Abstract
Recently, there has been a dramatic improvement in the quality and quantity ofdata 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.
Highlights
There has been a rapid increase in the number of structures solved using cryogenic electron microscopy
The need for cryogenic electron microscopy (cryo-EM) map and model validation has been recognized in recent years (Afonine et al, 2018; Rosenthal & Rubinstein, 2015; Lawson et al, 2021), and the EMDR (EMDataResource) map and model challenges (Lawson et al, 2021; Lawson & Chiu, 2018) have played a very useful role in comparing existing validation metrics, identifying new requirements and providing data sets for further developments
Evaluation Global and local geometry Global and local backbone geometry Protein–protein interface quality Global fit to data Global fit to data Local fit to data Local backbone trace Secondary structure models are supplied as input, combined scores that integrate both correlation coefficient (CCC) and mutual information (MI) with extent of overlap (CCC_OV and MI_OV; described in Joseph et al, 2017) are calculated
Summary
There has been a rapid increase in the number of structures solved using cryogenic electron microscopy (cryo-EM; Callaway, 2020; Subramaniam, 2019; Kuhlbrandt, 2014). The Ramachandran Z-score (Sobolev et al, 2020; Hooft et al, 1997) is very useful for detecting an ‘unusual’ ’/ dihedral distribution in the model, which is often caused by refinement approaches that overfit the backbone ’/ angles to the centroid of allowed Ramachandran space Another set of methods evaluate each residue in the atomic model based on the local structural neighborhood (Eisenberg et al, 1997; Sippl, 1993), which is especially useful to detect errors in the sequence register. The recently introduced FSC-Q score applies normalization to the local FSC to account for local resolution variation (Ramırez-Aportela et al, 2021) Another set of metrics evaluate whether atoms in the model are positioned within the molecular contour of the map. We discuss trends from the validation of atomic models determined from SARS-CoV-2 and demonstrate the importance of model agreement with data with the help of a few examples
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
