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Faraday Discuss ; 240(0): 196-209, 2022 Nov 08.
Article in English | MEDLINE | ID: covidwho-1972674


Cryogenic electron microscopy (cryo-EM) has recently been established as a powerful technique for solving macromolecular structures. Although the best resolutions achievable are improving, a significant majority of data are still resolved at resolutions worse than 3 Å, where it is non-trivial to build or fit atomic models. The map reconstructions and atomic models derived from the maps are also prone to errors accumulated through the different stages of data processing. Here, we highlight the need to evaluate both model geometry and fit to data at different resolutions. Assessment of cryo-EM structures from SARS-CoV-2 highlights a bias towards optimising the model geometry to agree with the most common conformations, compared to the agreement with data. We present the CoVal web service which provides multiple validation metrics to reflect the quality of atomic models derived from cryo-EM data of structures from SARS-CoV-2. We demonstrate that further refinement can lead to improvement of the agreement with data without the loss of geometric quality. We also discuss the recent CCP-EM developments aimed at addressing some of the current shortcomings.

COVID-19 , SARS-CoV-2 , Humans , Cryoelectron Microscopy/methods , Models, Molecular , Protein Conformation , Software
Nat Commun ; 12(1): 3399, 2021 06 07.
Article in English | MEDLINE | ID: covidwho-1260942


Structures of macromolecular assemblies derived from cryo-EM maps often contain errors that become more abundant with decreasing resolution. Despite efforts in the cryo-EM community to develop metrics for map and atomistic model validation, thus far, no specific scoring metrics have been applied systematically to assess the interface between the assembly subunits. Here, we comprehensively assessed protein-protein interfaces in macromolecular assemblies derived by cryo-EM. To this end, we developed Protein Interface-score (PI-score), a density-independent machine learning-based metric, trained using the features of protein-protein interfaces in crystal structures. We evaluated 5873 interfaces in 1053 PDB-deposited cryo-EM models (including SARS-CoV-2 complexes), as well as the models submitted to CASP13 cryo-EM targets and the EM model challenge. We further inspected the interfaces associated with low-scores and found that some of those, especially in intermediate-to-low resolution (worse than 4 Å) structures, were not captured by density-based assessment scores. A combined score incorporating PI-score and fit-to-density score showed discriminatory power, allowing our method to provide a powerful complementary assessment tool for the ever-increasing number of complexes solved by cryo-EM.

Cryoelectron Microscopy/methods , Macromolecular Substances/chemistry , Protein Interaction Domains and Motifs , Protein Interaction Mapping/methods , Protein Interaction Maps , Proteins/chemistry , Humans , Machine Learning , Macromolecular Substances/metabolism , Macromolecular Substances/ultrastructure , Models, Molecular , Neural Networks, Computer , Protein Conformation , Protein Multimerization , Proteins/metabolism , Proteins/ultrastructure , Support Vector Machine , Viral Nonstructural Proteins/chemistry , Viral Nonstructural Proteins/metabolism , Viral Nonstructural Proteins/ultrastructure