Q8J2J3 AF NFT

Antagonist of mitotic exit network protein 1

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigaoqsdwwbk3ropjevxgz73skj2htpc2hg2fftquk6dqnhc76ujfe/metadata.json
IPFS of structure file: 
bafybeicdveavnbois54lsar6is42cckv3htsnlnbmdmni63nqmf6my2kmq/AF-Q8J2J3-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x44E9181FBf88898482e9B5f9f7eBcE7D1B2b0722
First NFT owner address (bech32): 
genesis1gn53s8al3zycfqhfkhul067w05djkpeztpz6zs
Current NFT ID #14883 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
14883
Accession (UniProt):
Q8J2J3
Name::
Antagonist of mitotic exit network protein 1
Experimental Method:
Predicted structure
Sequence source:
Ogataea parapolymorpha (strain ATCC 26012 / BCRC 20466 / JCM 22074 / NRRL Y-7560 / DL-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 Q8J2J3 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q8J2J3|AMN1_OGAPD Antagonist of mitotic exit network protein 1 OS=Ogataea parapolymorpha (strain ATCC 26012 / BCRC 20466 / JCM 22074 / NRRL Y-7560 / DL-1) OX=871575 GN=AMN1 PE=3 SV=1 MWDLAQQYPDTTPESSPLHETRARERRSSLVDHRPLKRASSLSFRIHTPCDYSSGASMTSCTSSTSLSSLGSLSSSARQLIMSRKTSPRHPTRLKTRFFSPFHSTNTSPVSPRLLETVEKLSIDNDAHPIFEIPEIVNLILHYVGHDDRERVPTEHAPVRKPPMSLNHAKLIHGEQGEKVWQRTLSTSSTAPPTGNLHNCLLVNKLWHSLTLEVLQENLYFDSDFKLMNYSPPRIAAPKSFVLHKLKSTQQRDLANLHINPVNLEWLEFYICPKLLPSLHLYTAKLKKLVLPGSKVVDDVYLQQIAPLMPNLVHLDLRACEHITDAGLYAIGTHCPKIETLNCGRHTKGILVTDASISHIVANCNLKTLGVAGCGVSDAILWSLAYQKGHQLERLSLNSCWRLTDAGISSVLMMDRFPRLAVLEIRRLTRLQQLRPIVEFKKRQMRRKIAVLVEACEELEARLRAEERSLDLEISTRIFEDISEWLNGDDLQDQIEERNLQQFVQRRSVLV