Q65PF4 AF NFT

tRNA(Ile)-lysidine synthase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihgbae7hjzgljhsxgnpur723yhjoe7ppkbifyl4sbtd4yrcvjt6pm/metadata.json
IPFS of structure file: 
bafybeigo5qx2yq3z3q6va23zfnltrtzfl7xhwnyp4vs2kb3rshtvgxg2fy/AF-Q65PF4-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xD0337f9F0cD31B3005Bb291FE81bE29BE87fe0F8
First NFT owner address (bech32): 
genesis16qehl8cv6vdnqpdm9y07sxlzn058lc8c908pyd
Current NFT ID #466408 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
466408
Accession (UniProt):
Q65PF4
Name::
tRNA(Ile)-lysidine synthase
Experimental Method:
Predicted structure
Sequence source:
Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46)

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 Q65PF4 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q65PF4|TILS_BACLD tRNA(Ile)-lysidine synthase OS=Bacillus licheniformis (strain ATCC 14580 / DSM 13 / JCM 2505 / CCUG 7422 / NBRC 12200 / NCIMB 9375 / NCTC 10341 / NRRL NRS-1264 / Gibson 46) OX=279010 GN=tilS PE=3 SV=1 MKSIEDFMKKYELSFDGVTLIAGVSGGPDSMALLHALHHTVPASATLIAAHVDHMFRGEESERDMRFVQDYCEAEGIQCEAVQIDVLAFAAENNLNKQAAARECRYAFFQELMKRHRAEYLVLGHHGDDQVETMLMKMAKGTVGIGLAGIQPKRRFDSGWLLRPFLKLSKDDLLAYCKENRIPFRTDPSNSEDDYTRNRFRHHVLPFLKKESEDVHKRFQSVSEFLTEDELYLQALTKDKMNTVITSKSSSGVEISIERLLALPMPLQRRGVHLILNYLYENVPSAFSAHHIQVFLDWISQDGPSGSLDFPNGLKVAKSYHTCLFTFQRLQCENVSYHYEISGAGEEELILPGGSSLHVSGPGSTRKLQGNDVFATSPKQVQFPLYVRTRQNGDRVKLKGMNGSKKVKDIFIDEKVPLSKRDSWPIVTDAVGNIIWIPGLKKTIFEELDVTNNDRIVLQYRQHEKCRGLAKNETGY