Download files:
KSHV_K15_rank_001.pdb
KSHV_K15_rank_002.pdb
KSHV_K15_rank_003.pdb
KSHV_K15_rank_004.pdb
KSHV_K15_rank_005.pdb
KSHV_K15_coverage.png
KSHV_K15_plddt.png
KSHV_K15_pae.png

Search for similar structures using Foldseek:
Foldseek KSHV_K15_rank_001.pdb
Foldseek KSHV_K15_rank_002.pdb
Foldseek KSHV_K15_rank_003.pdb
Foldseek KSHV_K15_rank_004.pdb
Foldseek KSHV_K15_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

Download files:
KSHV_K4.2_rank_001.pdb
KSHV_K4.2_rank_002.pdb
KSHV_K4.2_rank_003.pdb
KSHV_K4.2_rank_004.pdb
KSHV_K4.2_rank_005.pdb
KSHV_K4.2_coverage.png
KSHV_K4.2_plddt.png
KSHV_K4.2_pae.png

Search for similar structures using Foldseek:
Foldseek KSHV_K4.2_rank_001.pdb
Foldseek KSHV_K4.2_rank_002.pdb
Foldseek KSHV_K4.2_rank_003.pdb
Foldseek KSHV_K4.2_rank_004.pdb
Foldseek KSHV_K4.2_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

Download files:
KSHV_K7_rank_001.pdb
KSHV_K7_rank_002.pdb
KSHV_K7_rank_003.pdb
KSHV_K7_rank_004.pdb
KSHV_K7_rank_005.pdb
KSHV_K7_coverage.png
KSHV_K7_plddt.png
KSHV_K7_pae.png

KSHV_K7-nosignal_rank_001.pdb
KSHV_K7-nosignal_rank_002.pdb
KSHV_K7-nosignal_rank_003.pdb
KSHV_K7-nosignal_rank_004.pdb
KSHV_K7-nosignal_rank_005.pdb

Search for similar structures using Foldseek:
Foldseek KSHV_K7_rank_001.pdb
Foldseek KSHV_K7_rank_002.pdb
Foldseek KSHV_K7_rank_003.pdb
Foldseek KSHV_K7_rank_004.pdb
Foldseek KSHV_K7_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

Download files:
HSV-1_RL2_rank_001.pdb
HSV-1_RL2_rank_002.pdb
HSV-1_RL2_rank_003.pdb
HSV-1_RL2_rank_004.pdb
HSV-1_RL2_rank_005.pdb
HSV-1_RL2_coverage.png
HSV-1_RL2_plddt.png
HSV-1_RL2_pae.png

Search for similar structures using Foldseek:
Foldseek HSV-1_RL2_rank_001.pdb
Foldseek HSV-1_RL2_rank_002.pdb
Foldseek HSV-1_RL2_rank_003.pdb
Foldseek HSV-1_RL2_rank_004.pdb
Foldseek HSV-1_RL2_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

Download files:
KSHV_ORF55_rank_001.pdb
KSHV_ORF55_rank_002.pdb
KSHV_ORF55_rank_003.pdb
KSHV_ORF55_rank_004.pdb
KSHV_ORF55_rank_005.pdb
KSHV_ORF55_coverage.png
KSHV_ORF55_plddt.png
KSHV_ORF55_pae.png

Search for similar structures using Foldseek:
Foldseek KSHV_ORF55_rank_001.pdb
Foldseek KSHV_ORF55_rank_002.pdb
Foldseek KSHV_ORF55_rank_003.pdb
Foldseek KSHV_ORF55_rank_004.pdb
Foldseek KSHV_ORF55_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

Download files:
HSV-1_UL26_pAP_rank_001.pdb
HSV-1_UL26_pAP_rank_002.pdb
HSV-1_UL26_pAP_rank_003.pdb
HSV-1_UL26_pAP_rank_004.pdb
HSV-1_UL26_pAP_rank_005.pdb
HSV-1_UL26_pAP_coverage.png
HSV-1_UL26_pAP_plddt.png
HSV-1_UL26_pAP_pae.png

Search for similar structures using Foldseek:
Foldseek HSV-1_UL26_pAP_rank_001.pdb
Foldseek HSV-1_UL26_pAP_rank_002.pdb
Foldseek HSV-1_UL26_pAP_rank_003.pdb
Foldseek HSV-1_UL26_pAP_rank_004.pdb
Foldseek HSV-1_UL26_pAP_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

Download files:
KSHV_ORF65_rank_001.pdb
KSHV_ORF65_rank_002.pdb
KSHV_ORF65_rank_003.pdb
KSHV_ORF65_rank_004.pdb
KSHV_ORF65_rank_005.pdb
KSHV_ORF65_coverage.png
KSHV_ORF65_plddt.png
KSHV_ORF65_pae.png

Search for similar structures using Foldseek:
Foldseek KSHV_ORF65_rank_001.pdb
Foldseek KSHV_ORF65_rank_002.pdb
Foldseek KSHV_ORF65_rank_003.pdb
Foldseek KSHV_ORF65_rank_004.pdb
Foldseek KSHV_ORF65_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

Download files:
HSV-1_US1_US1.5_rank_001.pdb
HSV-1_US1_US1.5_rank_002.pdb
HSV-1_US1_US1.5_rank_003.pdb
HSV-1_US1_US1.5_rank_004.pdb
HSV-1_US1_US1.5_rank_005.pdb
HSV-1_US1_US1.5_coverage.png
HSV-1_US1_US1.5_plddt.png
HSV-1_US1_US1.5_pae.png

Search for similar structures using Foldseek:
Foldseek HSV-1_US1_US1.5_rank_001.pdb
Foldseek HSV-1_US1_US1.5_rank_002.pdb
Foldseek HSV-1_US1_US1.5_rank_003.pdb
Foldseek HSV-1_US1_US1.5_rank_004.pdb
Foldseek HSV-1_US1_US1.5_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

Download files:
VZV_ORF0_rank_001.pdb
VZV_ORF0_rank_002.pdb
VZV_ORF0_rank_003.pdb
VZV_ORF0_rank_004.pdb
VZV_ORF0_rank_005.pdb
VZV_ORF0_coverage.png
VZV_ORF0_plddt.png
VZV_ORF0_pae.png

Search for similar structures using Foldseek:
Foldseek VZV_ORF0_rank_001.pdb
Foldseek VZV_ORF0_rank_002.pdb
Foldseek VZV_ORF0_rank_003.pdb
Foldseek VZV_ORF0_rank_004.pdb
Foldseek VZV_ORF0_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

Download files:
HSV-1_US1_rank_001.pdb
HSV-1_US1_rank_002.pdb
HSV-1_US1_rank_003.pdb
HSV-1_US1_rank_004.pdb
HSV-1_US1_rank_005.pdb
HSV-1_US1_coverage.png
HSV-1_US1_plddt.png
HSV-1_US1_pae.png

Search for similar structures using Foldseek:
Foldseek HSV-1_US1_rank_001.pdb
Foldseek HSV-1_US1_rank_002.pdb
Foldseek HSV-1_US1_rank_003.pdb
Foldseek HSV-1_US1_rank_004.pdb
Foldseek HSV-1_US1_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