Q3ITF7 AF NFT

5-methylthioadenosine/S-adenosylhomocysteine deaminase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeibaqnhevgucy5wuvqgdadvgjxej2736gn3l2v3up7caog2rjbta34/metadata.json
IPFS of structure file: 
bafybeiao5n4daz3e7i5hr4eeyqch7ckkqivj43db34byuhur54zsd76n4y/AF-Q3ITF7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x3F68E74Da58A02eCfd34cED79740EF24407440ed
First NFT owner address (bech32): 
genesis18a5wwnd93gpwelf5emtews80y3q8gs8dy5akj4
Current NFT ID #246708 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
246708
Accession (UniProt):
Q3ITF7
Name::
5-methylthioadenosine/S-adenosylhomocysteine deaminase
Experimental Method:
Predicted structure
Sequence source:
Natronomonas pharaonis (strain ATCC 35678 / DSM 2160 / CIP 103997 / JCM 8858 / NBRC 14720 / NCIMB 2260 / Gabara)

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 Q3ITF7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q3ITF7|MTAD_NATPD 5-methylthioadenosine/S-adenosylhomocysteine deaminase OS=Natronomonas pharaonis (strain ATCC 35678 / DSM 2160 / CIP 103997 / JCM 8858 / NBRC 14720 / NCIMB 2260 / Gabara) OX=348780 GN=mtaD PE=3 SV=1 MSTLQITGGQVLTPELTVRTADVLADSETGEILAVGDTAAGDRTLDAEGCLVVPGLVNTHCHAAMTLLRGYADDKPLDRWLQEDIWPVEAELTPEDIRAGTRLGLVELLKNGVTAVGDMYFEVPEVAAAVEEAGIRARLGHGIVTVGKDEADARADFEEGLAVARELDGAADGRVRTALMPHSLTTADPDLIAEFVPRARDAGVPIHYHANETTDEVDPIVDERGVRPLEFADELGLLDEGDFIAHGVHVDETEIELLAERGVGVAHCPASNMKLASGIAPVQEFLDAGVTVGIGTDGPASNNDLDVVDEMRDAAMVGKLGADDAAAVAAPDIVNAATAGGAETLGFDAGRVEAGALADLAIVDLDAPHLTPSHDLVSHLAYAVRGSDVRHTVVGGEVIVEDREVRTLDAGAVKREAERHAAELVSRAEGD