A7MHL3 AF NFT

Methionine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiabhrzijv525zvdj5sdsohmrfntr4eyw3x3g2pt2zqsoggks5ho6i/metadata.json
IPFS of structure file: 
bafybeia37o6hdikfpu6qlvhra77udxjx4yp7cjudcrrr2b6pvlxxpmeakm/AF-A7MHL3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7B131B54B20620f27c292388383dC24b5cBbb38F
First NFT owner address (bech32): 
genesis10vf3k49jqcs0ylpfywyrs0wzfdwthvu0q2py2u
Current NFT ID #450731 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
450731
Accession (UniProt):
A7MHL3
Name::
Methionine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Cronobacter sakazakii (strain ATCC BAA-894)

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 A7MHL3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A7MHL3|SYM_CROS8 Methionine--tRNA ligase OS=Cronobacter sakazakii (strain ATCC BAA-894) OX=290339 GN=metG PE=3 SV=2 MTQVAKKILVTCALPYANGSIHLGHMLEHIQADVWVRYQRMRGHEVNFICADDAHGTPIMLKAQQLGITPEQMIAEMSKEHQTDFAGFNISYDNYHSTHSDENRVLSELIYTRLKENGFIKNRTISQLYDPEKGMFLPDRFVKGTCPKCKSPDQYGDNCEVCGATYSPTELIEPKSVVSGATPVMRDSEHFFFDLPSFSEMLQAWTRSGALQEQVANKMQEWFESGLQQWDISRDAPYFGFEIPNAPGKYFYVWLDAPIGYMGSFKNLCDKRGDTVSFDEYWKKDSDAELYHVIGKDIVYFHSLFWPAMLEGSGFRKPTNLFVHGYVTVNGAKMSKSRGTFIKASTWLNHFDADSLRYYYAAKLSSRIDDIDLNLEDFVQRVNADIVNKVVNLASRNAGFIAKRFDGELAAELADPALYQTFTDAAAVIGEAWESREFGKAIREIMALADLANRYVDEQAPWVVAKQEGRDADLQAICSMGINLFRVLMTYLKPVLPELCARAEAFLNVELTWDGVNAPLLGHKVNSFKALYNRIEMKQVEALVEASKEEVKAAAAPVTGELAENPIQETITFDDFAKIDLRVALIENAEFVDGSDKLLRLTLDLDGEKRNVFSGIRSAYPDPQALIGRLTVMVANLAPRKMRFGISEGMVMAAGPGGKDIFLLSPDSGAKPGQQVK