A9R2M7 AF NFT

Methionine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeih72oh3sufpo4o6lecgukg337odenp7x7lcffcouvkozf5remr2pm/metadata.json
IPFS of structure file: 
bafybeihknshu65lissxfmsdcwrhmqfupeyiq72qygg4ptkvusdqqydrhpm/AF-A9R2M7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x58f547A730085a8675d05778EcA28963bCE2dbA7
First NFT owner address (bech32): 
genesis1tr650fesppdgvaws2auweg5fvw7w9ka8qqeh80
Current NFT ID #451060 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
451060
Accession (UniProt):
A9R2M7
Name::
Methionine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Yersinia pestis bv. Antiqua (strain Angola)

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 A9R2M7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A9R2M7|SYM_YERPG Methionine--tRNA ligase OS=Yersinia pestis bv. Antiqua (strain Angola) OX=349746 GN=metG PE=3 SV=1 MAQVAKKILVTCALPYANGSIHLGHMLEHIQADIWVRFQRMRGNQVHFICADDAHGTPIMLKAQQMGIEPEQMIAEMSQEHQQDFAGFAISYDNYHSTHSDENRELSSLIYGRLKANGYIKNRTISQLYDPEKGMFLPDRFVKGTCPKCKAPEQYGDNCEVCGATYSPTELIDPKSAVSGATPVMRESEHFFFDLPAFSDMLQAWTRSGALQEQVANKMQEWFDSGLQQWDITRDAPYFGFEVPDAPGKYFYVWLDAPIGYMGAFKNLCDKRGDLDFDEFWGKDAKTDLYHFIGKDIVYFHSLFWPAMLEGSNFRKPTNLFVHGYVTVNGAKMSKSRGTFIKAGTYLKYLDADCLRYYYAAKLSSRIDDIDLNLEDFVQRVNADIVNKVVNLASRNAGFINKRFAGQLADQLADPVLYKTFTDAATSIADAYNNRESGKAIREIMALADVANRYVDEQAPWVVAKQEGRDADLHAICSMGINLFRVLMTYLKPVLPSLTERTEAFLNTELTWDSIEQPLLGHSITAFKALFNRIDLDKVNEMVASSKEDMAPATRVTGPLADDPIQETISFDDFAKVDMRIALIQQAEFVEGSDKLLKLTLELGGETRQVFSGLRSAYPDPKALEGRMTVMVANLAPRKMRFGVSEGMVMAAGPGGSDIFLLSPDSGAQPGMQVK