Assembly of macromolecular complexes by satisfaction of spatial restraints from electron microscopy images - PubMed (original) (raw)

Assembly of macromolecular complexes by satisfaction of spatial restraints from electron microscopy images

Javier Velázquez-Muriel et al. Proc Natl Acad Sci U S A. 2012.

Abstract

To obtain a structural model of a macromolecular assembly by single-particle EM, a large number of particle images need to be collected, aligned, clustered, averaged, and finally assembled via reconstruction into a 3D density map. This process is limited by the number and quality of the particle images, the accuracy of the initial model, and the compositional and conformational heterogeneity. Here, we describe a structure determination method that avoids the reconstruction procedure. The atomic structures of the individual complex components are assembled by optimizing a match against 2D EM class-average images, an excluded volume criterion, geometric complementarity, and optional restraints from proteomics and chemical cross-linking experiments. The optimization relies on a simulated annealing Monte Carlo search and a divide-and-conquer message-passing algorithm. Using simulated and experimentally determined EM class averages for 12 and 4 protein assemblies, respectively, we show that a few class averages can indeed result in accurate models for complexes of as many as five subunits. Thus, integrative structural biology can now benefit from the relative ease with which the EM class averages are determined.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.

Fig. 1.

Flow chart of the scoring and sampling algorithms. (A) To calculate the em2D score, we project the model in evenly spaced directions on the hemisphere with positive _y-_axis values. The resulting projections and the input images are preprocessed and subsequently aligned in two dimensions to obtain an initial coarse alignment. The best alignments are refined using the Simplex algorithm to minimize the squared difference between the pixels of the image and the projection, providing the em2D score of the image. The total score for a model is the average of the individual image scores. (B) Inputs to the sampling protocol are the atomic structures of the assembly components and all the restraints. If chemical cross-linking data are available, we build a graph with nodes corresponding to assembly components and edges between cross-linked components; the edge weight is the number of cross-links. A pairwise rigid-body docking is performed between the elements connected by an edge in the maximum spanning tree of the graph. We use the docking solutions to constrain the possible moves of the components during the SA-MC sampling. The models coming from multiple SA-MC runs are improved using the DOMINO algorithm to obtain a set of models for the macromolecular assembly.

Fig. 2.

Fig. 2.

Clusters of the 100 best-scoring models for each structure in the benchmark. (A) Size of the largest cluster (asterisk on top of a bar indicates that the best-scoring model is part of the cluster). (B) Placement distance of the models in the cluster. (C) Placement angle of the models in the cluster. The error bars in B and C correspond to 1 SD. (D) Simulated density map for the native configuration of six structures in the benchmark set compared with the simulated density map generated by the 10 best-scoring solutions in the largest cluster. Each component of the assembly appears in a different color.

Fig. 3.

Fig. 3.

Model for the TfR–Tf complex. (A) Class averages used for modeling, corresponding to the 10 most populated class averages from the cryo-EM study. (B) Proximity restraints (colored ellipses) and cross-linking restraints (connected circles) used for modeling. Tf-1, first molecule of Tf (red); Tf-2, second molecule of Tf (green); TfR-A, first monomer of the receptor (gold); TfR-B, second monomer of the receptor (blue). (C) Comparison of the experimental cryo-EM density map (Left) with the simulated density map (8 Å) of the 10 best-scoring solutions in the largest cluster (Right). (D) Fitting of the best-scoring model into the experimental cryo-EM density map. The N- and C-terminal domains of Tf-1 are swapped with respect to the correct configuration (red arrow).

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