Reliable model building for cryo-EM

可靠的冷冻电镜模型构建

• 批准号: 10484081
• 负责人: Raquel Bromberg
• 金额: $ 86.54万
• 依托单位: LIGO ANALYTICS, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10484081
• 关键词: 3-Dimensional; Address; Alpha Particles; Architecture; Articular Range of Motion; Cell physiology; Classification; Complex; Computer software; Coupling; Cryoelectron Microscopy; Data Analyses; Data Set; Development; Disease; Electron Microscopy; Excision; Experimental Models; Feedback; Freedom; Grouping; Health; Ice; Image; Individual; Maps; Methods; Modeling; Molecular; Motion; Noise; Pharmacotherapy; Phase; Procedures; Process; Proteins; Resolution; Running; Sampling; Signal Transduction; Small Business Innovation Research Grant; Structural Models; Structure; Thinness; Translations; Validation; Weight; Work; base; cryogenics; design; flexibility; guided inquiry; improved; insight; macromolecule; model building; molecular modeling; novel; novel strategies; particle; predictive modeling; rational design; reconstruction; restraint; structural biology; vibration

Project Abstract Atomic models generated by experimental structural biology are used to understand cellular processes at the molecular level, and also for rational design of drugs and treatments. Cryogenic electron microscopy single particle reconstruction (cryo-EM SPR) generates such atomic models based on interpretation of maps of Coulomb potential that are obtained by averaging many thousands of weak images, with each image containing projection snapshots of individual macromolecules suspended in a thin layer of ice. Consequently, cryo-EM maps represent averages of structural states and frequently have limited and uneven quality resulting from both flexibility of particles used in cryo-EM SPR as well as their non-random distributions in ice called preferred orientation. Building and rebuilding structural models in maps that are low quality due to lack of detail (resolution) or having sampling and reconstruction problems is a challenge. In this proposal, we will address these challenges by developing and implementing methods for automatic and comprehensive model building and validation with carefully designed feedback loops to experimental data analysis. The initial and intermediate models generated by our model building methods will aid cryo-EM SPR projects by stabilizing convergence of computations at all steps of reconstruction, but without introducing bias, with bias removal and quantification being addressed explicitly. Aim 1 will focus on methods that will use ab initio predicted models or experimental models to improve cryo-EM SPR so that they can be used without introducing bias. In Aim 2, a new weighting of information for refinement and validation of the structural models will be developed and implemented to account for uneven quality of information in cryo-EM SPR. Aim 3 will focus on developing and implementing methods for directly coupling 2D classification to model building. Finally, Aim 4 will target analysis and modeling of internal motions to guide the structural interpretation and to improve the resolution of reconstructions. The results will be incorporated into the commercially distributed suite cryoEMMA. The competitive advantage of these approaches arises from their ability to provide highly informative 3D reconstructions in the presence of severe preferred orientation and for single particle signal-to-noise lower than in the current approaches.

项目摘要 由实验结构生物学产生的原子模型被用来理解细胞过程， 分子水平，以及药物和治疗的合理设计。低温电子显微镜 粒子重建（cryo-EM SPR）基于对以下的图的解释来生成这样的原子模型： 库仑势是通过平均数千个弱图像获得的，每个图像包含 悬浮在一层薄薄的冰层中的单个大分子的投影快照。因此，低温电磁图 代表结构状态的平均值，并且经常具有有限的和不均匀的质量，这是由于 冷冻EM SPR中使用的颗粒的灵活性以及它们在冰中的非随机分布被称为优选的 导向在地图中构建和重建由于缺乏细节（分辨率）而导致质量低下的结构模型 或者具有采样和重建问题是一个挑战。 在本提案中，我们将通过开发和实施自动和 通过精心设计的实验数据反馈回路，全面的模型构建和验证 分析.通过我们的模型构建方法生成的初始和中间模型将有助于cryo-EM SPR 通过在重建的所有步骤中稳定计算的收敛，但不引入偏差， 其中明确地解决了偏差去除和量化。目标1将集中于使用从头算的方法 预测模型或实验模型，以改善冷冻EM SPR，使得它们可以在不引入 bias.在目标2中，将对结构模型的细化和验证信息进行新的加权， 开发和实施，以解决cryo-EM SPR中信息质量不均匀的问题。目标3将侧重于 开发和实施直接将2D分类耦合到模型构建的方法。最后，Aim 4将 目标分析和内部运动建模，以指导构造解释，并提高 重建的解决方案。结果将纳入市售套件cryoEMMA中。 这些方法的竞争优势来自于它们能够提供高度信息化的3D 在存在严重的优先取向和单粒子信噪比低于 在目前的方法中。