O87278 AF NFT

Probable N-methylproline demethylase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeid2j5qfcnoskj5zg3ee4kohp2v6obniizdopmgm5gs6egam6ohcqm/metadata.json
IPFS of structure file: 
bafybeiazcp3iveffu3ovjlhtqfu5o767cvzov2wizasuoasvq3nsvjh2hi/AF-O87278-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x40B9C9d5F084D34d026E9d5Dd10501AcdF6d6152
First NFT owner address (bech32): 
genesis1gzuun40ssnf56qnwn4wazpgp4n0k6c2jdg88yn
Current NFT ID #439353 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
439353
Accession (UniProt):
O87278
Name::
Probable N-methylproline demethylase
Experimental Method:
Predicted structure
Sequence source:
RHIZOBIUM MELILOTI (STRAIN 1021)

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 O87278 research and analysis (ICN3D with VR)

FASTA sequences

>sp|O87278|STCD_RHIME Probable N-methylproline demethylase OS=Rhizobium meliloti (strain 1021) OX=266834 GN=stcD PE=2 SV=2 MPNDPLLQPYQLKHLTLRNRIIVTAHEPAYPEDGMPKERYRAYTVERARGGVAMTMTAGSAAVSKDSPPVFNNLLAYRDEIVPWIREMTDAVHEEGAVIMIQLTHLGRRTRWDKGDWLPVVAPSHHREAAHRAFPKKIEDWDIDRIIKDFADAAERMKAGGMDGVELEAYGHLIDQFASPLTNELDGPYGGSLDNRMRFCFDVLKAIRARVGDEFILGVRYTADECLPGGTDKAEGLEISKRLKESGLIDYLNIIRGHIDTDPGLTDVIPIQGMANSPHLDFAGEIRAATNFPTFHAAKIPDVATARHAIASGKVDMVGMTRAHMTDPHIVRKIIEKREEDIRPCVGANYCLDRIYQGGAAYCIHNAATGRELTMPHSIAKAHCRRKVVVVGTGPAGLEAARVAGERGHEVIVFEAASDPGGQVRLTAQSPRRREMISIIDWRMSQCEKLGVTFHFNTWAEAEAIQAESPDVVIIATGGLPHTEVLSRGNELVVSAWDIISGDAKPGTNVLIFDDAGDHAALQAAEFLATAGARVEIMTPDRSFAPEVMAMNLVPYMRCLQKLDVTFTVTYRLEAVEKSGNELVAHVGSDYGGISKQRTFDQVVVNHGTIPLDELYFELKPFSSNLGEIAHDQMIAGEPQSVVRNAEGKFQLFRIGDAVAARNTHAAIYDALRLLKDI