Improved Hydroxyl Radical Footprinting for Modeling Protein Structure

改进的羟基自由基足迹用于蛋白质结构建模

基本信息

  • 批准号:
    8489306
  • 负责人:
  • 金额:
    $ 27.23万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2012
  • 资助国家:
    美国
  • 起止时间:
    2012-07-01 至 2017-06-30
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): One of the most popular and promising targets for HIV vaccine development are immunogens derived from gp120, an envelope glycoprotein essential for viral entry into CD4 cells. Recently, our collaborators have isolated multiple HIV broadly neutralizing antibodies to gp120 and shown that they require certain N-linked glycans for activity. However, some of these antibodies do not bind the free N-linked glycans, suggesting that at least some of them may also interact with the protein portion of gp120. The antibody.gp120 complexes too large for generation of high-resolution structures by NMR spectroscopy, and as the glycan appear to play key roles in the antibody epitopes, crystallization of the complexes is a daunting task. Computational modeling is an attractive approach for this problem, but purely computational approaches can generate models of questionable accuracy, and require empirical constraints or testing in order to generate a reliable model. We propose to characterize the various broadly neutralizing antibody epitopes using hydroxyl radical protein footprinting of the gp120-antibody complexes, a technique that labels a broad variety of amino acid side chains based on their accessibility to solvent. In order to improve the usefulness of the footprinting data for accurate, high-resolution model building, we propose to develop a number of improved footprinting methods, including accurate quantitation at the amino acid level to improve structural resolution and normalization protocols to generate absolute solvent accessible surface area values from footprinting data. We also propose to develop an appropriate scoring function to utilize solvent accessible surface areas as a constraint in molecular dynamics simulations, analogous to the use of distance constraints. From these improvements and their application to the characterization of gp120-antibody complexes, we anticipate the generation of accurate, experimentally-constrained models that correctly identify the epitope for each antibody. These models will be very important for the rational design of immunogens to raise the corresponding broadly neutralizing antibodies in a host through immunization.
描述(由申请人提供):HIV疫苗开发中最受欢迎和最有希望的靶点之一是来自gp 120的免疫原,gp 120是病毒进入CD 4细胞所必需的包膜糖蛋白。最近,我们的合作者已经分离出多种针对gp 120的HIV广泛中和抗体,并表明它们需要某些N-连接聚糖才能起作用。然而,这些抗体中的一些不结合游离的N-连接聚糖,这表明它们中的至少一些也可能与gp 120的蛋白质部分相互作用。抗体. gp 120复合物太大,无法通过NMR光谱法产生高分辨率结构,并且由于聚糖似乎在抗体表位中起关键作用,复合物的结晶是一项艰巨的任务。计算建模是解决这个问题的一种有吸引力的方法,但纯计算方法可能会生成精度可疑的模型,并且需要经验约束或测试才能生成可靠的模型。我们建议使用羟基自由基蛋白质足迹的gp 120-抗体复合物,一种技术,标记各种各样的氨基酸侧链的基础上,他们的可及性溶剂的各种广泛的中和抗体表位的特征。为了提高足迹数据的实用性,准确,高分辨率的模型构建,我们建议开发一些改进的足迹方法,包括在氨基酸水平上的准确定量,以提高结构分辨率和归一化协议,以产生绝对溶剂可及表面积值的足迹数据。我们还建议开发一个适当的评分函数,利用溶剂可及的表面面积作为分子动力学模拟中的约束,类似于使用的距离约束。从这些改进及其应用到gp 120-抗体复合物的表征中,我们预期生成准确的、实验约束的模型,该模型能够正确识别每个抗体的表位。这些模型对于合理设计免疫原以通过免疫在宿主中产生相应的广泛中和抗体将是非常重要的。

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 }}

Joshua S Sharp其他文献

Joshua S Sharp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('Joshua S Sharp', 18)}}的其他基金

Administrative Core
行政核心
  • 批准号:
    10165744
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Analytical and Biophysical Research Core
分析和生物物理研究核心
  • 批准号:
    10165746
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Analytical and Biophysical Research Core
分析和生物物理研究核心
  • 批准号:
    10392494
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Administrative Core
行政核心
  • 批准号:
    10885780
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Administrative Core
行政核心
  • 批准号:
    10611849
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Administrative Core
行政核心
  • 批准号:
    10392493
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Analytical and Biophysical Research Core
分析和生物物理研究核心
  • 批准号:
    10611851
  • 财政年份:
    2020
  • 资助金额:
    $ 27.23万
  • 项目类别:
Molecular Structure Determination by Mass Spectrometry and Computational Modeling
通过质谱和计算模型测定分子结构
  • 批准号:
    10735319
  • 财政年份:
    2018
  • 资助金额:
    $ 27.23万
  • 项目类别:
Improved Hydroxyl Radical Footprinting for Modeling Protein Structure
改进的羟基自由基足迹用于蛋白质结构建模
  • 批准号:
    8236656
  • 财政年份:
    2012
  • 资助金额:
    $ 27.23万
  • 项目类别:
Improved Hydroxyl Radical Footprinting for Modeling Protein Structure
改进的羟基自由基足迹用于蛋白质结构建模
  • 批准号:
    8681470
  • 财政年份:
    2012
  • 资助金额:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 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:
    $ 27.23万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了