T2KN85 AF NFT

Beta-xylosidase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeialllphmw7ogmurrc2dtox2gmkztyqrsyvtikne53ob3mcoiicpym/metadata.json
IPFS of structure file: 
bafybeiguck4muwgo5ubdryxiyb2wekdy7aarv3ja3hwqzwk7jf7mpiguey/AF-T2KN85-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x6d550761034d809D2162B175722cf896575846D6
First NFT owner address (bech32): 
genesis1d42swcgrfkqf6gtzk96hyt8cjet4s3kknt7klx
Current NFT ID #301247 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
301247
Accession (UniProt):
T2KN85
Name::
Beta-xylosidase
Experimental Method:
Predicted structure
Sequence source:
Formosa agariphila (strain DSM 15362 / KCTC 12365 / LMG 23005 / KMM 3901 / M-2Alg 35-1)

NFT data

MolNFT AF v1 is an AlphaFold database of AI predicted Swiss-Prot protein sequences preserved as regular ERC-721 NFT tokens on GenesisL1 blockchain for public free use, research and entertainment:

https://alphafold.ebi.ac.uk/
Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. 
Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

MOLNFT T2KN85 research and analysis (ICN3D with VR)

FASTA sequences

>sp|T2KN85|PLH27_FORAG Beta-xylosidase OS=Formosa agariphila (strain DSM 15362 / KCTC 12365 / LMG 23005 / KMM 3901 / M-2Alg 35-1) OX=1347342 GN=BN863_22160 PE=1 SV=1 MYLNACRALTLISVLSLLACNSEPEKKQATPSKEIAKAQTGSWGDQGDGTYINPILNADYPDSDIEQVGDTYYMITSKQHMSPGMPILESKDMVNWTNVGHVFNSLSWAPEYNWDRMNGYSFGTWAGDLAYHEGTWYCYQIDYQHGLMVATSKDIKGPWSKPIMMLPKSEVLDDPAVFWDEDTHKAYIIINTAGKQKEASNTIEGNENRIYEMSWDGTKILDEGKLVYTGMGAEAAKIYKIDGTWYIFLAQWTMGDMSTKPGVKNPKNDRKQIVLRSKESIYGPYEVKTVLEKGTVFNNRSASQGALMQAPDNSWWYMHQLIQNDDIPFQGRPQCLEPVTWVDGWPIIGVDEDNDGIGEPVKTYKKPIDGYPVTAPRTDDDFSSPKLGFQWEWNHNPRNTHWSLTERPGWLRLKASKVLPNEKGYGPNINEWTNNDGSDSDFWRANNTLSQRIMGITTGTAVAKFDVSGMKPHQLAGFVRYGGVFNLLGVEVDEHGKKHLFYMEPMGEKTVGPEITVNDLYIRTSNRSNQAIYEYSFDGKNFKRFGPTFTIAFGKWTGDRLGLFSWNDKEDAGYIDVDWFTYDYDGPKAANQ