P97562 AF NFT

Peroxisomal acyl-coenzyme A oxidase 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicqim6shp7np4wymzxod7eatwbispsk2evgtftw4hcc2qrvm4insm/metadata.json
IPFS of structure file: 
bafybeickfivc5we3siwlc2svd45gf7uonkbixajvq3erpypyax7wwnyv7y/AF-P97562-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xc2C3F148eB26FE91165F4EE55FE4D2E3738B0950
First NFT owner address (bech32): 
genesis1ctplzj8tymlfz9jlfmj4lexjudeckz2s0hu5hx
Current NFT ID #6557 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
6557
Accession (UniProt):
P97562
Name::
Peroxisomal acyl-coenzyme A oxidase 2
Experimental Method:
Predicted structure
Sequence source:
RATTUS NORVEGICUS

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 P97562 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P97562|ACOX2_RAT Peroxisomal acyl-coenzyme A oxidase 2 OS=Rattus norvegicus OX=10116 GN=Acox2 PE=1 SV=1 MGSPMHRVSLGDHWSWQVHPDIDSERHSPSFSVERLTNILDGGLPNTVLRRKVESIIQSDPVFNLKKLYFMTREELYEDAIQKRFHLEKLAWSLGWSEDGPERIYANRVLDGNVNLSLHGVAMNAIRSLGSDEQIAKWGQLCKNFQIITTYAQTELGHGTYLQGLETEATYDEARQELVIHSPTMTSTKWWPGDLGWSVTHAVVLAQLTCLGVRHGMHAFIVPIRSLEDHTPLPGITVGDIGPKMGLEHIDNGFLQLNHVRVPRENMLSRFAEVLPDGTYQRLGTPQSNYLGMLVTRVQLLCKGILPSLQKACIIATRYSVIRHQSRLRPSDPEAKILEYQTQQQKLLPQLAVSYAFHFTATSLSEFFHSSYSAILKRDFSLLPELHALSTGMKATFADFCAQGAEICRRACGGHGYSKLSGLPTLVARATASCTYEGENTVLYLQVARFLMKSYLQAQASPGATPQKPLPQSVMYIATQRPARCSAQTAADFRCPDVYTTAWAYVSTRLIRDAAHRTQTLMKSGVDQHDAWNQTTVIHLQAAKAHCYFITVKNFKEAVEKLDKEPEIQRVLQRLCDLYALHGVLTNSGDFLHDGFLSGAQVDMAREAFLDLLPLIRKDAILLTDAFDFSDHCLNSALGCYDGHVYERLFEWAQKYPANTQENPAYKKYIRPLMLGWRHKM