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Generation, functionalization, and distribution of de novo designed protein nanomaterials

Generation, functionalization, and distribution of de novo designed protein nanomaterials
从头设计的蛋白质纳米材料的生成、功能化和分布
批准号:
1629214
负责人:
David Baker
金额:
$135.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2016
资助国家:
美国
项目状态:
已结题
起止时间:
2016-09-15 至 2021-08-31

项目摘要

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中文摘要
翻译
蛋白质是具有复杂三维(3D)结构的大分子,这些结构赋予它们复杂的细胞功能。 为了获得自然界中没有的复杂蛋白质结构,华盛顿大学的Baker和King教授以及加州大学洛杉矶分校的Yeates教授合作计算并精确控制蛋白质组装成3D结构。 该研究团队与工程和科学界分享这些能力,并构建了一个设计蛋白质纳米材料(DPN)数据库,使其他人能够快速建立这些进步。 长期目标是允许蛋白质基材料的可预测设计,用于广泛的应用,如靶向药物输送,能量转换功能材料和其他化学技术。 该跨学科项目为博士后研究人员和学生提供了极好的研究培训机会。 这项研究整合了理论,计算和实验,以关闭设计周期,创造新的基于蛋白质的纳米材料,目前无法使用传统的蛋白质工程方法。 它制定了从模块化构建块制备更坚固和复杂的蛋白质纳米材料的策略,其3D组装和拆卸可以响应环境条件的变化。 该研究团队招募公民科学家使用Foldit解决复杂的蛋白质设计问题,并提供创新投入以增强此类材料的功能化。
英文摘要
Proteins are large molecules that have complex three-dimensional (3D) structures that give them sophisticated cellular functions. To provide access to complex protein structures not found in nature, Professors Baker and King of the University of Washington and Professor Yeates of University of California Los Angeles collaborate to compute and precisely control the assembly of proteins into 3D architectures. The research team shares these capabilities with the engineering and scientific communities and constructs a Designed Protein Nanomaterial (DPN) database to enable others to rapidly build on these advances. The long-term goal is to allow predictable design of protein-based materials for use in a wide range of applications such as targeted drug delivery, functional materials for energy conversion, and other chemical technologies. The interdisciplinary project provides excellent research training opportunities to postdoctoral researchers and students. This research integrates theory, computation and experiment to close the design cycle on creation of new protein-based nanomaterials that are not currently accessible using conventional protein engineering approaches. It develops strategies to prepare more robust and complex protein nanomaterials from modular building blocks, whose 3D assembly and disassembly can occur in response to changes in environmental conditions. The research team recruits citizen scientists to solve complex protein design problems using Foldit, and to provide innovative input to enhance functionalization of such materials.
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MFB: Deep-Learning Enabled Structure Prediction and Design of Protein-DNA Assemblies
  • 批准号:
    2226466
  • 项目类别:
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  • 资助金额:
    $149.85万
  • 财政年份:
    2022
  • 负责人:
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  • 依托单位:
Co-production of a software tool for field-scale species distribution modelling (fs-SDM) and mapping using local biodiversity records
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    NE/V007726/1
  • 项目类别:
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    $25.61万
  • 财政年份:
    2020
  • 负责人:
    David Baker
  • 依托单位:
CIBR: Collaborative Research: CIBR Expanding structure coverage of genomes to facilitate macromolecular assembly determination.
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    1937533
  • 项目类别:
    Standard Grant
  • 资助金额:
    $47.36万
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    2019
  • 负责人:
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  • 依托单位:
RAPID: Empowering the Citizen Scientist in the Fight Against Ebolaviruses
  • 批准号:
    1523362
  • 项目类别:
    Standard Grant
  • 资助金额:
    $20.0万
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  • 负责人:
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  • 依托单位:
海外基金