P34090 AF NFT

Conditioned medium factor

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeifyz4my5apaehw2mjuhd2ud7hgfrnqt4qyalulhmhzeayz22owyjq/metadata.json
IPFS of structure file: 
bafybeian4ttb3ax5yj3pu22rgwlx4h5krp2vx6k56guvi5fkr6gr3d33zu/AF-P34090-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x463a972e031C685D1BD2C43E9dFA3Ed66670D0bc
First NFT owner address (bech32): 
genesis1gcafwtsrr3596x7jcslfm7376en8p59uktr335
Current NFT ID #67643 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
67643
Accession (UniProt):
P34090
Name::
Conditioned medium factor
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 P34090 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P34090|CMF_DICDI Conditioned medium factor OS=Dictyostelium discoideum OX=44689 GN=cmfA PE=1 SV=2 MRLLLLLILIITINFSYGVLTPKNLAGEASEFGSFNIPNPMDVAQSSDSSLFGISMTQSQLKNSFEWSGVVPVDSEEEFTLTFFSSFPLSEFKVEASPKTSLSSSKSESEHKSKFYKVIKSIYQTPTVTNGSFGIDGATTPSFSLTWDKPVVGDWNVVITASSSLRKNEKFQKMVEDSTPQLLMLVQNPSDTHIYSYVSSYNNLFTGQKVSVLAMLHKKSEFIKKSSANRPLNWKPSPILLSDVSAEMILGLPDGSKETIPMFDDGLHDDEQANDGLFGGYINVSELGNYDLQVVYKGSQNGNGVIRSNQHLIPITSQYLELTGEVQSVQDGDANLNIYFIVNSPNQTTVDQTPVHVYSEVYGTDDDGKKVAIAWVAGVTSAQPIQGSTTTFALSAVLNERWIAKVGATAPFFVKNVQVSDLDTFIPLSNTTSTSNVKMVGEYKDVRTIVHSPPLHEITKEMRDGKMPKELADRIGKSTGNGKLILTHGYCSEGVWPIEDFENSVEFQDFNQNRGNDEFAQILANFGSQYTDGFSLVAHSQGGNAALHLVTFYFSGLDLSQKLEGRVIQSMGTPYQGTALAGTWASIGSAVGVGCSANDDLTVDGAALWLKSIPADKRALVYYTTTQYSTGSLINYCNLASNAVLEWPNDGVVDNEHTPLEGGVYLNNFKDWCHSDGMHSPQQTTNTEYNKEMSSNSVW