful tool for the prediction and design of protein interactions. In this project, we will develop molecular modeling techniques for simulating interactions between tandem repeat proteins and their binding partners, focusing on repeat proteins that recognize nucleic acids. Repeat proteins typically have highly symmetrical structures, with individual repeat units assuming similar conformations; in the case of repeat proteins that recognize a modular partner such as a nucleic acid or peptide, this symmetry may also extend to the conformation of the binding partner. Our modeling framework will incorporate the symmetry of repeat-protein interactions in order to constrain the space of conformations and protein sequences that must be explored. Tandem repeat proteins provide an architecture for protein interactions that has been used repeatedly throughout biological evolution to generate specific binding proteins. Armadillo, TPR, ankyrin, and HEAT repeat proteins have been selected for use in protein-protein interactions, while C2H2 zinc fingers, PUF, and PPR repeats are widely deployed for sequence-specific recognition of nucleic acids. Recently, a novel family of bacterial repeat proteins - the transcriptional activator-like (TAL) effectors - has been discovered that recognizes DNA in a remarkably modular fashion, with each repeat targeting a single base of the DNA binding site according to a simple recognition code. Using this recognition code, engineered TAL effectors can be efficiently targeted to novel DNA sites; this capability is transforming current approaches to genome engineering. To understand the molecular mechanisms underlying this recognition code, we performed molecular modeling simulations of TAL effector-DNA interactions that used structural symmetry and predicted protein-DNA contacts to reduce the space of possible bound conformations. Using these simulations we were able to generate accurate molecular models of TAL effector-DNA complexes; we subsequently used these models to solve, by molecular replacement, the first crystal structure of a naturally occurring TAL effector in complex with DNA. We propose to extend these simulations to allow prediction and design of a diverse range of tandem repeat-protein:nucleic acid interactions, with two specific applications: (1) optimization of the TAL effector platform for modular DNA sequence recognition, and (2) prediction of the RNA binding mode of pentatricopeptide repeat (PPR) proteins, a widespread family of RNA binding proteins that may represent a new and powerful ssRNA targeting scaffold.
描述(由申请人提供):原子详细的分子建模是预测和设计蛋白质相互作用的有力工具。在这个项目中,我们将开发分子建模技术,用于模拟串联重复蛋白及其结合伙伴之间的相互作用,重点是识别核酸的重复蛋白。重复序列蛋白通常具有高度对称的结构,其中各个重复序列单元呈现相似的构象;在识别模块配偶体(诸如核酸或肽)的重复序列蛋白的情况下,这种对称性也可以延伸到结合配偶体的构象。我们的建模框架将纳入重复蛋白质相互作用的对称性,以限制空间的构象和蛋白质序列,必须探索。串联重复序列蛋白提供了蛋白质相互作用的结构,其在整个生物进化过程中被反复使用以产生特异性结合蛋白。Armadillo、TPR、锚蛋白和HEAT重复序列蛋白已被选择用于蛋白质-蛋白质相互作用,而C2 H2锌指、PUF和PPR重复序列被广泛用于核酸的序列特异性识别。最近,一个新的细菌重复蛋白家族-转录激活因子样(TAL)效应子-已被发现,以显着的模块化方式识别DNA,每个重复序列根据一个简单的识别代码靶向DNA结合位点的单个碱基。使用这种识别代码,工程TAL效应器可以有效地靶向新的DNA位点;这种能力正在改变目前的基因组工程方法。为了理解这种识别代码背后的分子机制,我们对TAL效应器-DNA相互作用进行了分子建模模拟,该模拟使用结构对称性并预测蛋白质-DNA接触以减少可能的结合构象的空间。使用这些模拟,我们能够生成TAL效应物-DNA复合物的精确分子模型;我们随后使用这些模型通过分子置换来解决天然TAL效应物与DNA复合物的第一个晶体结构。我们建议扩展这些模拟,以允许预测和设计不同范围的串联重复序列-蛋白质:核酸相互作用,具有两个特定的应用:(1)优化TAL效应器平台用于模块化DNA序列识别,以及(2)预测五肽重复序列(PPR)蛋白的RNA结合模式,这是一个广泛存在的RNA结合蛋白家族,可能代表一种新的强大的ssRNA靶向支架。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(3)

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Philip Bradley其他文献

Philip Bradley的其他文献

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{{ truncateString('Philip Bradley', 18)}}的其他基金

Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
将 T 细胞受体特征与基因表达谱整合以定义 T 细胞特异性和分化
  • 批准号:
    10433774
  • 财政年份:
    2022
  • 资助金额:
    $ 26.4万
  • 项目类别:
Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
将 T 细胞受体特征与基因表达谱整合以定义 T 细胞特异性和分化
  • 批准号:
    10569090
  • 财政年份:
    2022
  • 资助金额:
    $ 26.4万
  • 项目类别:
Integrating T cell receptor features with gene expression profiles to define T cell specificity and differentiation
将 T 细胞受体特征与基因表达谱整合以定义 T 细胞特异性和分化
  • 批准号:
    10593429
  • 财政年份:
    2022
  • 资助金额:
    $ 26.4万
  • 项目类别:
Molecular modeling and machine learning for protein structures and interactions
蛋白质结构和相互作用的分子建模和机器学习
  • 批准号:
    10191763
  • 财政年份:
    2021
  • 资助金额:
    $ 26.4万
  • 项目类别:
Molecular modeling and machine learning for protein structures and interactions
蛋白质结构和相互作用的分子建模和机器学习
  • 批准号:
    10707065
  • 财政年份:
    2021
  • 资助金额:
    $ 26.4万
  • 项目类别:
Molecular modeling and machine learning for protein structures and interactions
蛋白质结构和相互作用的分子建模和机器学习
  • 批准号:
    10631595
  • 财政年份:
    2021
  • 资助金额:
    $ 26.4万
  • 项目类别:
Molecular modeling and machine learning for protein structures and interactions
蛋白质结构和相互作用的分子建模和机器学习
  • 批准号:
    10406274
  • 财政年份:
    2021
  • 资助金额:
    $ 26.4万
  • 项目类别:
High-resolution modeling of protein-RNA interfaces
蛋白质-RNA 界面的高分辨率建模
  • 批准号:
    10641354
  • 财政年份:
    2017
  • 资助金额:
    $ 26.4万
  • 项目类别:
Rational design and functionalization of circular tandem repeat proteins
环状串联重复蛋白的合理设计和功能化
  • 批准号:
    9301141
  • 财政年份:
    2017
  • 资助金额:
    $ 26.4万
  • 项目类别:
High-resolution modeling of protein-RNA interfaces
蛋白质-RNA 界面的高分辨率建模
  • 批准号:
    10013238
  • 财政年份:
    2017
  • 资助金额:
    $ 26.4万
  • 项目类别:

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