B1LN54 AF NFT

Methionine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeia4pctnvw4j7nszsl7yexjksivhms32ut3uorgzs4hpsn563aluxi/metadata.json
IPFS of structure file: 
bafybeiccr6vrfrsjv377uvkcjqbkaiv6hokp45c26m6sqznmxibaftfmxm/AF-B1LN54-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x71588E5F8E9a83dA03D5148D4320336d807CA6D0
First NFT owner address (bech32): 
genesis1w9vguhuwn2pa5q74zjx5xgpndkq8efksuvwmfn
Current NFT ID #450764 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
450764
Accession (UniProt):
B1LN54
Name::
Methionine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Escherichia coli (strain SMS-3-5 / SECEC)

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 B1LN54 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B1LN54|SYM_ECOSM Methionine--tRNA ligase OS=Escherichia coli (strain SMS-3-5 / SECEC) OX=439855 GN=metG PE=3 SV=1 MTQVAKKILVTCALPYANGSIHLGHMLEHIQADVWVRYQRMRGHEVNFICADDAHGTPIMLKAQQLGITPEQMIGEMSQEHQTDFAGFNISYDNYHSTHSEENRQLSELIYSRLKENGFIKNRTISQLYDPEKGMFLPDRFVKGTCPKCKSPDQYGDNCEVCGATYSPTELIEPKSVVSGATPVMRDSEHFFFDLPSFSEMLQAWTRSGALQEQVANKMQEWFESGLQQWDISRDAPYFGFEIPNAPGKYFYVWLDAPIGYMGSFKNLCDKRGDSVSFDEYWKKDSTAELYHFIGKDIVYFHSLFWPAMLEGSNFRKPTNLFVHGYVTVNGAKMSKSRGTFIKASTWLNHFDADSLRYYYTAKLSSRIDDIDLNLEDFVQRVNADIVNKVVNLASRNAGFINKRFDGVLASELADPQLYKTFTDAAEVIGEAWESREFGKAVREIMALADLANRYVDEQAPWVVAKQEGRDADLQAICSMGINLFRVLMTYLKPVLPKLTERAEAFLNTELTWDGIQQPLLGHKVNPFKALYNRIDMKQVEALVEASKEEVKAAAAPVTGPLADDPIQETITFDDFAKVDLRVALIENAEFVEGSDKLLRLTLDLGGEKRNVFSGIRSAYPDPQALIGRHTIMVANLAPRKMRFGISEGMVMAAGPGGKDIFLLSPDAGAKPGHQVK