Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps.
cryo-EM
cryo-electron microscopy
de novo modeling
density map
machine learning methods
protein modeling
structure fitting algorithms
Journal
Molecules (Basel, Switzerland)
ISSN: 1420-3049
Titre abrégé: Molecules
Pays: Switzerland
ID NLM: 100964009
Informations de publication
Date de publication:
24 12 2019
24 12 2019
Historique:
received:
11
12
2019
revised:
20
12
2019
accepted:
20
12
2019
entrez:
28
12
2019
pubmed:
28
12
2019
medline:
17
6
2020
Statut:
epublish
Résumé
Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.
Identifiants
pubmed: 31878333
pii: molecules25010082
doi: 10.3390/molecules25010082
pmc: PMC6982917
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : National Science Foundation
ID : MCB1925643
Pays : International
Organisme : NIGMS NIH HHS
ID : R01 GM123055
Pays : United States
Organisme : NIH HHS
ID : R01GM123055
Pays : United States
Organisme : National Science Foundation
ID : DMS1614777
Pays : International
Organisme : National Science Foundation
ID : CMMI1825941
Pays : International
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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