Q554L3 AF NFT

Putative uncharacterized protein DDB_G0275003

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiadgp2bxrd4rtz6hjmjtwjkuwhdm2gnh6hvfdlecgtq5ysjdbnwve/metadata.json
IPFS of structure file: 
bafybeibtumd6jc2r5qjm6bcz7j55lhirnebqv6egfsm5aam6dy3oh4egxy/AF-Q554L3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x696fbd2e2d1499E7c009274A815cA989Bda9ceaC
First NFT owner address (bech32): 
genesis1d9hm6t3dzjv70sqfya9gzh9f3x76nn4v5xtu99
Current NFT ID #518790 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
518790
Accession (UniProt):
Q554L3
Name::
Putative uncharacterized protein DDB_G0275003
Experimental Method:
Predicted structure
Sequence source:
DICTYOSTELIUM DISCOIDEUM

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 Q554L3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q554L3|Y5003_DICDI Putative uncharacterized protein DDB_G0275003 OS=Dictyostelium discoideum OX=44689 GN=DDB_G0275003 PE=4 SV=1 MILPNYLISKILNYLIFHEYQYEINYISLDKNYFDNNYYICEHFPASHILSYGLVCKELFEIVSSLITITSRLSFKCPRHSKFCIVQSKNITRFIEEHRPSYKTKSERDSIKSSLLKENNYGERYRTIQELLDIYPNLKELVYNINLLIFSDFNELRFWDEKFFEQFNSEFLKSTTSSNVKIKIRILLYCEPNSNIKTISTKKSILKFKNDIENSNKKVQVDLLYLKGLDSNMENQDDIFQLINNLTPKKLIIVNEKKILNGELQEIQDKNSGDGGGGGFFSHSSYLKVLQINNIKKIKIIKDFMDPFVFIGIENNIQLKSVDIGFHFHEILFHMIRLFKPVNKNNSSHLIENCKNLKKYISNSDFSFLDKDDFYCFKSICNADEEVIQPIQSIFRVSHCIKDWEEMCHSISISTTLKDLKIKEFCNKLHCQIWKSISSYSNKKSDGITDDGDDVGDRVDCCSHDKIHYRDCINGNEVMNKIPFFTDPFSLMISNNKSIETLHLEGMNGLLNETVLSSFIKNKSIKKLSLNYSLSHSNLDYFLTNVMPRNTTIKHLEIVLTDKLWCDKNTCCPNSLANFIQTNTTLSTLKCQFFDTIIDDSNDGAKKLTFESLINSSKIQKINIST