低温电子显微镜(cryo-EM)单粒子分析需要成千上万的粒子投影才能揭示大分子复合物的结构信息。然而,由于低信噪比和高对比度伪影和污染物在显微照片的存在,半自动和全自动粒子拾取算法往往会出现高假阳性率,这降低了结构确定的置信度。在这项研究中,我们引入PickerOptimizer(PO),基于迁移学习的分类神经网络,用于低温EM中的粒子修剪,作为补充当前自动粒子拾取算法的附加策略。为了以最少的人为干预实现高分类性能,我们采用了两个关键策略:(1)利用迁移学习技术训练卷积神经网络,其中从公共分类数据集获得的知识应用于低温EM领域。(2)设计多损失策略,多个损失函数的组合,来指导网络参数的优化。为了减少预训练的低温EM图像和自然图像之间的域偏移,我们为cryo-EM构建了第一个图像分类数据集,其中包含从EMPIAR条目中收集的阳性和阴性样本。PO在14个公共实验数据集上进行了测试,在大多数情况下,达到95%以上的准确性和F1分数。此外,提供了三个案例研究,通过在有问题的粒子选择上应用PO来验证模型性能,表明与其他粒子修剪策略相比,我们的算法获得了更好或可比的性能。
The cryo-electron microscopy (cryo-EM) single-particle analysis requires tens of thousands of particle projections to reveal structural information of macromolecular complexes. However, due to the low signal-to-noise ratio and the presence of high contrast artifacts and contaminants in the micrographs, the semiautomatic and fully automatic particle picking algorithms tend to suffer from high false-positive rates, which degrades the confidence of structure determination. In this study, we introduce PickerOptimizer (PO), a transfer learning-based classification neural network for particle pruning in cryo-EM, as an additional strategy to complement the current automated particle picking algorithms. To achieve high classification performance with minimal human intervention, we adopted two key strategies: (1) utilizing the transfer learning techniques to train the convolutional neural network, where the knowledge gained from public classification datasets is applied to the field of cryo-EM. (2) Designing a multiloss strategy, a combination of multiple loss functions, to guide the optimization of the network parameters. To reduce the domain shift between cryo-EM images and natural images for pretraining, we build the first image classification dataset for cryo-EM, which contains positive and negative samples collected from EMPIAR entries. The PO is tested on 14 public experimental datasets, achieving accuracy and F1 scores above 95% in most cases. Furthermore, three case studies are provided to verify the model performance by applying PO on problematic particle selections, showing that our algorithm achieved better or comparable performance compared with other particle pruning strategies.