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.

中文翻译：

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按分类进行分割：一种新颖可靠的半自动选择 HIV/SIV 包络尖峰的方法。

我们描述了一种半自动方法，通过冷冻水合标本的冷冻电子断层扫描可视化从猿猴免疫缺陷病毒的膜包膜中分割 Env 尖峰。多变量数据分析应用于从病毒包膜中半自动提取的大量重叠子体积，并且不使用目标结构的模板。该方法中使用的主要手动步骤涉及确定六个点，这些点定义了一个近似于病毒粒子形状的椭圆体。该方法对于实际病毒体从该起始椭球体的偏离是稳健的。生成足够密度的点笼，以确保多次识别任何尖峰样蛋白质。随后将类平均值平移对齐到曲面上的圆柱参考，将具有尖峰的子体积与没有尖峰的子体积分开。包含多次识别的子卷的尖峰通过邻近分析去除。赤道和极地地区的峰值略有不同。一旦所有的尖峰都被分割，分别使用极角和自旋角进一步对齐类平均值会产生可识别的尖峰图像。我们的方法定位了 96% 的赤道尖峰和 85% 的手动识别的所有尖峰；它识别出大量因手动选择而遗漏的额外峰值。分割了两种类型的尖峰形状，一种具有类似于传统尖峰的近 3 倍对称性，另一个具有类似于当抗体（如 PG9）与 HIV Env 结合时获得的尖峰结构的 T 形。该方法应适用于从细胞或病毒体包膜延伸的任何蛋白质尖峰的分割。