Q2FLL1 AF NFT

Phenylalanine--tRNA ligase beta subunit

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifelm77mk5l7nrkvua64seis7kcifbd7poiyeyf3y2fcrklagrghq/metadata.json
IPFS of structure file: 
bafybeiczusqwsrgatv6d7lptojsdpprz3wuzqom3jkrylb5zlljqlqmvoa/AF-Q2FLL1-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xC0f5cbbADD874269E5C27dE8258432D46a904ac7
First NFT owner address (bech32): 
genesis1cr6uhwkasapxnewz0h5ztppj634fqjk8n2xjps
Current NFT ID #446929 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
446929
Accession (UniProt):
Q2FLL1
Name::
Phenylalanine--tRNA ligase beta subunit
Experimental Method:
Predicted structure
Sequence source:
METHANOSPIRILLUM HUNGATEI JF-1 (STRAIN ATCC 27890 / DSM 864 / NBRC 100397 / JF-1)

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 Q2FLL1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q2FLL1|SYFB_METHJ Phenylalanine--tRNA ligase beta subunit OS=Methanospirillum hungatei JF-1 (strain ATCC 27890 / DSM 864 / NBRC 100397 / JF-1) OX=323259 GN=pheT PE=3 SV=1 MPVVSLPYKYLERLCGIDRKTIIDHLPMIGSDIERILEDQVDVEFFPSRVDLYSTEGVARAMRGFLELETGEEVYPVSPSSITFSVDENLKEVRPYIGSAVIRDIQLDNEAIISLMGVQEALHWVVGRGRAKVAIGVHDLDKVTPPFRYYGAPVTRSFIPLDYDREMTLTEIMEEHPKGRDYSHIVKDKPVMPLIEDANGNVLSFPPIINGELTRVTESSKNLLLDVTGTDERAVMTALRVLCTTLITAGGRCESVTVNGVVMPDLSPALRNINVTSCNKLLGTSFSADEIATILKKMRYGAEKSGDSTVSIRIPCYRADIMHEWDVYEDVAIGAGFGNLEAELPATFATGCEHPIIALASAIRDICSGLGYLEVMPFTLSNEDVMYTRMQREPDPKALHVLHPISEEQTLVRTDLLPLLLDLLRMNKRRELPQRIFHTGDVVSSMQTYQKLALASTHPGADFSEAYATTDALMRELGISYLPAESADPAFIPGRAVDIMVDGKKMGVFGEIHPGVLNKFDIDQPVIGIEIDLHLLFPGQKETKS