AF:CIF:Small:Computational structural biology: Reconstruction and understanding for heterogeneous biological macro molecular complexes based on electron microscopy images

AF:CIF:Small:计算结构生物学：基于电子显微镜图像的异质生物大分子复合物的重建和理解

• 批准号: 1217867
• 负责人: Peter Doerschuk
• 金额: $ 48.77万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217867&HistoricalAwards=false
• 关键词: AF; CIF; Small; Computational; structural

Macro molecular complexes that act as nano-scale machines provide thefunction of biological systems. Understanding the geometry and functioning ofthe complex as a machine, for instance, how do the various parts move relativeto each other, is a central goal of biology. Cryo electron microscopy providesone noisy image of each of many (e.g., 100,000) instances of the complex. Theobjective of this research is to compute a statistical description of the machinefrom the images and then compute a mechanical model of the machine fromthe statistical description. Understanding how existing complexes function iscentral to engineering changes in their function. Achieving changes would havebroad impact, for instance, in cancer treatment (inhibiting complexes that pro-tect tumor cells by pumping chemotherapy drugs out of tumor cells) and inimproving the efficiency of complexes involved in the production of biofuels.Further impacts include the training of two Ph.D. students who will come from a diverse pool of students.The complex in question will be described as a weighted sum of basis functions with randomGaussian weights. A maximum likelihood estimator is used to estimate themean and covariance of the weights. The estimator is computed by an expecta-tion maximization algorithm which requires substantial computation due to theunknown projection directions of the images. A "spring and mass" mechanicalmodel in thermodynamic equilibrium predicts the mean and covariance. Bycomparing the predictions of the mechanical model to the estimated mean andcovariance by Kullback-Leibler divergence, the parameters in the mechanicalmodel are estimated.

大分子复合物作为纳米尺度的机器提供了生物系统的功能。了解复杂的几何结构和功能，例如，如何使各个部分相对于彼此移动，是生物学的中心目标。低温电子显微镜提供了许多（例如，100，000）复杂的实例。本研究的目的是从图像中计算机器的统计描述，然后从统计描述中计算机器的机械模型。了解现有复合体的功能是工程改变其功能的核心。实现这些变化将产生广泛的影响，例如，在癌症治疗（通过将化疗药物从肿瘤细胞中泵出来抑制保护肿瘤细胞的复合物）和提高生物燃料生产中所涉及的复合物的效率方面。学生将来自不同的学生池。复杂的问题将被描述为一个加权和的基函数与随机高斯权重。最大似然估计用于估计权值的均值和协方差。该估计器由期望最大化算法计算，由于图像的投影方向未知，该算法需要大量的计算。一个“弹簧和质量”的热力学平衡力学模型预测的平均值和协方差。通过比较力学模型的预测值与Kullback-Leibler散度估计的均值和协方差，估计了力学模型中的参数。