D4ANG0 AF NFT

Probable tripeptidyl-peptidase SED2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeif6ve3m5mnu6hsw443b227am3lcgxw6klnm6qcwr7vpx3lelybbuy/metadata.json
IPFS of structure file: 
bafybeidplmohmw3mistiq7tiacpz7z3xio24y5hq4y4lv7tjexg4t5pari/AF-D4ANG0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7FDF8b382C6a36A8772D1A265E8e480c7DF0A9C1
First NFT owner address (bech32): 
genesis10l0ckwpvdgm2saedrgn9arjgp37lp2wpyzq5qn
Current NFT ID #425162 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
425162
Accession (UniProt):
D4ANG0
Name::
Probable tripeptidyl-peptidase SED2
Experimental Method:
Predicted structure
Sequence source:
Arthroderma benhamiae (strain ATCC MYA-4681 / CBS 112371)

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 D4ANG0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|D4ANG0|SED2_ARTBC Probable tripeptidyl-peptidase SED2 OS=Arthroderma benhamiae (strain ATCC MYA-4681 / CBS 112371) OX=663331 GN=SED2 PE=1 SV=1 MRLLKFVCLLASVAAAKPTPGASHKVIEHLDFVPEGWQMVGAADPAAIIDFWLAIERENPEKLYDTIYDVSTPGRAQYGKHLKREELDDLLRPRAETSESIINWLTNGGVNPQHIRDEGDWVRFSTNVKTAETLMNTRFNVFKDNLNSVSKIRTLEYSVPVAISAHVQMIQPTTLFGRQKPQNSLILNPLTKDLESMSVEEFAASQCRSLVTTACLRELYGLGDRVTQARDDNRIGVSGFLEEYAQYRDLELFLSRFEPSAKGFNFSEGLIAGGKNTQGGPGSSTEANLDMQYVVGLSHKAKVTYYSTAGRGPLIPDLSQPSQASNNNEPYLEQLRYLVKLPKNQLPSVLTTSYGDTEQSLPASYTKATCDLFAQLGTMGVSVIFSSGDTGPGSSCQTNDGKNATRFNPIYPASCPFVTSIGGTVGTGPERAVSFSSGGFSDRFPRPQYQDNAVKDYLKILGNQWSGLFDPNGRAFPDIAAQGSNYAVYDKGRMTGVSGTSASAPAMAAIIAQLNDFRLAKGSPVLGFLNPWIYSKGFSGFTDIVDGGSRGCTGYDIYSGLKAKKVPYASWNATKGWDPVTGFGTPNFQALTKVLP