Combining molecular dynamics simulations with crystallographic refinement

将分子动力学模拟与晶体学细化相结合

基本信息

  • 批准号:
    9243621
  • 负责人:
  • 金额:
    $ 26.69万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2017
  • 资助国家:
    美国
  • 起止时间:
    2017-01-01 至 2020-12-31
  • 项目状态:
    已结题

项目摘要

The principal goals of this project are the development of algorithms that allow one to make better con- nections between molecular simulations using force fields and the interpretation of crystallographic data on biomolecules. This will involve the following components: Improved models for X-ray scattering from bulk solvent. Most crystallographic refinements protocols try to place a few “ordered” solvent (water) molecules into the observed density, and treat the remaining solvent space as arising from a flat density. Neither part of this model is physcially correct: very few solvent molecules are really ordered, and the bulk is far from flat. This project will explore alternate methods for describing the scattering contribution from solvent, based on integral equation models and explicit molecular dynamics simulations. This is expected to lead to a more correct description of the solvent environment, which it turn should lead to more accurate electron densities and interpretations for the biomolecules themselves Improved models for biomolecular motion and conformational heterogeneity. The standard model for crys- tallographic refinement treats motion and heterogenety via optimization of atomic displacement parame- ters (ADPs) and some combination of rigid-body (TLS) motions for certain subsets of the biomolecule, along with the specification of “alternate conformations” where this can be determined from an examina- tion of the density. The proper description of correlated motions in crystals is likely to be key in obtaining better models for X-ray scattering, but relatively little is known about what motional models are the most physically realistic. Molecular dynamics simulations of super-cells (many unit cells) of biomolecular crys- tals will be used to examine these issues, with particular attention to the question of how well refined TLS parameters are likely to correspond to physical motions. Using modern force fields to guide X-ray refinement. My group has collaborated for about two years with the Phenix development team to create a software environment that can use modern force fields (as imple- mented, for now, in the Amber simulation package) to complement or replace geometric restraints of the Engh-Huber variety with the forces arising from a periodic molecular mechanics model. The initial version of this will be released in the Spring of 2016, and goes a long way towards automating the process from a user's perspective: running a preparation script and setting useAmber=true is often enough to turn on the new procedure. But much remains to be done: allowing for alternate conformations, ensemble-based refinement, and incorporation of implicit solvent models in a periodic environment will be top priorities, as will be more extended testing and incorporation of user feedback.
这个项目的主要目标是开发算法,使人们能够更好地进行计算。 力fi场分子模拟与结晶学数据解释的关系 在生物分子方面。这将涉及以下组成部分: 改进的体相溶剂X射线散射模型。大多数结晶学研究协议都试图 将一些“有序”的溶剂(水)分子放入观察到的密度中,并处理剩余的溶剂。 在密度上由fl产生的空间。这个模型的任何一个部分都不是系统上正确的:很少有溶剂 分子确实是有序的,而大部分分子远不是flat。这个项目将探索替代方法 为了描述溶剂的散射贡献,基于积分方程模型和显式 分子动力学模拟。预计这将导致对溶剂的更正确的描述 环境,它转向应该导致更准确的电子密度和对 生物分子本身 生物分子运动和构象异质性的改进模型。哭的标准模型- 通过原子位移参数的优化来处理运动和非均质性。 生物分子某些子集的TERS(ADP)和刚体(TLS)运动的某种组合, 连同“交替构象”的特殊fi阳离子,这可以通过检查来确定- 关于密度的问题。正确描述晶体中的相关运动很可能是获得 更好的X射线散射模型,但对运动模型最多的了解相对较少 身体上很逼真。生物分子晶体超细胞(多个单胞)的分子动力学模拟 TALS将用来检查这些问题,并特别注意fiNed TLS的重新使用情况 参数很可能与物理运动相对应。 使用现代化的力fi场来指导X射线治疗。我的团队已经合作了大约两年 菲尼克斯开发团队创建了一个可以使用现代Forcefi字段的软件环境(例如- 目前,在琥珀模拟包中添加),以补充或取代 由周期分子力学模型产生的力的Engh-Huber变化。初始版本 将于2016年春季发布,并将大大有助于将该过程从 用户观点:运行准备脚本并设置useAmber=TRUE通常就足以打开 新的程序。但仍有许多工作要做:允许以整体为基础的替代构象 在周期性环境中采用隐式溶剂模型将是重中之重,因为 将进行更广泛的测试并纳入用户反馈。

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

Core 4 - Computation Core
核心4-计算核心
  • 批准号:
    10245112
  • 财政年份:
    2012
  • 资助金额:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 项目类别:
SOFTWARE DVMT & DISSEMIN CON BETWEEN CHARMM & AMBER & DVMT OF REDUCED REP
软件动态管理
  • 批准号:
    6659396
  • 财政年份:
    2002
  • 资助金额:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 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:
    $ 26.69万
  • 项目类别:
    Continuing Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了