Collaborative Research: De novo Protein Constructs for Photosynthetic Energy Transduction

合作研究:用于光合能量转导的从头蛋白质构建体

基本信息

  • 批准号:
    1413295
  • 负责人:
  • 金额:
    $ 35.1万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2014
  • 资助国家:
    美国
  • 起止时间:
    2014-08-15 至 2017-07-31
  • 项目状态:
    已结题

项目摘要

With this award, the Chemistry of Life Processes Program is funding Michael J. Therien (Duke University), Jeffery G. Saven (University of Pennsylvania), and William F. DeGrado (University of California at San Francisco) for research to further the understanding of the precise way plants capture energy from sunlight. Proteins perform many functions in living organisms and catalyze the complex set of chemical reactions necessary for life. Among the most critical of these functions is the conversion of energy from one form to another, such as during photosynthesis, when plants convert sunlight to chemical energy. As a result of this process, tons of carbon dioxide are removed from the atmosphere every year. In this work, the investigators are building artificial proteins that mimic the behavior of proteins in plants involved in photosynthesis. This strategy provides an important means to test how natural photosynthetic proteins work. Important insights can then be used to develop novel proteins that enable energy conversion processes not found in nature. The work will have a broader impact on diverse fields such as biology and energy storage, through the heightened understanding of key molecular events involved in photosynthesis. There is further broad impact on the training of the next generation of scientists. The unique multi-institution structure provides additional opportunities for students of all educational levels, graduate and undergraduate as well as high school, to participate in an exciting collaborative investigation being carried out in three different states.In this research, key protein design principles that provide for photosynthetic energy transduction and storage are being elucidated. An integrated, multi-disciplinary approach is employed toward this goal, and focus is on the evolution of peptide-cofactor complexes that undergo photoinduced charge-transfer reactions, where the protein matrix stabilizes the charge-separated state and guides the efficient separation of electrons and holes. Toward this end: (i) light-harvesting and redox-active cofactors are being designed and synthesized; (ii) de novo proteins are also being designed to selectively bind linked assemblies of these units; (iii) these de novo proteins are then expressed and characterized; (iv) de novo photosynthetic proteins that undergo photo-induced electron transfer are being interrogated using state-of-the-art pump-probe transient optical methods; (v) experimental data is guiding cofactor and protein design and redesign, initially focusing on the positioning of appropriate amino acid side chains near donor and acceptor redox sites to modulate charge separation and charge recombination dynamics; and (vi) the spectroscopic and dynamical properties of re-designed assemblies that control orientation via self-assembly are being characterized as functions of their nanostructured electronic environments. Information from this study is providing new insights into aspects of protein structure and dynamics that are integral for highly efficient photonic energy conversion, pushing the limits of functional de novo design, and guiding the design of complex peptide-cofactor assemblies that have unique photosynthetic functionality.This project is co-funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences and the Computational and Data-Enabled Science and Engineering program
凭借该奖项,生命过程化学项目正在资助 Michael J. Therien(杜克大学)、Jeffery G. Saven(宾夕法尼亚大学)和 William F. DeGrado(加州大学旧金山分校)进行研究,以进一步加深对这一问题的理解。植物从阳光中获取能量的精确方式。蛋白质在生物体中发挥多种功能,并催化生命所需的一系列复杂的化学反应。其中最关键的功能是将能量从一种形式转换为另一种形式,例如在光合作用过程中,植物将阳光转化为化学能。通过这一过程,每年从大气中去除大量二氧化碳。在这项工作中,研究人员正在构建人工蛋白质,模仿植物中参与光合作用的蛋白质的行为。该策略提供了测试天然光合蛋白质如何工作的重要手段。然后可以利用重要的见解来开发新型蛋白质,从而实现自然界中未发现的能量转换过程。通过加深对光合作用中涉及的关键分子事件的理解,这项工作将对生物学和能源存储等不同领域产生更广泛的影响。这对下一代科学家的培训也产生了更广泛的影响。独特的多机构结构为所有教育水平的学生(研究生、本科生以及高中)提供了额外的机会,参与在三个不同州进行的令人兴奋的合作调查。在这项研究中,关键的蛋白质设计原理提供了光合作用能量的转换和储存正在被阐明。为了实现这一目标,采用了综合的、多学科的方法,重点是经历光诱导电荷转移反应的肽-辅因子复合物的进化,其中蛋白质基质稳定了电荷分离状态并引导电子和电荷的有效分离。洞。为此:(i)正在设计和合成光捕获和氧化还原活性辅因子; (ii) de novo 蛋白质也被设计为选择性结合这些单元的连接组件; (iii) 然后表达并表征这些从头蛋白质; (iv) 使用最先进的泵浦探针瞬态光学方法对经历光诱导电子转移的从头光合蛋白进行研究; (v) 实验数据指导辅因子和蛋白质的设计和重新设计,最初侧重于在供体和受体氧化还原位点附近放置适当的氨基酸侧链,以调节电荷分离和电荷重组动力学; (vi)通过自组装控制方向的重新设计的组件的光谱和动力学特性被表征为其纳米结构电子环境的函数。 这项研究的信息为高效光子能量转换所必需的蛋白质结构和动力学方面提供了新的见解,突破了功能从头设计的极限,并指导了具有独特光合作用功能的复杂肽-辅因子组装体的设计。该项目由分子和细胞生物科学部的分子生物物理学集群以及计算和数据支持的科学与工程计划共同资助

