Q65HI9 AF NFT

Exodeoxyribonuclease 7 large subunit

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigmck5bi5mswjvhs6uwe5ytgm2vo5i235jcynxd3r76rlypq2dgim/metadata.json
IPFS of structure file: 
bafybeic5ndfykgqi37jmxld4vvrlacb5lplkhocn232ctz7vanp2qpl4zy/AF-Q65HI9-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xF9712C8b39bAA978803c4F86751462468AC3FD98
First NFT owner address (bech32): 
genesis1l9cjezeeh25h3qpuf7r829rzg69v8lvcvg9zyn
Current NFT ID #121456 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
121456
Accession (UniProt):
Q65HI9
Name::
Exodeoxyribonuclease 7 large subunit
Experimental Method:
Predicted structure
Sequence source:
Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46)

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 Q65HI9 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q65HI9|EX7L_BACLD Exodeoxyribonuclease 7 large subunit OS=Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46) OX=279010 GN=xseA PE=3 SV=1 MSEISFVTVTALTKYIKRKFDVDPHLEDIWIKGELSNVKIHSRGHIYFTLKDDNARIQSVMFARQNRNLAFTPENGMKVLVRGGISVYEPSGSYQLYAKEIQPDGIGALHLAYEELKKKLSQEGLFDEKYKKPIPAFPSVVGVVTSPTGAAVRDVITTIKRRYPLVKVIVLPTLVQGVNAGESIVSSIEEANRRDLCDVLIVGRGGGSIEELWAFNEEAVARAIFSSDIPIISAVGHETDFTISDFVADLRAATPTGAAELAVPNMLDLLERLKTLEARMTKSMQQDLKKRQERVKNLQSSYAFRYPKRLYAQKEQEFDLIFDRFQKQLAMTVDRKKQKLNEQTYKLEMLHPEEQLKQARKRHEEQTVRLKRNMKVQLKQIHSQFQTVLGKLNALSPLQVMERGYSLTYKENELIKSIEQVHEKDEIRVTLNDGTLTCEVLQKRGETNG