Integrating Molecular Models Into CryoEM Heterogeneity Analysis Using Scalable High-resolution Deep Gaussian Mixture Models.
CryoEM
Gaussian mixture model
deep neural networks
single particle analysis
structure heterogeneity
Journal
Journal of molecular biology
ISSN: 1089-8638
Titre abrégé: J Mol Biol
Pays: Netherlands
ID NLM: 2985088R
Informations de publication
Date de publication:
01 05 2023
01 05 2023
Historique:
received:
19
10
2022
revised:
07
02
2023
accepted:
08
02
2023
pmc-release:
01
05
2024
medline:
8
5
2023
pubmed:
23
2
2023
entrez:
22
2
2023
Statut:
ppublish
Résumé
Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we introduce an improved computational method that uses Gaussian mixture models for protein structure representation and deep neural networks for conformation space embedding. By integrating information from molecular models into the heterogeneity analysis, we can analyze continuous protein conformational changes using structural information at the frequency of 1/3 Å
Identifiants
pubmed: 36806476
pii: S0022-2836(23)00070-0
doi: 10.1016/j.jmb.2023.168014
pmc: PMC10164680
mid: NIHMS1876530
pii:
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
168014Subventions
Organisme : NIMH NIH HHS
ID : R21 MH125285
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM080139
Pays : United States
Organisme : NIGMS NIH HHS
ID : R01 GM136780
Pays : United States
Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Références
J Chem Inf Model. 2020 May 26;60(5):2484-2491
pubmed: 32207941
Science. 2021 Nov 05;374(6568):729-734
pubmed: 34735239
Biomolecules. 2022 Jun 02;12(6):
pubmed: 35740898
Nat Methods. 2020 Mar;17(3):328-334
pubmed: 32042190
Nat Commun. 2021 Jan 4;12(1):42
pubmed: 33397925
Sci Rep. 2020 Mar 9;10(1):4282
pubmed: 32152330
J Mol Biol. 2022 Apr 15;434(7):167483
pubmed: 35150654
Cell. 2016 Sep 22;167(1):145-157.e17
pubmed: 27662087
Nat Struct Mol Biol. 2022 Sep;29(9):942-953
pubmed: 36097293
Nature. 2017 Jun 29;546(7660):617-621
pubmed: 28530653
Front Mol Biosci. 2022 Sep 08;9:965645
pubmed: 36158571
J Comput Chem. 2004 Oct;25(13):1605-12
pubmed: 15264254
Acta Crystallogr D Struct Biol. 2021 Sep 1;77(Pt 9):1142-1152
pubmed: 34473085
Nature. 2019 Jan;565(7737):49-55
pubmed: 30479383
Nature. 2016 May 18;534(7607):347-51
pubmed: 27281200
Nat Methods. 2021 Feb;18(2):176-185
pubmed: 33542510
Int J Mol Sci. 2022 Aug 09;23(16):
pubmed: 36012133
J Struct Biol. 2021 Jun;213(2):107702
pubmed: 33582281
Biophys J. 2016 Apr 26;110(8):1753-1765
pubmed: 27119636
J Struct Biol. 2016 Dec;196(3):289-298
pubmed: 27436409
J Mol Biol. 2023 May 1;435(9):168020
pubmed: 36863660
IEEE Trans Comput Imaging. 2022;8:462-478
pubmed: 36258699
Nat Methods. 2021 Aug;18(8):930-936
pubmed: 34326541
Nature. 2015 Nov 19;527(7578):336-41
pubmed: 26458101
Methods. 2016 May 1;100:25-34
pubmed: 26931650
Nature. 2020 Jan;577(7790):432-436
pubmed: 31915381