Atomic model validation using the CCP-EM software suite.

COVID-19 Cryoelectron Microscopy / methods Image Processing, Computer-Assisted Models, Molecular Reproducibility of Results Software Software Validation User-Computer Interface
CCP-EM SARS-CoV-2 cryo-EM model geometry model validation

Journal

Acta crystallographica. Section D, Structural biology
ISSN: 2059-7983
Titre abrégé: Acta Crystallogr D Struct Biol
Pays: United States
ID NLM: 101676043

Informations de publication

Date de publication:
01 Feb 2022
Historique:
received: 29 06 2021
accepted: 01 12 2021
entrez: 1 2 2022
pubmed: 2 2 2022
medline: 4 2 2022
Statut: ppublish

Résumé

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.

Identifiants

DOI: 10.1107/S205979832101278X PMID: 35102881 PMC: PMC8805302
pubmed: 35102881
pii: S205979832101278X
doi: 10.1107/S205979832101278X
pmc: PMC8805302
doi:

Types de publication

Journal Article Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

152-161

Subventions

Organisme : Medical Research Council
ID : MR/V000403/1)
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N009614/1, MR/V000403/1
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/T012935/1
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 208398/Z/17/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V000403/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/N009614/1
Pays : United Kingdom

Informations de copyright

open access.

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Auteurs

Agnel Praveen Joseph (AP)

Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom.

Mateusz Olek (M)

Department of Chemistry, University of York, York, United Kingdom.

Sony Malhotra (S)

Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom.

Peijun Zhang (P)

Electron BioImaging Center, Diamond Light Source, Rutherford Appleton Laboratory, Didcot, United Kingdom.

Kevin Cowtan (K)

Department of Chemistry, University of York, York, United Kingdom.
ORCID: 0000-0002-0189-1437

Tom Burnley (T)

Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom.
ORCID: 0000-0001-5307-348X

Martyn D Winn (MD)

Scientific Computing Department, Science and Technology Facilities Council, Didcot, United Kingdom.
ORCID: 0000-0003-0496-6796

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