Download files:
EBV_BARF1_rank_001.pdb
EBV_BARF1_rank_002.pdb
EBV_BARF1_rank_003.pdb
EBV_BARF1_rank_004.pdb
EBV_BARF1_rank_005.pdb
EBV_BARF1_coverage.png
EBV_BARF1_plddt.png
EBV_BARF1_pae.png

EBV_BARF1-nosignal_rank_001.pdb
EBV_BARF1-nosignal_rank_002.pdb
EBV_BARF1-nosignal_rank_003.pdb
EBV_BARF1-nosignal_rank_004.pdb
EBV_BARF1-nosignal_rank_005.pdb

Search for similar structures using Foldseek:
Foldseek EBV_BARF1_rank_001.pdb
Foldseek EBV_BARF1_rank_002.pdb
Foldseek EBV_BARF1_rank_003.pdb
Foldseek EBV_BARF1_rank_004.pdb
Foldseek EBV_BARF1_rank_005.pdb

This page uses 3Dmol.js: Molecular visualization with WebGL by Nicholas Rego and David Koes.
Bioinformatics (2015) doi: 10.1093/bioinformatics/btu829

Predictions were run with Colabfold by the Steinegger lab:
Mirdita M, Schütze 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, Söding J, and Steinegger M. “Foldseek: fast and accurate protein structure search”.
bioRxiv (2022) doi: 10.1101/2022.02.07.479398

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, José 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).
Nature (2021) doi: 10.1038/s41586-024-07487-w