P70389 AF NFT

Insulin-like growth factor-binding protein complex acid labile subunit

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeif6fgn5ufyl2jj53d722uqzxw45d22z2r6srxlhtjq77gi5yjynsa/metadata.json
IPFS of structure file: 
bafybeicxljl63tugy7xhtfude6adpzhbwfa5fnmer5k3wxnrswmr24b6zy/AF-P70389-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7a1388B6Aad67B04634A32f63b8A6D6005eab86c
First NFT owner address (bech32): 
genesis10gfc3d426easgc62xtmrhzndvqz74wrv0qh3l8
Current NFT ID #14575 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
14575
Accession (UniProt):
P70389
Name::
Insulin-like growth factor-binding protein complex acid labile subunit
Experimental Method:
Predicted structure
Sequence source:
MUS MUSCULUS

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 P70389 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P70389|ALS_MOUSE Insulin-like growth factor-binding protein complex acid labile subunit OS=Mus musculus OX=10090 GN=Igfals PE=1 SV=1 MALRTGSPALVVLLAFWVALGPCYLQGTDPGASADAEGPQCPVTCTCSYDDYTDELSVFCSSRNLTQLPDGIPVSTRALWLDGNNLSSIPSAAFQNLSSLDFLNLQGSWLRSLEPQALLGLQNLYHLHLERNLLRSLAAGLFRHTPSLASLSLGNNLLGRLEEGLFRGLSHLWDLNLGWNSLVVLPDTVFQGLGNLHELVLAGNKLTYLQPALLCGLGELRELDLSRNALRSVKANVFIHLPRLQKLYLDRNLITAVAPRAFLGMKALRWLDLSHNRVAGLLEDTFPGLLGLHVLRLAHNAITSLRPRTFKDLHFLEELQLGHNRIRQLGEKTFEGLGQLEVLTLNDNQIHEVKVGAFFGLFNVAVMNLSGNCLRSLPEHVFQGLGRLHSLHLEHSCLGRIRLHTFAGLSGLRRLFLRDNSISSIEEQSLAGLSELLELDLTANQLTHLPRQLFQGLGQLEYLLLSNNQLTMLSEDVLGPLQRAFWLDLSHNRLETPAEGLFSSLGRLRYLNLRNNSLQTFVPQPGLERLWLDANPWDCSCPLKALRDFALQNPGVVPRFVQTVCEGDDCQPVYTYNNITCAGPANVSGLDLRDISETLFVHC