Assessment of machine-learning predictions for the Mediator complex subunit MED25 ACID domain interactions with transactivation domains.
AlphaFold
machine learning
mediator complex
transactivation domain
Journal
FEBS letters
ISSN: 1873-3468
Titre abrégé: FEBS Lett
Pays: England
ID NLM: 0155157
Informations de publication
Date de publication:
04 Mar 2024
04 Mar 2024
Historique:
revised:
01
02
2024
received:
30
11
2023
accepted:
10
02
2024
medline:
4
3
2024
pubmed:
4
3
2024
entrez:
4
3
2024
Statut:
aheadofprint
Résumé
The human Mediator complex subunit MED25 binds transactivation domains (TADs) present in various cellular and viral proteins using two binding interfaces, named H1 and H2, which are found on opposite sides of its ACID domain. Here, we use and compare deep learning methods to characterize human MED25-TAD interfaces and assess the predicted models to published experimental data. For the H1 interface, AlphaFold produces predictions with high-reliability scores that agree well with experimental data, while the H2 interface predictions appear inconsistent, preventing reliable binding modes. Despite these limitations, we experimentally assess the validity of MED25 interface predictions with the viral transcriptional activators Lana-1 and IE62. AlphaFold predictions also suggest the existence of a unique hydrophobic pocket for the Arabidopsis MED25 ACID domain.
Identifiants
pubmed: 38436147
doi: 10.1002/1873-3468.14837
doi:
Banques de données
RefSeq
['X04370.1']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024 Federation of European Biochemical Societies.
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