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 实验结构生物学产生的原子模型用于理解细胞过程 分子水平，也用于合理设计药物和治疗方法。低温电子显微镜单 粒子重建（cryo-EM SPR）根据对粒子图的解释生成这样的原子模型 通过平均数千张弱图像获得的库仑势，每个图像包含 悬浮在薄冰层中的单个大分子的投影快照。因此，冷冻电镜图 代表结构状态的平均值，并且经常由于两者而导致质量有限且不均匀 冷冻电镜 SPR 中使用的粒子的灵活性以及它们在冰中的非随机分布称为首选 方向。在由于缺乏细节（分辨率）而质量较低的地图中构建和重建结构模型 或者遇到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 全面的模型构建和验证，以及精心设计的实验数据反馈循环 分析。我们的模型构建方法生成的初始和中间模型将有助于冷冻电镜 SPR 通过稳定重建所有步骤的计算收敛来进行项目，但不引入偏差， 明确解决偏差消除和量化问题。目标 1 将重点关注从头开始使用的方法 改进冷冻电镜 SPR 的预测模型或实验模型，以便无需引入即可使用它们 偏见。在目标 2 中，用于细化和验证结构模型的新信息权重将是 制定和实施的目的是为了解决冷冻电镜 SPR 中信息质量不均匀的问题。目标3将重点关注 开发和实现将二维分类直接耦合到模型构建的方法。最后，目标 4 将 内部运动的目标分析和建模，以指导结构解释并改进 重建的决议。结果将被纳入商业发行的 CryoEMMA 套件中。 这些方法的竞争优势源于它们提供信息丰富的 3D 的能力 存在严重优选方向且单粒子信噪比低于 在目前的方法中。