Confocal micrographs of EGFP fusion proteins localized at key cell organelles in murine and human cells were acquired for use as subcellular localization landmarks. For each of the respective 789,011 and 523,319 optically validated cell images, morphology and statistical features were measured. Machine learning algorithms using these features permit automated assignment of the localization of other proteins and dyes in both cell types with very high accuracy. Automated assignment of subcellular localizations for model tail-anchored (TA) proteins with randomly mutated C-terminal targeting sequences allowed the discovery of motifs responsible for targeting to mitochondria, endoplasmic reticulum, and the late secretory pathway. Analysis of directed mutants enabled refinement of these motifs and characterization of protein distributions in within cellular subcompartments.

中文翻译：

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通过基于图像的机器学习分配蛋白质亚细胞定位的参考库

获取位于小鼠和人类细胞关键细胞器处的 EGFP 融合蛋白的共聚焦显微照片，用作亚细胞定位标志。对 789,011 和 523,319 个经过光学验证的细胞图像、形态和统计特征进行了测量。使用这些功能的机器学习算法允​​许以非常高的精度自动分配两种细胞类型中其他蛋白质和染料的定位。具有随机突变的 C 端靶向序列的模型尾锚定 (TA) 蛋白的亚细胞定位的自动分配允许发现负责靶向线粒体、内质网和晚期分泌途径的基序。对定向突变体的分析能够细化这些基序并表征细胞亚区室内的蛋白质分布。