Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA.
Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
Commun Biol. 2024 Oct 31;7(1):1421. doi: 10.1038/s42003-024-07045-0.
Cryo-EM particle identification from micrographs ("picking") is challenging due to the low signal-to-noise ratio and lack of ground truth for particle locations. State-of-the-art computational algorithms ("pickers") identify different particle sets, complicating the selection of the best-suited picker for a protein of interest. Here, we present REliable PIcking by Consensus (REPIC), a computational approach to identifying particles common to the output of multiple pickers. We frame consensus particle picking as a graph problem, which REPIC solves using integer linear programming. REPIC picks high-quality particles even when the best picker is not known a priori or a protein is difficult-to-pick (e.g., NOMPC ion channel). Reconstructions using consensus particles without particle filtering achieve resolutions comparable to those from particles picked by experts. Our results show that REPIC requires minimal (often no) manual intervention, and considerably reduces the burden on cryo-EM users for picker selection and particle picking. Availability: https://github.com/ccameron/REPIC .
由于信噪比低且缺乏粒子位置的真实信息，从显微镜照片中进行冷冻电镜粒子识别（“挑选”）具有挑战性。最先进的计算算法（“挑选器”）可以识别不同的粒子集，这使得选择最适合目标蛋白质的挑选器变得复杂。在这里，我们提出了基于共识的可靠挑选（REPIC），这是一种用于识别多个挑选器输出中共有的粒子的计算方法。我们将共识粒子挑选表示为一个图问题，REPIC 使用整数线性规划来解决这个问题。即使事先不知道最佳挑选器或蛋白质难以挑选（例如，NOMPC 离子通道），REPIC 也能挑选出高质量的粒子。使用共识粒子进行重构而无需粒子滤波可以达到与专家挑选的粒子相当的分辨率。我们的结果表明，REPIC 只需进行最少的（通常无需）手动干预，极大地减轻了冷冻电镜用户在挑选器和粒子挑选方面的负担。可使用性：https://github.com/ccameron/REPIC。
