Determination of solution structures by NMR

通过 NMR 测定溶液结构

基本信息

  • 批准号:
    7367231
  • 负责人:
  • 金额:
    $ 18.16万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    1991
  • 资助国家:
    美国
  • 起止时间:
    1991-04-01 至 2008-08-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): The principal goals of this project are the development of algorithms that allow one to make the best use of NMR data to determine solution structures of biomolecules, to assess in a systematic fashion their accuracy and precision, and to explore the extent to which dynamical information can be extracted from NMR data. This will involve the following components: Updated refinement methods. Refinement models will be developed that use modern protein and nucleic acid force fields in combination with generalized Born or explicit solvation models, and which incorporate conformational disorder through the "locally enhanced sampling" model that uses multiple copies of portions of the macromolecule. Studies on protein and nucleic acid dynamics. Long-time scale molecular dynamics simulations will be used to model NMR relaxation, with attention paid to anisotropic tumbling, to the correlation between internal and overall motions, and to conformational disorder. This will include an analysis of contributions from internal motions to chemical shift anisotropy (CSA) relaxation and to CSA-dipolar cross-correlated relaxation. Slower, microsecond to millisecond motions uncovered by relaxation dispersion experiments will be studied using novel models to identify minor conformers and to estimate their rates of interconversion with other conformations. Initial applications will be to protein G, ribonuclease A, and dihydrofolate reducatase. Nuclear magnetic resonance (NMR) spectroscopy provides a powerful tool for probing the properties of proteins and nucleic acids under conditions like those in living cells. The project uses computational tools to help gain the most information from NMR, promoting our understanding of basic biochemical processes that underlie both healthy and diseased cells. Two of the proteins studied here (ribonuclease and dihydrofolate reductase) are important targets for cancer chemotherapy.
描述(由申请人提供):该项目的主要目标是开发算法,使人们能够最好地利用NMR数据来确定生物分子的溶液结构,以系统的方式评估其准确性和精度,并探索从NMR数据中提取动态信息的程度。这将涉及以下组成部分:更新完善方法。将开发使用现代蛋白质和核酸力场与广义玻恩或显式溶剂化模型相结合的精细模型,并通过使用大分子部分的多个拷贝的“局部增强采样”模型将构象紊乱纳入其中。蛋白质和核酸动力学研究。长时间尺度的分子动力学模拟将被用来模拟NMR弛豫,注意各向异性翻滚,内部和整体运动之间的相关性,以及构象紊乱。这将包括从内部运动化学位移各向异性(CSA)松弛和CSA偶极交叉相关松弛的贡献的分析。较慢,微秒到毫秒的运动发现的松弛分散实验将使用新的模型进行研究,以确定未成年人的构象,并估计其与其他构象的相互转化率。最初的应用将是蛋白G,核糖核酸酶A,和二氢叶酸还原酶。核磁共振(NMR)光谱学提供了一个强大的工具,用于探测蛋白质和核酸在活细胞等条件下的性质。该项目使用计算工具来帮助从NMR中获得最多的信息,促进我们对健康和患病细胞基础的基本生化过程的理解。这里研究的两种蛋白质(核糖核酸酶和二氢叶酸还原酶)是癌症化疗的重要靶点。

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(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

David A Case其他文献

David A Case的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('David A Case', 18)}}的其他基金

Combining molecular dynamics simulations with crystallographic refinement
将分子动力学模拟与晶体学细化相结合
  • 批准号:
    9243621
  • 财政年份:
    2017
  • 资助金额:
    $ 18.16万
  • 项目类别:
Core 4 - Computation Core
核心4-计算核心
  • 批准号:
    10245112
  • 财政年份:
    2012
  • 资助金额:
    $ 18.16万
  • 项目类别:
The Center for HIV RNA Studies (CRNA)
HIV RNA 研究中心 (CRNA)
  • 批准号:
    8512883
  • 财政年份:
    2012
  • 资助金额:
    $ 18.16万
  • 项目类别:
DEVELOPMENT AND TESTING OF IMPROVED ?XED-CHARGE FORCE ?ELDS FOR PROTEINS
改进的蛋白质固定电荷力场的开发和测试
  • 批准号:
    8364361
  • 财政年份:
    2011
  • 资助金额:
    $ 18.16万
  • 项目类别:
Computer Cluster for Computational and Structural Biology
用于计算和结构生物学的计算机集群
  • 批准号:
    7794139
  • 财政年份:
    2010
  • 资助金额:
    $ 18.16万
  • 项目类别:
MODEL BUILDING & SIMULATION OF DNA & RNA AT MULTIPLE LENGTH SCALES
建筑模型
  • 批准号:
    7957336
  • 财政年份:
    2009
  • 资助金额:
    $ 18.16万
  • 项目类别:
MODEL BUILDING & SIMULATION OF DNA & RNA AT MULTIPLE LENGTH SCALES
建筑模型
  • 批准号:
    7602248
  • 财政年份:
    2007
  • 资助金额:
    $ 18.16万
  • 项目类别:
MODEL BUILDING & SIMULATION OF DNA & RNA AT MULTIPLE LENGTH SCALES
建筑模型
  • 批准号:
    7358846
  • 财政年份:
    2006
  • 资助金额:
    $ 18.16万
  • 项目类别:
MODEL BUILDING & SIMULATION OF DNA & RNA AT MULTIPLE LENGTH SCALES
建筑模型
  • 批准号:
    7182446
  • 财政年份:
    2005
  • 资助金额:
    $ 18.16万
  • 项目类别:
MODEL BUILDING & SIMULATION OF DNA & RNA AT MULTIPLE LENGTH SCALES
建筑模型
  • 批准号:
    6978768
  • 财政年份:
    2004
  • 资助金额:
    $ 18.16万
  • 项目类别:

相似海外基金

DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Research Grant
CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 18.16万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了