Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM.
Journal
bioRxiv : the preprint server for biology
Titre abrégé: bioRxiv
Pays: United States
ID NLM: 101680187
Informations de publication
Date de publication:
29 Jun 2023
29 Jun 2023
Historique:
pubmed:
10
7
2023
medline:
10
7
2023
entrez:
10
7
2023
Statut:
epublish
Résumé
With the advent of AlphaFold, protein structure prediction has attained remarkable accuracy. These achievements resulted from a focus on single static structures. The next frontier in this field involves enhancing our ability to model conformational ensembles, not just the ground states of proteins. Notably, deposited structures result from interpretation of density maps, which are derived from either X-ray crystallography or cryogenic electron microscopy (cryo-EM). These maps represent ensemble averages, reflecting molecules in multiple conformations. Here, we present the latest developments in qFit, an automated computational approach to model protein conformational heterogeneity into density maps. We present algorithmic advancements to qFit, validated by improved R
Identifiants
pubmed: 37425870
doi: 10.1101/2023.06.28.546963
pmc: PMC10327213
pii:
doi:
Types de publication
Preprint
Langues
eng
Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM133769
Pays : United States
Organisme : NIGMS NIH HHS
ID : R35 GM145238
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM008284
Pays : United States