HLA09870 DQB1*06:103 HLA NFT

DQB1*06:103

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0x00
IPFS of NFT metadata: 
bafybeibw5b6hhoxmdfz7ulsofuqmwrqqvexcqrd22l33ebb7tpih3nn5he/metadata.json
IPFS of structure file: 
bafybeihinjtysuqmxhe5baghd2apgk5ne5ffhjjf66vhztmsulnlpglmoq/HLA09870_DQB1*06:103_predicted_with_MetaAI_for_GenesisL1.pdb

MOLNFT HLA

NFT id:
9639
HLA:
DQB1*06:103
Experimental Method:
Predicted structure
pTM:
0.482
pLDDT:
75.900

NFT data

MolNFT HLA is a sequential and structural database of HLA protein sequences from IPD-IMGT/HLA Database and corresponding structures predicted with ESMFold (MetaAI). Prediction done using open models by GenesisL1 blockchain enthusiasts and preserved as regular ERC-721 NFT tokens on GenesisL1 blockchain for public free use, research and entertainment.

https://www.ebi.ac.uk/ipd/imgt/hla/
Barker DJ, Maccari G, Georgiou X, Cooper MA, Flicek P, Robinson J, Marsh SGE
The IPD-IMGT/HLA Database
Nucleic Acids Research (2023) 51:D1053-60

https://github.com/facebookresearch/esm
Evolutionary-scale prediction of atomic level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Salvatore Candido, Alexander Rives
bioRxiv 2022.07.20.500902; doi:https://doi.org/10.1101/2022.07.20.500902

MOLNFT HLA09870 DQB1*06:103 research and analysis (ICN3D with VR)

FASTA sequences

>HLA:HLA09870 DQB1*06:103 269 bp MSWKKALRIPGGLRAPTVTLMLAMLSTPVAEGRDPPEDFVLQFKAMCYFTNGTERVRYVTRYIYNREEDVRFDSDVGVYRAVTPQGRPDAEYWNSQKDILERTRAELDTVCRHNYEVAFRGILQRRVEPTVTISPSRTEALNHHNLLVCSVTDFYPGQIKVRWFQNDQEETAGVVSTPLIRNGDWTFQILVMLEMTPQHGDVYTCHVEHPSLQSPITVEWRAQSESAQNKMLSGIGGFVLGLIFLGLGLIIRQRSQKGPQGPPPAGLLH