Reference free structure determination through eigenvectors of center of mass operatorsl

被引:16
作者
Coifman, Ronald R. [2 ]
Shkolnisky, Yoel [1 ]
Sigworth, Fred J. [3 ]
Singer, Amit [4 ,5 ]
机构
[1] Tel Aviv Univ, Sch Math Sci, Dept Appl Math, IL-69978 Tel Aviv, Israel
[2] Yale Univ, Dept Math, Program Appl Math, New Haven, CT 06520 USA
[3] Yale Univ, Sch Med, Dept Cellular & Mol Physiol, New Haven, CT 06520 USA
[4] Princeton Univ, Dept Math, Princeton, NJ 08544 USA
[5] Princeton Univ, PACM, Princeton, NJ 08544 USA
关键词
DIMENSIONALITY REDUCTION; ELECTRON CRYOMICROSCOPY; DIFFUSION MAPS; EIGENMAPS; TRANSFORM;
D O I
10.1016/j.acha.2009.11.003
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
Recovering the three-dimensional structure of molecules is important for understanding their functionality. We describe a spectral graph algorithm for reconstructing the three-dimensional structure of molecules from their cryo-electron microscopy images taken at random unknown orientations. We first identify a one-to-one correspondence between radial lines in three-dimensional Fourier space of the molecule and points on the unit sphere. The problem is then reduced to determining the coordinates of points on the sphere given a subset of their pairwise geodesic distances. To recover those coordinates, we exploit the special geometry of the problem, as rendered by the Fourier projection-slice theorem, to construct a weighted graph whose vertices are the radial Fourier lines and whose edges are linked using the common line property. The graph organizes the radial lines on the sphere in a global manner that reveals the acquisition direction of each image. This organization is derived from a global computation of a few eigenvectors of the graph's sparse adjacency matrix. Once the directions are obtained, the molecule can be reconstructed using classical tomography methods. The presented algorithm is direct (as opposed to iterative refinement schemes), does not require any prior model for the reconstructed object, and is shown to have favorable computational and numerical properties. Moreover, the algorithm does not impose any assumption on the distribution of the projection orientations. Physically, this means that the algorithm is applicable to molecules that have unknown spatial preference. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:296 / 312
页数:17
相关论文
共 18 条
[1]  
[Anonymous], 1985, Matrix Analysis
[2]   A framework for discrete integral transformations I - The pseudopolar Fourier transform [J].
Averbuch, A. ;
Coifman, R. R. ;
Donoho, D. L. ;
Israeli, M. ;
Shkolnisky, Y. .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 2008, 30 (02) :764-784
[3]   3D Fourier based discrete Radon transform [J].
Averbuch, A ;
Shkolnisky, Y .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2003, 15 (01) :33-69
[4]   Laplacian eigenmaps for dimensionality reduction and data representation [J].
Belkin, M ;
Niyogi, P .
NEURAL COMPUTATION, 2003, 15 (06) :1373-1396
[5]   Electron cryomicroscopy of biological machines at subnanometer resolution [J].
Chiu, W ;
Baker, ML ;
Jiang, W ;
Dougherty, M ;
Schmid, MF .
STRUCTURE, 2005, 13 (03) :363-372
[6]   Graph Laplacian tomography from unknown random projections [J].
Coifman, Ronald R. ;
Shkolnisky, Yoel ;
Sigworth, Fred J. ;
Singer, Amit .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2008, 17 (10) :1891-1899
[7]   Diffusion maps [J].
Coifman, Ronald R. ;
Lafon, Stephane .
APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, 2006, 21 (01) :5-30
[8]   Geometric diffusions as a tool for harmonic analysis and structure definition of data: Diffusion maps [J].
Coifman, RR ;
Lafon, S ;
Lee, AB ;
Maggioni, M ;
Nadler, B ;
Warner, F ;
Zucker, SW .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2005, 102 (21) :7426-7431
[9]   Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data [J].
Donoho, DL ;
Grimes, C .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2003, 100 (10) :5591-5596
[10]   FAST FOURIER-TRANSFORMS FOR NONEQUISPACED DATA [J].
DUTT, A ;
ROKHLIN, V .
SIAM JOURNAL ON SCIENTIFIC COMPUTING, 1993, 14 (06) :1368-1393