3-D understanding of electron microscopy images of nano bio objects by computing generative mechanical models

Cryo electron microscopy records essentially projection images of each of many instances of a nano bio object. This data allows reconstruction of a stochastic model of the object, i.e., the mean and covariance functions of the electron scattering intensity of the object. Understanding the covariance function, which characterizes the heterogeneity of the instances of the object, is challenging because the covariance function is not wide-sense stationary but instead depends separately on the two three-dimensional positions. This paper describes a method, qualitatively motivated by fluctuation-dissipation theory, for estimating a spring-and-mass mechanical model of the object. Then, from the model, a wide range of properties of the object can be understood, such as normal modes of vibration.

[1]  Yili Zheng,et al.  Three-dimensional reconstruction of the statistics of heterogeneous objects from a collection of one projection image of each object. , 2012, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  Yaser Hashem,et al.  Efficient estimation of three-dimensional covariance and its application in the analysis of heterogeneous samples in cryo-electron microscopy. , 2015, Structure.

[3]  Conrad C. Huang,et al.  UCSF Chimera—A visualization system for exploratory research and analysis , 2004, J. Comput. Chem..

[4]  Wei Zhang,et al.  Heterogeneity of large macromolecular complexes revealed by 3D cryo-EM variance analysis. , 2008, Structure.

[5]  Bruno P. Klaholz,et al.  Structure of the 30S translation initiation complex , 2008, Nature.

[6]  Marek Kimmel,et al.  Identifying conformational states of macromolecules by eigen-analysis of resampled cryo-EM images. , 2011, Structure.

[7]  Pawel A Penczek,et al.  Exploring conformational modes of macromolecular assemblies by multiparticle cryo-EM. , 2009, Current opinion in structural biology.

[8]  John E. Johnson,et al.  Dynamics in cryo EM reconstructions visualized with maximum-likelihood derived variance maps. , 2013, Journal of structural biology.

[9]  H. Callen,et al.  Irreversibility and Generalized Noise , 1951 .

[10]  T. Creighton Methods in Enzymology , 1968, The Yale Journal of Biology and Medicine.

[11]  Kristian Kirsch,et al.  Theory Of Ordinary Differential Equations , 2016 .

[12]  Alexander Katsevich,et al.  Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem , 2013, SIAM J. Imaging Sci..

[13]  Timothy S Baker,et al.  Dynamic and geometric analyses of Nudaurelia capensis ω virus maturation reveal the energy landscape of particle transitions , 2014, Journal of molecular recognition : JMR.

[14]  Qiyu Jin,et al.  Iterative elastic 3D-to-2D alignment method using normal modes for studying structural dynamics of large macromolecular complexes. , 2014, Structure.

[15]  Willy Wriggers,et al.  Conventions and workflows for using Situs , 2012, Acta crystallographica. Section D, Biological crystallography.

[16]  Murali Rao,et al.  Directly reconstructing principal components of heterogeneous particles from cryo-EM images. , 2015, Journal of structural biology.

[17]  Chao Yang,et al.  Estimation of variance in single-particle reconstruction using the bootstrap technique. , 2006, Journal of Structural Biology.

[18]  Grant J Jensen,et al.  Plunge freezing for electron cryomicroscopy. , 2010, Methods in enzymology.

[19]  John E. Johnson,et al.  Effect of the viral protease on the dynamics of bacteriophage HK97 maturation intermediates characterized by variance analysis of cryo EM particle ensembles. , 2016, Journal of structural biology.