Eliminating Critical Systematic Errors In Structural Biology With Next-Generation Simulation

通过下一代模拟消除结构生物学中的关键系统误差

基本信息

批准号：
10710387
负责人：
James M Holton
金额：
$ 30.85万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
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：
2017-09-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10710387
关键词：
3-Dimensional Active Sites Area Back Binding Biological Models Biological Process Biology Breathing Catalysis Chemistry Cryoelectron Microscopy Crystallography Data Data Collection Data Set Deposition Detection Dimensions Discrimination Disease Dose Electrons Humidity Hybrids Hydrogen Image Ligand Binding Ligands Lighting Lipids Maps Membrane Proteins Metals Methodology Methods Modeling Modernization Molecular Molecular Conformation Molecular Structure Motion Movement Noise Paint Periodicals Photons Proteins Protocols documentation Radiation induced damage Reaction Reporting Resolution Roentgen Rays Sampling Series Side Signal Transduction Solvents Source Spottings Structure Surface Synchrotrons Syncope Techniques Technology Temperature Testing Time Water Work beamline computerized data processing conformer data modeling density detector electron density electron diffraction experimental study improved inhibitor insight interest macromolecule method development models and simulation multiple data sources next generation non-Native prevent restraint simulation structural biology success terabyte three dimensional structure three-dimensional modeling tomography vector virtual

项目摘要

PROJECT SUMMARY/ABSTRACT Macromolecular Crystallography (MX) is an established and widely used method for obtaining accurate, high- resolution 3D models of biological molecules, yet MX data contain information that has yet to be unlocked. Single-electron changes can be clearly visible at resolutions as low as 3.5 Å if systematic errors can be eliminated. Creating simulation technologies that can account for these errors will have significant impact on three fronts: 1) eliminating the structural changes and other caveats of radiation damage, which ultimately limits the amount of data available from a given sample 2) improving multi-crystal averaging and comparison by capturing and correcting non-isomorphism, which will open the gateway to arbitrary gains in signal-to-noise, 3) discriminating hotly contested alternative interpretations such as the presence or absence of a bound ligand, by creating simulations with more realistic solvent and protein models. To move towards damage-free data from a synchrotron, we will start by implementing a new kind of data collection we call “painting with X-rays” that leverages modern fast-framing detectors to combine the best features of broad-beam and micro-beam technologies: low dose contrast and isolation of the best parts of the crystal. We will then enhance zero-dose extrapolation to handle the rich temporal information made available by finely dividing up the available photons. We will build on our success correcting non-isomorphism in real space into reciprocal space, enabling merging of incomplete data such as XFEL stills into parametric structure factor frameworks. These low-dimensional frameworks will allow selection from a continuum of 3D molecular structures by dialing in desired parameter values, and will also be applied to cases where the parameters are known quantities, such as time-resolved, temperature series, humidity, or other reaction coordinates and variables controlled in an experiment. We will test these framework models against the thousands of non-isomorphous data sets that have been collected at our beamline and report on best practice. To enable robust interpretation of experimental data, we will extend these multi-conformer models with simulation-based solvent models. Our work will create standard protocols for comparing solvent density to alternative interpretations and to quantitatively assess how likely each simulated situation is compared to the real macromolecular crystallography data. In addition to distinguishing between different interpretations of the experimental data, improving solvent models will enhance understanding of how macromolecules influence and interact with other molecules near their surface. Collectively, we expect the benefits of eliminating these critical systematic errors to be transformative to both methods development and functional studies using complimentary structural techniques, such as CryoEM, SAXS, tomography and electron diffraction, especially hybrid methods that combine structural data from multiple sources.

项目摘要/摘要大分子晶体学（MX）是一种已建立的且广泛使用的方法，用于获得准确，高的生物分子的分辨率3D模型，但是MX数据包含尚未解锁的信息。如果系统错误可能是，单元更改可以在低至3.5Å的分辨率下清晰可见淘汰。创建可以解释这些错误的模拟技术将对三个方面：1）消除辐射损伤的结构变化和其他警告，最终限制从给定样本中可用的数据量2）改进多晶平均和比较通过捕获和纠正非同态，这将打开通往信号到信号的任意收益的门户， 3）区分备受争议的替代解释，例如存在或不存在绑定的配体，通过创建具有更现实的溶液和蛋白质模型的模拟。迈向无损害数据从同步器中，我们将开始实施一种新型的数据收集，我们称为“用X射线绘画” 这利用现代快速框架探测器结合了宽光束和微梁的最佳功能技术：低剂量的对比度和晶体最佳部分的隔离。然后，我们将增强零剂量外推以处理丰富的临时信息，可通过精细分配可用的照片提供。我们将基于我们的成功，将真实空间中的非同态纠正到相互空间，使合并能够合并不完整的数据（例如XFEL）静止为参数结构因子框架。这些低维框架将通过拨打所需的参数从3D分子结构的连续元素中进行选择值，也将应用于已知参数的情况，例如时间分辨，在实验中控制的温度序列，湿度或其他反应坐标和变量。我们将测试这些框架模型针对已收集在我们的光束线并报告最佳实践。为了启用对实验数据的强大解释，我们将扩展这些具有基于仿真的溶剂模型的多构造模型。我们的工作将创建标准协议将溶剂密度与替代解释进行比较，并定量评估每个解释的可能性将模拟情况与真实的大分子晶体学数据进行了比较。除了区分在实验数据的不同解释之间，改进溶剂模型将增强了解大分子如何影响并与其表面附近的其他分子相互作用。总的来说，我们预计消除这些关键系统错误的好处将对两者进行变革方法开发和功能研究，使用免费结构技术，例如冷冻， SAX，层析成像和电子衍射，尤其是结合结构数据的混合方法多个来源。

项目成果

期刊论文数量（9）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Structural basis for dimerization quality control.

DOI：
10.1038/s41586-020-2636-7
发表时间：
2020-10
期刊：
Nature
影响因子：
64.8
作者：
Mena EL;Jevtić P;Greber BJ;Gee CL;Lew BG;Akopian D;Nogales E;Kuriyan J;Rape M
通讯作者：
Rape M

Sphingomonas sp. KT-1 PahZ2 Structure Reveals a Role for Conformational Dynamics in Peptide Bond Hydrolysis.

DOI：
10.1021/acs.jpcb.1c01216
发表时间：
2021-06-10
期刊：
The journal of physical chemistry. B
影响因子：
0
作者：
Brambley CA;Yared TJ;Gonzalez M;Jansch AL;Wallen JR;Weiland MH;Miller JM
通讯作者：
Miller JM

GHB analogs confer neuroprotection through specific interaction with the CaMKIIα hub domain.

DOI：
10.1073/pnas.2108079118
发表时间：
2021-08-03
期刊：
Proceedings of the National Academy of Sciences of the United States of America
影响因子：
11.1
作者：
Leurs U;Klein AB;McSpadden ED;Griem-Krey N;Solbak SMØ;Houlton J;Villumsen IS;Vogensen SB;Hamborg L;Gauger SJ;Palmelund LB;Larsen ASG;Shehata MA;Kelstrup CD;Olsen JV;Bach A;Burnie RO;Kerr DS;Gowing EK;Teurlings SMW;Chi CC;Gee CL;Frølund B;Kornum BR;van Woerden GM;Clausen RP;Kuriyan J;Clarkson AN;Wellendorph P
通讯作者：
Wellendorph P

Structure of the Cladosporium fulvum Avr4 effector in complex with (GlcNAc)6 reveals the ligand-binding mechanism and uncouples its intrinsic function from recognition by the Cf-4 resistance protein.