B4TNL2 AF NFT

Methionine--tRNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeidmcyfgio6vkzmzfta25rfpsmsdyhbgi2lkwcrrnnabobojpenqey/metadata.json
IPFS of structure file: 
bafybeic7qylm7i3humgk5kgcre64jjtu7qzht3p5o5elpd4g7mk7hav7zi/AF-B4TNL2-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x1f017c069141048125D6750ba3Cef818EC174268
First NFT owner address (bech32): 
genesis1ruqhcp53gyzgzfwkw5968nhcrrkpwsng7ryy4h
Current NFT ID #450961 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
450961
Accession (UniProt):
B4TNL2
Name::
Methionine--tRNA ligase
Experimental Method:
Predicted structure
Sequence source:
Salmonella schwarzengrund (strain CVM19633)

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 B4TNL2 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B4TNL2|SYM_SALSV Methionine--tRNA ligase OS=Salmonella schwarzengrund (strain CVM19633) OX=439843 GN=metG PE=3 SV=1 MTQVAKKILVTCALPYANGSIHLGHMLEHIQADVWVRYQRMRGHEVNFICADDAHGTPIMLKAQQLGITPEQMIGEMSQEHQTDFAGFNISYDNYHSTHSDENRELSELIYTRLKENGFIKNRTISQLYDPEKGMFLPDRFVKGTCPKCKSADQYGDNCEVCGATYSPTELIEPKSVVSGATPVMRDSEHFFFDLPSFSEMLQAWTRSGALQEQVANKMQEWFESGLQQWDISRDAPYFGFEIPNAPGKYFYVWLDAPIGYMGSFKNLCDKRGDTTSFEEYWKKDSDAELYHFIGKDIVYFHSLFWPAMLEGSHFRKPTNLFVHGYVTVNGAKMSKSRGTFIKASTWLKHFDADSLRYYYTAKLSSRIDDIDLNLEDFVQRVNADIVNKVVNLASRNAGFINKRFDGVLAAELADPQLYKTFTDAAVVIGEAWESREFGKAIREIMALADVANRYVDEQAPWVVAKQEGRDADLQAICSMGINLFRVLMTYLKPVLPTLSERVEAFLNSELNWDAIEQPLLGHKVNTFKALYNRIDMKQVEALVEASKEEVKAAAAPVTGPLADFPIQETITFDDFAKVDLRVALIENAEFVEGSDKLLRLTLDLGGEKRNVFSGIRSAYPDPQALIGRQTVMVANLAPRKMRFGVSEGMVMAAGPGGKDIFLLSPDDGAKPGQQVK