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CAREER: Computational Enzymology of Non-Aqueous Biocatalysis: Application to Biomass Pretreatment

CAREER: Computational Enzymology of Non-Aqueous Biocatalysis: Application to Biomass Pretreatment
职业:非水生物催化的计算酶学:在生物质预处理中的应用
批准号:
1150596
负责人:
Jim Pfaendtner
金额:
$45.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2012
资助国家:
美国
项目状态:
已结题
起止时间:
2012-03-15 至 2019-02-28

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1150596-Pfaendtner Societal ability to design and produce chemicals and fuels from sustainable sources relies on future improvements in bioprocessing. Non-aqueous solvents such as ionic liquids may potentially revolutionize bioprocess efficiency and economics, particularly in the enzymatic pretreatment of biomass for fermentation. The potential for non-aqueous solvents to endow biological catalysts with improved or previously unknown properties is exciting and could transform their use in industry. In spite of this great promise, the incomplete understanding of the relationship between solvent, enzyme structure, and reactivity has hampered significant scientific technological development in this arena. Specifically, what is lacking is the ability to relate molecular features of the solvent to its effect on enzyme stability and activity. In cases where solvents lower the activity of the enzyme compared with its natural environment we do not have the tools to systematically understand the root cause of the loss of activity or how to effectively use mutagenesis techniques to overcome the activity loss.This CAREER project put forward By Jim Pfaendtner of the University of Washington will develop and validate a new multi-scale computational modeling toolkit for the discovery and optimization of bioprocess solvent environments. The target of the research proposal is the investigation of two glycoside hydrolase (GH) enzymes with a combination of three ionic liquids (ILs). These enzymes are widely used in bioprocessing applications and the ILs are chosen for their cost effectiveness and potential for use as biomass solubilization agents. Using advanced simulation techniques the PI and his group will systematically determine the underlying molecular scale mechanisms by which ILs change the activity of GH enzymes. Four research objectives are being pursued that will discriminate whether the activity changes are based on structure, dynamics or a combination of both. There are several technological broader impacts stemming from this project. Successful completion of the research will lead to new biomass utilization technologies that could eliminate one or more pretreatment stages. Additionally, this work will create a general computational approach for studying enzymes in non-aqueous environments. The new approach offers a new level of predictive capability that will speed scientific discovery, reduce experimental costs, and transform the way we use computers to guide bioprocess design and optimization. Integrated with the proposed research is an extensive outreach, education and training plan. Educational components include a simulation module for an AP chemistry class at a Seattle High School and a new biocatalysis module for the senior undergraduate laboratory course. Research training components include research opportunities for local High School teachers and collaboration with a campus outreach organization, Louis Stokes Alliance for Minority Participation, (LSAMP) to involve under-represented minorities in the PIs research group. This CAREER award is jointly made and sponsored by the Catalysis and Biocatalysis Program of the Chemical, Bioengineering, Environmental, and Transport Systems Division and the Chemical Catalysis Program of the Division of Chemistry.
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Collaborative Research: Mechanisms of Catalytic Enhancement of Immobilized Lipases by Tunable Polymer Materials
  • 批准号:
    2103613
  • 项目类别:
    Standard Grant
  • 资助金额:
    $32.84万
  • 财政年份:
    2021
  • 负责人:
    Jim Pfaendtner
  • 依托单位:
Collaborative Research: Experimental and computational methods to study chemical transformations of solid xylose into useful compounds
  • 批准号:
    1703638
  • 项目类别:
    Standard Grant
  • 资助金额:
    $22.5万
  • 财政年份:
    2017
  • 负责人:
    Jim Pfaendtner
  • 依托单位:
Combined molecular simulation and experimental study to discover, predict and control enzyme immobilization in polymeric nanoparticles
  • 批准号:
    1703438
  • 项目类别:
    Standard Grant
  • 资助金额:
    $33.13万
  • 财政年份:
    2017
  • 负责人:
    Jim Pfaendtner
  • 依托单位:
NRT-DESE: Data Intensive Research Enabling Clean Technologies (DIRECT)
  • 批准号:
    1633216
  • 项目类别:
    Standard Grant
  • 资助金额:
    $300.0万
  • 财政年份:
    2016
  • 负责人:
    Jim Pfaendtner
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data