Q28DV7 AF NFT

Mini-chromosome maintenance complex-binding protein

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifd55qhyzjtjltclahnoffosmbxxyuoatrxlflfslq3zhtffxxf5m/metadata.json
IPFS of structure file: 
bafybeid4ax6jui6tnhxzp7fukkga3xksaq4eh7tvd4vaeexkuolze4vlny/AF-Q28DV7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x33DDd87D6476a8e5277F2A859860A0611e9762aA
First NFT owner address (bech32): 
genesis1x0wasltyw65w2fml92zesc9qvy0fwc42ve0shj
Current NFT ID #226505 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
226505
Accession (UniProt):
Q28DV7
Name::
Mini-chromosome maintenance complex-binding protein
Experimental Method:
Predicted structure
Sequence source:
XENOPUS TROPICALIS

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 Q28DV7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q28DV7|MCMBP_XENTR Mini-chromosome maintenance complex-binding protein OS=Xenopus tropicalis OX=8364 GN=mcmbp PE=2 SV=2 MTGGESWISDPLGVVQGLFAAQRPADGDWQKNVTDYFSAKLRDSDSAAAVPCLNDTPLHYLKPNSLVRFRCMVQDMFDPEFYMGVYETVDTNTSARVLHFGKYRDVAESLPQQEVDPDSRRNVTCERQTFYCVPVPGECPWVKDAYTSCSQAKVCASTSYAPSRQKRSYEEDEDAAPCDTRASHGLGEPKRLETEAAGAQQHLPSSCSSAPDLNFPLPGEKGPACLIKVYEGWDLFKVNDVLEVYGILSVDPALSALTEDREAVSALLDPSDDMDTLEEHRAHCPPASLVPRIHTIVTRKLQHNNPLLPPSLQGSEESKLFVSNFLSELSAVRAELLDFLSHALLGDSLAAEYLILHLISTVYARRDVLPLGKFTLNLSGCPRNGTFSELLYRVVQQLVPAAHYLPMTIENMNKLCFIPRKDYNANRLVSGLLQLSPQTSLLVDETLLEQGQLDTTGVHNVTALGNLITWQKVDYDFSYHRMEFPCNINVLVTSEGRSLLPSDCHVHLQPQMMPPNLEQYLGALLAASLPSLLNKFRTYIGLLRLLDYSISDEATKAVEDDFVEMRKNDPQSISADDLHRLLVVARLSSLSAGQTSLSREQWLRAKQLEQQRKNRLREQKHMNVNEL