Nhs: Network‐based hierarchical segmentation for cryo‐electron microscopy density maps

Abstract

Electron cryo-microscopy (cryo-EM) experiments yield low-resolution (3–30Å) 3D-density maps of macromolecules. These density maps are segmented to identify structurally distinct proteins, protein domains, and sub-units. Such partitioning aids the inference of protein motions and guides fitting of high-resolution atomistic structures. Cryo-EM density map segmentation has traditionally required tedious and subjective manual partitioning or semi-supervised computational methods, while validation of resulting segmentations has remained an open problem in this field. Our network-based bias-free segmentation method for cryo-EM density map segmentation, Nhs (Network-based hierarchical segmentation), provides the user with a multi-scale partitioning, reflecting local and global clustering, while requiring no user input. This approach models each map as a graph, where map voxels constitute nodes and edges connect neighboring voxels. Nhs initiates Markov diffusion (or random walk) on the weighted graph. As Markov probabilities homogenize through diffusion, an intrinsic segmentation emerges. We validate the segmentations with ground-truth maps based on atomistic models. When implemented on density maps in the 2010 Cryo-EM Modeling Challenge, Nhs efficiently and objectively partitions macromolecules into structurally and functionally relevant sub-regions at multiple scales.

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“…The edges weights are binary (either 0 or 1) denoting either the presence or absence of an edge. We then cluster this network using a hierarchical Markov diffusion framework that we have proposed previously [ 31 , 39 , 41 ]. First, we initiate a Markov chain to propagate on the network and identify a set of putative cluster centers.…”

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“…19 Nhs applies a graph-based clustering method for identifying grid points that correspond to single components in a map. 20 Other related efforts include segmentation of ribosomal proteins and RNA densities, 21 searching motif in cryo-EM maps using local correlation-based template matching. 22 Although several methods have been developed, segmentation is still a challenging problem.…”

“…There are several computational map segmentation methods developed. − Segger, which is also available as a UCSF Chimera plugin, uses the watershed method, which iteratively smoothens the density map by a Gaussian filter and then segments the map according to local maximum density regions in the map. The initially identified small segmented regions are grouped into larger domains to produce the final separations.…”

“…Then, the critical points are clustered by the marching contour algorithm . Nhs applies a graph-based clustering method for identifying grid points that correspond to single components in a map . Other related efforts include segmentation of ribosomal proteins and RNA densities, searching motif in cryo-EM maps using local correlation-based template matching .…”