The Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ, United States.
Center for Computational Biology, Flatiron Institute, New York, NY, United States.
J Struct Biol. 2018 Nov;204(2):215-227. doi: 10.1016/j.jsb.2018.08.012. Epub 2018 Aug 19.
Particle picking is a crucial first step in the computational pipeline of single-particle cryo-electron microscopy (cryo-EM). Selecting particles from the micrographs is difficult especially for small particles with low contrast. As high-resolution reconstruction typically requires hundreds of thousands of particles, manually picking that many particles is often too time-consuming. While template-based particle picking is currently a popular approach, it may suffer from introducing manual bias into the selection process. In addition, this approach is still somewhat time-consuming. This paper presents the APPLE (Automatic Particle Picking with Low user Effort) picker, a simple and novel approach for fast, accurate, and template-free particle picking. This approach is evaluated on publicly available datasets containing micrographs of β-galactosidase, T20S proteasome, 70S ribosome and keyhole limpet hemocyanin projections.
粒子挑选是单颗粒冷冻电子显微镜（cryo-EM）计算流程的关键第一步。从显微照片中挑选粒子非常困难，特别是对于对比度低的小粒子。由于高分辨率重建通常需要数十万粒子，手动挑选那么多粒子通常太耗时。虽然基于模板的粒子挑选是目前一种很流行的方法，但它可能会在选择过程中引入人为偏见。此外，这种方法仍然有些耗时。本文提出了 APPLE（低用户投入的自动粒子挑选）挑选器，这是一种简单而新颖的快速、准确且无需模板的粒子挑选方法。该方法在包含β-半乳糖苷酶、T20S 蛋白酶体、70S 核糖体和贻贝血蓝蛋白投影的公开数据集上进行了评估。
