We describe a semiautomated approach to segment Env spikes from the membrane envelope of Simian Immunodeficiency Virus visualized by cryoelectron tomography of frozen-hydrated specimens. Multivariate data analysis is applied to a large set of overlapping subvolumes extracted semiautomatically from the viral envelope and does not utilize a template of the target structure. The major manual step used in the method involves determination of six points that define an ellipsoid approximating the virion shape. The approach is robust to departures of the actual virion from this starting ellipsoid. A point cage of sufficient density is generated to ensure that any spike-like protein is identified multiple times. Subsequently translational alignment of class averages to a cylindrical reference on a curved surface separates subvolumes with spikes from those without. Spike containing subvolumes identified multiple times are removed by proximity analysis. Slightly different procedures segment spikes in the equatorial and the polar regions. Once all spikes are segmented, further alignment of class averages using separately the polar and spin angles produces recognizable spike images. Our approach localized 96% of the equatorial spikes and 85% of all spikes identified manually; it identifies a significant number of additional spikes missed by manual selection. Two types of spike shapes were segmented, one with near 3-fold symmetry resembling the conventional spike, the other had a T-shape resembling the spike structure obtained when antibodies such as PG9 bind to HIV Env. The approach should be applicable to segmentation of any protein spikes extending from a cellular or virion envelope.

Myosin-based motility utilizes catalysis of ATP to drive the relative sliding of F-actin and myosin. The earliest detailed model based on cryo-electron microscopy (cryoEM) and X-ray crystallography postulated that higher actin affinity and lever arm movement were coupled to closure of a feature of the myosin head dubbed the actin-binding cleft. Several studies since then using crystallography of myosin-V and cryoEM structures of F-actin bound myosin-I,-II and-V have provided details of this model. The smooth muscle myosin II interaction with F-actin may differ from those for striated and non-muscle myosin II due in part to different lengths of important surface loops. Here we report a similar to 6 A resolution reconstruction of F-actin decorated with the nucleotide-free recombinant smooth muscle myosin-II motor domain (MD) from images recorded using a direct electron detector. Resolution is highest for F-actin and the actin-myosin interface (3.5-4 A) and lowest (similar to 6-7 A) for those parts of the MD at the highest radius. Atomic models built into the F-actin density are quite comparable to those previously reported for rabbit muscle actin and show density from the bound ADP. The atomic model of the MD, is quite similar to a recently published structure of vertebrate non-muscle myosin n bound to F-actin and a crystal structure of nucleotide free myosin-V. Larger differences are observed when compared to the cryoEM structure of F-actin decorated with rabbit skeletal muscle myosin subfragment 1. The differences suggest less closure of the 50 kDa domain in the actin bound skeletal muscle myosin structure.

Nano-scale imaging technologies make it possible to visualize objects at nanometer resolutions. To investigate structures and functions of interest, there is an intrinsic demand for explicit models to extract them from nano-scale data. Segmentation is one of the most critical steps in processing pipelines. However, existing segmentation methods often fail due to extremely low signal-to-noise ratio, low contrast and large data size. In this paper we propose a new context-sensitive method for segmenting three-dimensional volumes. As our method efficiently narrows the search space by using robust context cues, we achieve tractable and reliable nano-scale semantic segmentation. We demonstrate our method on a tomogram of microvilli spikes, for which our method is able to yield accurate spike segmentation and in comparison the state-of-the-art semantic segmentation methods fail due to their inability to handle signal-to-noise ratio and low contrast volumes.