项目成果

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William DeGrado其他文献

Theoretical and experimental studies of complex peptide–membrane systems: general discussion
  • DOI:
    10.1039/d1fd90066h
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Mibel Aguilar;Kareem Al Nahas;Francisco Barrera;Patricia Bassereau;Margarida Bastos;Paul Beales;Burkhard Bechinger;Boyan Bonev;Izabella Brand;Amitabha Chattopadhyay;Ronald J. Clarke;William DeGrado;Evelyne Deplazes;Ana J. Garcia Saez;Bart Hoogenboom;Reidar Lund;Paula Milán Rodríguez;Paul O’Shea;Georg Pabst;Sreetama Pal;Aurélien Roux;John Sanderson;Enrico Federico Semeraro;Durba Sengupta;David P. Siegel;Leonie van 't Hag;Aishwarya Vijayakumar;Larisa Zoranić
  • 通讯作者:
    Larisa Zoranić
Behaviour and interactions of proteins and peptides with and within membranes; from simple models to cellular membranes: general discussion
  • DOI:
    10.1039/d1fd90067f
  • 发表时间:
    2021-12
  • 期刊:
  • 影响因子:
    3.4
  • 作者:
    Mibel Aguilar;Kareem Al Nahas;Francisco Barrera;Patricia Bassereau;Margarida Bastos;Paul Beales;Burkhard Bechinger;Boyan Bonev;Izabella Brand;Amitabha Chattopadhyay;William DeGrado;Patrick Fuchs;Ana J. Garcia Saez;Bart Hoogenboom;Shobhna Kapoor;Paula Milán Rodríguez;Justin Molloy;Paul O’Shea;Georg Pabst;Sreetama Pal;Amy Rice;Aurelien Roux;John Sanderson;John Seddon;Lukas K. Tamm;Aishwarya Vijayakumar
  • 通讯作者:
    Aishwarya Vijayakumar

William DeGrado的其他文献

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{{ truncateString('William DeGrado', 18)}}的其他基金

NSF/MCB-BSF: De novo design of minimalistic light-switchable protein binding domains
NSF/MCB-BSF:简约光可切换蛋白结合域的从头设计
  • 批准号:
    2306190
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Standard Grant
NSF/MCB-BSF: De novo design of minimalistic light-switchable protein binding domains
NSF/MCB-BSF:简约光可切换蛋白结合域的从头设计
  • 批准号:
    2306190
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Standard Grant
Collaborative Research: De Novo Protein Constructs for Photosynthetic Energy Transduction
合作研究:用于光合能量转导的从头蛋白质构建体
  • 批准号:
    2108660
  • 财政年份:
    2021
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Continuing Grant
Collaborative Research: De novo Protein Constructs for Photosynthetic Energy Transduction
合作研究:用于光合能量转导的从头蛋白质构建体
  • 批准号:
    1709506
  • 财政年份:
    2017
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Continuing Grant
Protein Mimetics Based on Beta Amino Acids
基于 β 氨基酸的蛋白质模拟物
  • 批准号:
    9905566
  • 财政年份:
    1999
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Standard Grant
Libraries of Template-Constrained Cyclic Peptides
模板限制的环肽文库
  • 批准号:
    9634646
  • 财政年份:
    1996
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Standard Grant

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Collaborative Research: DMREF: De Novo Proteins as Junctions in Polymer Networks
合作研究:DMREF:De Novo 蛋白质作为聚合物网络中的连接点
  • 批准号:
    2323316
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
    Standard Grant
Collaborative Research: FET: Small: De Novo Protein Scaffold Filling by Combinatorial Algorithms and Deep Learning Models
合作研究:FET:小型:通过组合算法和深度学习模型从头填充蛋白质支架
  • 批准号:
    2307571
  • 财政年份:
    2023
  • 资助金额:
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  • 项目类别:
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Characterizing the genetic etiology of delayed puberty with integrative genomic techniques
利用综合基因组技术表征青春期延迟的遗传病因
  • 批准号:
    10663605
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
Characterizing the genetic etiology of delayed puberty with integrative genomic techniques
利用综合基因组技术表征青春期延迟的遗传病因
  • 批准号:
    10663605
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
Collaborative Research: FET: Small: De Novo Protein Scaffold Filling by Combinatorial Algorithms and Deep Learning Models
合作研究:FET:小型:通过组合算法和深度学习模型从头填充蛋白质支架
  • 批准号:
    2307573
  • 财政年份:
    2023
  • 资助金额:
    $ 35.1万
  • 项目类别:
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