B5R0E7 AF NFT

Methionine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeibqxvwqxx3qq2rqxktyfqzi4bgvqq36mfwpdl75syzeopqab3xkyu/metadata.json
IPFS of structure file: 
bafybeibjogjzg5ljejpsgccmtxpms6o66egzprnsbnav5nu2luwo2oxjk4/AF-B5R0E7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xCC78Ace3dBC179a99a10f6D1FeDF008EfDA3457E
First NFT owner address (bech32): 
genesis1e3u2ec7mc9u6nxss7mglahcq3m76x3t7ftdxqs
Current NFT ID #450952 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
450952
Accession (UniProt):
B5R0E7
Name::
Methionine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Salmonella enteritidis PT4 (strain P125109)

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 B5R0E7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B5R0E7|SYM_SALEP Methionine--tRNA ligase OS=Salmonella enteritidis PT4 (strain P125109) OX=550537 GN=metG PE=3 SV=1 MTQVAKKILVTCALPYANGSIHLGHMLEHIQADVWVRYQRMRGHEVNFICADDAHGTPIMLKAQQLGITPEQMIGEMSQEHQTDFAGFNISYDNYHSTHSDENRELSELIYTRLKENGFIKNRTISQLYDPEKGMFLPDRFVKGTCPKCKSADQYGDNCEVCGATYSPTELIEPKSVVSGATPVMRDSEHFFFDLPSFSEMLQAWTRSGALQEQVANKMQEWFESGLQQWDISRDAPYFGFEIPNAPGKYFYVWLDAPIGYMGSFKNLCDKRGDTTSFDEYWKKDSDAELYHFIGKDIVYFHSLFWPAMLEGSHFRKPTNLFVHGYVTVNGAKMSKSRGTFIKASTWLKHFDADSLRYYYTAKLSSRIDDIDLNLEDFVQRVNADIVNKVVNLASRNAGFINKRFDGVLAAELADPQLYKTFTDAAAVIGEAWESREFGKAIREIMALADVANRYVDEQAPWVVAKQEGRDADLQAICSMGINLFRVLMTYLKPVLPTLSERVEAFLNSELNWDAIEQPLLSHKVNTFKALYNRIDMKQVEALVEASKEEVKAAAAPVTGPLADFPIQETITFDDFAKIDLRVALIENAEFVEGSDKLLRLTLDLGGEKRNVFSGIRSAYPDPQALIGRQTVMVANLAPRKMRFGVSEGMVMAAGPGGKDIFLLSPDDGAKPGQQVK