P45082 AF NFT

ATP-binding/permease protein CydD

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeibsnrot332lzwjrufabuaq67okr5k5nf75mwpn7inubpnv63mgtsy/metadata.json
IPFS of structure file: 
bafybeifmxumo3hztuvxjgxvmpapwtbji5hc54di7p6olglqzvwujpro3wq/AF-P45082-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x9b4a5433D1f672742697bef940cd1C6DDee6FebF
First NFT owner address (bech32): 
genesis1nd99gv737ee8gf5hhmu5pngudh0wdl4la3h9ks
Current NFT ID #84539 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
84539
Accession (UniProt):
P45082
Name::
ATP-binding/permease protein CydD
Experimental Method:
Predicted structure
Sequence source:
HAEMOPHILUS INFLUENZAE (STRAIN ATCC 51907 / DSM 11121 / KW20 / RD)

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 P45082 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P45082|CYDD_HAEIN ATP-binding/permease protein CydD OS=Haemophilus influenzae (strain ATCC 51907 / DSM 11121 / KW20 / Rd) OX=71421 GN=cydD PE=3 SV=1 MNKLRQKYLQKWLRAQQEPIKKLMRANIVLATLSSFILVAQTYFLATLLDKLIMQNVPRDELIPYFLGLIIGFGMRAIILWAREKIGFQSGQLLRNHIRQKILDKIHLVGPATINQKPAGSWASIMLEQVENLHNFYARFLPQQSLSAIVPVVIFIAVFPLNWAAGLILMITAPLVPLFMIIVGIAAADNSQKNMDTLSRLSAQFLDRLRGLETLRLFNRTSEQTEHIENATEDFRETTMDVLKLAFLSSAVLEFFTSISIALMAVYFGFSYLGQIEFGTYNAPLTLFTGFFCLILAPEFYQPLRDLGTYYHDRAAGIGAADAIVDFLESDYLTVHQNEKTISLESAVEISAENLVVLSTQGSALTKPLNFQIPANHNVALVGQSGAGKTSLMNVILGFLPYEGSLKINGQELRESNLADWRKHIAWVGQNPLLLQGTIKENLLLGDVQANDEEINQALMRSQAKEFTDKLGLHHEIKDGGLGISVGQAQRLAIARALLRKGDLLLLDEPTASLDAQSENLVLQALNEASQHQTTLMITHRIEDLKQCDQIFVMQRGEIVQQGKFTELQHEGFFAELLAQRQQDIQ