The application of machine learning to cryogenic electron microscopy (cryoEM) data analysis has added a valuable set of tools to the cryoEM data processing pipeline. As these tools become more accessible and widely available, the implications of their use should be assessed. We noticed that machine learning map modification tools can have differential effects on cryoEM densities. In this perspective, we evaluate these effects to show that machine learning tools generally improve densities for biomacromolecules while generating unpredictable results for ligands. This unpredictable behavior manifests both in quantitative metrics of map quality and in qualitative investigations of modified maps. The results presented here highlight the power and potential of machine learning tools in cryoEM, while also illustrating some of the risks of their unexamined use.

中文翻译：

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冷冻电镜密度修饰的机器学习方法对生物大分子和配体密度质量有不同的影响

机器学习在低温电子显微镜 (cryoEM) 数据分析中的应用为 CryoEM 数据处理流程添加了一组有价值的工具。随着这些工具变得更加容易获得和广泛使用，应评估其使用的影响。我们注意到机器学习图修改工具可能对冷冻电镜密度产生不同的影响。从这个角度来看，我们评估这些影响，以表明机器学习工具通常可以提高生物大分子的密度，同时为配体产生不可预测的结果。这种不可预测的行为既体现在地图质量的定量指标中，也体现在对修改后的地图的定性研究中。这里提出的结果强调了机器学习工具在冷冻电镜中的力量和潜力，同时也说明了未经审查的使用它们的一些风险。