HHV-7

HHV-7 U27

Predicted structure model: AlphaFold 2.

The model was generated using AlphaFold 2.

Predicted Structure (AlphaFold 2)
Structural Homologs
Virus Protein Uniprot pTM
HSV-1 UL42 P10226 0.57
HSV-2 UL42 P89463 0.59
VZV ORF16 P09274 0.70
MDV MDV055 Q77MR9 n/a
HCMV UL44 F5HC97 0.34
RCMV E44 K7YNK0 n/a
HHV-6A U27 P52439 0.68
HHV-6B U27 Q9QJ40 0.65
HHV-7 U27 Q77Y79 0.62
EBV BMRF1 P03191 0.71
KSHV ORF59 F5HID2 0.72

This page uses Mol* for molecular visualization, with JSmol as a fallback when WebGL is unavailable.

Predictions were run with Colabfold by the Steinegger lab:
Mirdita M, Schutze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. "ColabFold: Making protein folding accessible to all". Nature Methods (2022) doi: 10.1038/s41592-022-01488-1

Foldseek is developed by the Steinegger lab:
van Kempen M, Kim S, Tumescheit C, Mirdita M, Soding J, and Steinegger M. "Fast and accurate protein structure search with Foldseek". Nature Biotechnology (2023) doi: 10.1038/s41587-023-01773-0

Alphafold2 was developed by Deepmind:
Jumper et al. "Highly accurate protein structure prediction with AlphaFold." Nature (2021) doi: 10.1038/s41586-021-03819-2

Signal peptide predictions were performed with DeepTMHMM
Jeppe Hallgren, Konstantinos D. Tsirigos, Mads Damgaard Pedersen, Jose Juan Almagro Armenteros, Paolo Marcatili, Henrik Nielsen, Anders Krogh, Ole Winther. "DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks." bioRxiv (2022) doi: 10.1101/2022.04.08.487609

AlphaFold3 models were generated through the Alphafold Server, which uses Google DeepMind's AlphaFold technology
Abramson J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature (2024). doi: 10.1038/s41586-024-07487-w