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Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis

Collaborative Proposal: SI2-CHE: ExTASY Extensible Tools for Advanced Sampling and analYsis
合作提案:SI2-CHE:用于高级采样和分析的 ExTASY 可扩展工具
批准号:
1708353
负责人:
Mauro Maggioni
金额:
$14.56万
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:
2016-07-01 至 2017-08-31

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中文摘要
翻译
合作研究:SI2-CHEExTASY高级采样和分析可扩展工具SI2-CHEExTASY高级采样和分析可扩展工具通过SI2-CHE计划得到支持,该国际团队由Cecilia Clementi(莱斯大学)、Mauro Maggioni(杜克大学)Shantenu Jha(罗格斯大学)、Glenn Martyna(BM T.J.Watson实验室)、Charlie Laughton(诺丁汉大学)、Ben Leimkuhler(爱丁堡大学)、Iain Bethune(爱丁堡大学)和Panos Parpas(帝国理工学院)组成,用于开发高级采样和分析扩展可扩展工具包,这是一个概念和软件框架,提供了大分子系统构象空间采样的一步变化。具体地说,Extasy是一个轻量级工具包,可实现对基于集合的模拟的一流支持,以及它们与动态分析能力和超大时间步长集成方法的无缝集成,同时可通过精心设计的标准接口扩展到其他社区软件组件。该项目的主要影响是生物科学。该软件促进了我们对生物重要系统的理解,因为它可以用于获得稳定蛋白质构象动力学的快速和准确采样;这是准确预测热力学参数和生物功能的先决条件。它还可以处理像天生无序的蛋白质这样的系统,这可能超出了经典结构生物学的范围。除了研究本身,PI还参与了外展项目,以吸引高中生对科学感兴趣。
英文摘要
Collaborative Research: SI2-CHEExTASY Extensible Tools for Advanced Sampling and analYsisAn international team consisting of Cecilia Clementi(Rice University), Mauro Maggioni (Duke University) Shantenu Jha (Rutgers University), Glenn Martyna (BM T. J. Watson Laboratory ), Charlie Laughton (University of Nottingham), Ben Leimkuhler ( University of Edinburgh), Iain Bethune (University of Edinburgh) and Panos Parpas(Imperial College) are supported through the SI2-CHE program for the development of ExTASY -- Extensible Toolkit for Advanced Sampling and analYsis, -- a conceptual and software framework that provides a step-change in the sampling of the conformational space of macromolecular systems. Specifically, ExTASY is a lightweight toolkit to enable first-class support for ensemble-based simulations and their seamless integration with dynamic analysis capabilities and ultra-large time step integration methods, whilst being extensible to other community software components via well-designed and standard interfaces. The primary impacts of this project are in the biological sciences. This software advances our understanding of biologically important systems, as it can be used to obtain fast and accurate sampling of the conformational dynamics of stable proteins; a prerequisite for the accurate prediction of thermodynamic parameters and biological functions. It also allows tackling systems like intrinsically disordered proteins, which can be beyond the reach of classical structural biology. Along with the research itself, the PIs are involved with outreach programs to attract high school students to science.
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BIGDATA: F: Compositional Learning, Maps and Transfer: Statistical and Machine Learning on Collections of Data Sets
  • 批准号:
    1837991
  • 项目类别:
    Standard Grant
  • 资助金额:
    $70.0万
  • 财政年份:
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  • 负责人:
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  • 依托单位:
ATD: Estimation and Anomaly Detection for high-dimensional Data, Maps and Dynamic Processes
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    1737984
  • 项目类别:
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  • 资助金额:
    $25.0万
  • 财政年份:
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    Mauro Maggioni
  • 依托单位:
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  • 批准号:
    1756892
  • 项目类别:
    Standard Grant
  • 资助金额:
    $37.99万
  • 财政年份:
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  • 负责人:
    Mauro Maggioni
  • 依托单位:
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  • 批准号:
    1708553
  • 项目类别:
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  • 资助金额:
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  • 财政年份:
    2016
  • 负责人:
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  • 依托单位:
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