Q8MJA0 AF NFT

Forkhead box protein P2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicwtcab7ejwyhjsg5vgrysetmcwpoievgiffhymhi5xzpotfj2ngu/metadata.json
IPFS of structure file: 
bafybeig7wxqy3iavp5juzs2ifhvqbbw2zrzjaczjkrmx3ecjkhngyj7r4y/AF-Q8MJA0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x60b588AdB9eC3Ae190f6dfe62fBd8F2eb45726FD
First NFT owner address (bech32): 
genesis1vz6c3tdeasawry8kmlnzl0v09669wfha0l0tvz
Current NFT ID #135216 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
135216
Accession (UniProt):
Q8MJA0
Name::
Forkhead box protein P2
Experimental Method:
Predicted structure
Sequence source:
PAN TROGLODYTES

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 Q8MJA0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q8MJA0|FOXP2_PANTR Forkhead box protein P2 OS=Pan troglodytes OX=9598 GN=FOXP2 PE=2 SV=1 MMQESATETISNSSMNQNGMSTLSSQLDAGSRDGRSSGDTSSEVSTVELLHLQQQQALQAARQLLLQQQTSGLKSPKSSDKQRPLQVPVSVAMMTPQVITPQQMQQILQQQVLSPQQLQALLQQQQAVMLQQQQLQEFYKKQQEQLHLQLLQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQQHPGKQAKEQQQQQQQQQQLAAQQLVFQQQLLQMQQLQQQQHLLSLQRQGLISIPPGQAALPVQSLPQAGLSPAEIQQLWKEVTGVHSMEDNGIKHGGLDLTTNNSSSTTSSTTSKASPPITHHSIVNGQSSVLNARRDSSSHEETGASHTLYGHGVCKWPGCESICEDFGQFLKHLNNEHALDDRSTAQCRVQMQVVQQLEIQLSKERERLQAMMTHLHMRPSEPKPSPKPLNLVSSVTMSKNMLETSPQSLPQTPTTPTAPVTPITQGPSVITPASVPNVGAIRRRHSDKYNIPMSSEIAPNYEFYKNADVRPPFTYATLIRQAIMESSDRQLTLNEIYSWFTRTFAYFRRNAATWKNAVRHNLSLHKCFVRVENVKGAVWTVDEVEYQKRRSQKITGSPTLVKNIPTSLGYGAALNASLQAALAESSLPLLSNPGLINNASSGLLQAVHEDLNGSLDHIDSNGNSSPGCSPQPHIHSIHVKEEPVIAEDEDCPMSLVTTANHSPELEDDREIEEEPLSEDLE