Atomic model validation using the CCP-EM software suite.
Atomic model validation using the CCP-EM software suite.
复制标题
DOI：
10.1107/s205979832101278x
复制
发表时间：
2022-02-01
期刊：
影响因子：
--
通讯作者：
Winn MD
中科院分区：
文献类型：
--
作者：
Joseph AP;Olek M;Malhotra S;Zhang P;Cowtan K;Burnley T;Winn MD
在CCP-EM软件套件的原子模型验证的实用程序进行了讨论。对SARS-CoV-2结构的评估反映了与数据一致性相比，模型改进对几何约束的强烈偏见。最近，有一个显着的改善，使用低温电子显微镜（cryo-EM）获得的数据的质量和数量。这也与构建的原子模型数量的大量增加有关。虽然可以实现的最佳分辨率正在提高，但局部分辨率往往是可变的，并且大部分数据的分辨率仍然低于3 μ m。 在这些分辨率下，模型构建和细化通常具有挑战性，因此原子模型验证对于识别模型的不太可靠的区域变得更加重要。在这里，一个图形用户界面的原子模型验证，实现在CCP-EM软件套件，提出。其目的是将其发展成为一个平台，用户可以访问多个互补的验证指标，这些指标适用于一系列解决方案，并获得评价摘要。基于与SARS-CoV-2的冷冻EM结构相关的原子模型的验证估计，观察到与模型与数据的一致性相比，模型通常倾向于采用最常见的构象而不是拟合观察结果。在低分辨率下，立体化学质量可能优于数据拟合，但应注意确保模型在可分辨特征方面与数据一致。它表明，进一步重新细化可以导致改善的协议与数据的几何质量的损失。这也突出了需要改进的分辨率依赖的权重优化模型细化和有效的测试过拟合，这将有助于指导细化过程。
Utilities for atomic model validation in the CCP-EM software suite are discussed. An assessment of SARS-CoV-2 structures reflects a strong bias in model refinement towards geometric restraints when compared with agreement with the data. Recently, there has been a dramatic improvement in the quality and quantity of data derived using cryogenic electron microscopy (cryo-EM). This is also associated with a large increase in the number of atomic models built. Although the best resolutions that are achievable are improving, often the local resolution is variable, and a significant majority of data are still resolved at resolutions worse than 3 Å. Model building and refinement is often challenging at these resolutions, and hence atomic model validation becomes even more crucial to identify less reliable regions of the model. Here, a graphical user interface for atomic model validation, implemented in the CCP-EM software suite, is presented. It is aimed to develop this into a platform where users can access multiple complementary validation metrics that work across a range of resolutions and obtain a summary of evaluations. Based on the validation estimates from atomic models associated with cryo-EM structures from SARS-CoV-2, it was observed that models typically favor adopting the most common conformations over fitting the observations when compared with the model agreement with data. At low resolutions, the stereochemical quality may be favored over data fit, but care should be taken to ensure that the model agrees with the data in terms of resolvable features. It is demonstrated that further re-refinement can lead to improvement of the agreement with data without the loss of geometric quality. This also highlights the need for improved resolution-dependent weight optimization in model refinement and an effective test for overfitting that would help to guide the refinement process.