P42675 AF NFT

Neurolysin, mitochondrial

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicq4ofumwvf7dahcoyupqgwvsmgyelwv4t5yr6o2b7wxltmk34tga/metadata.json
IPFS of structure file: 
bafybeihq6il7qrjml4psdmrju4u74ojhrrkkx5scysvwoiy4ogxnkrelbm/AF-P42675-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x25407324632f250b48F55dc1AAaAf437C879135C
First NFT owner address (bech32): 
genesis1y4q8xfrr9ujskj84thq642h5xly8jy6ukdeppe
Current NFT ID #263310 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
263310
Accession (UniProt):
P42675
Name::
Neurolysin, mitochondrial
Experimental Method:
Predicted structure
Sequence source:
ORYCTOLAGUS CUNICULUS

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 P42675 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P42675|NEUL_RABIT Neurolysin, mitochondrial OS=Oryctolagus cuniculus OX=9986 GN=NLN PE=1 SV=1 MIARCFSAVRGLHRVGGSRILFKMTLGREVMSPLQAVSSYTAAGRNVLRWDLSPEQIKTRTEELIAQTKQVYDSVGMLDIKDVTYENCLQALADVEVKYIVERTMLDFPQHVSTDREVRAASTEADKRLSRFDIEMSMREDIFQRIVHLQETCDLEKIKPEARRYLEKSVKMGRRNGLHLPEEVQNEIKSMKKRMSELCIDFNKNLNEDDTFLVFSKAELGALPDDFIDSLEKMDDDKYKITLKYPHYFPVMKKCCIPETRRRMEMAFNTRCKEENTVILQQLLPLRAQVAKLLGYSTHADFVLEMNTAKSTSRVTAFLDDLSQKLKPLGEAEREFILSLKKKECEEKGFEYDGKINAWDLHYYMTQTEELKYSIDQEFIKEYFPIEVVTEGLLNIYQELLGLSFEQVADAHVWNPSVTLYTVKDKATGEVLGQFYLDLYPREGKYNHAACFGLQPGCLLPDGSRMLSVAALVVNFSQPVAGRPSLLRHDEVRTYFHEFGHVMHQICAQTDFARFSGTNVETDFVEVPSQMLENWVWDIDSLRRLSKHYKDGNPIADDLLEKLVASRLVNTGLLTLRQIVLSKVDQSLHTNSSLDAASEYARYCTDILGVAATPGTNMPATFGHLAGGYDGQYYGYLWSEVFSMDMFYSCFKKEGIMNPEVGMKYRNLILRPGGSLDGMDMLQNFLQREPNQKAFLMSRGLQAP