Q5TYQ1 AF NFT

Protein zyg-11 homolog

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeih3bfdx4zjmgqshhazkcpd7mcr2iyvfrn3vifqicqqg3btvcwrbyu/metadata.json
IPFS of structure file: 
bafybeidgnsjjm6jclvo4b6fvytp76rnr457ejvep4kcl5zc6x72m6cfwmm/AF-Q5TYQ1-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xc0F878C3f3E2a1874e2A6daC80d429e553DdB0D5
First NFT owner address (bech32): 
genesis1cru83slnu2scwn32dkkgp4pfu4famvx4x7nssv
Current NFT ID #542303 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
542303
Accession (UniProt):
Q5TYQ1
Name::
Protein zyg-11 homolog
Experimental Method:
Predicted structure
Sequence source:
DANIO RERIO

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 Q5TYQ1 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q5TYQ1|ZYG11_DANRE Protein zyg-11 homolog OS=Danio rerio OX=7955 GN=zyg11 PE=2 SV=1 MIHLSQTMDKSSLPSLSDLCMSLVSSRLELFCEMRDDGSLSFREPLVFPQELADQLLCKMATDGVLNDSTVGIFRNCQQFRLRHACIRTARISAEAFHRALCPHRLVELDASRVNADLTIADILRGLSSNKSLQESLQRLVLNGLTMSSLEEPSRRCFSAMQGLRALSVSNVDFYDWGLADVCLLPRLESLDISNTSVSNLTPLLGLRSRLRYLTMHQLKRLEMTTAQLLAVLSQLEVLQHLDISDDKQFTSDVARQLLETPGILPQLVSLDVSGRKQVTDAAVKAFVEARPGMTFVGLLATDAGFSEFLSGEGSLKVTGEANETQICEALRRYSEREGFVREALFHLFSLTHAIEKPRPDILKLVALGMKNHPTTLNVQLAASACVFNLTKQELAFGIPVRLLGNVTQQLLEAMKTFPNHQQLQKNCLLSLCSDRILQEVPFNRFEAAKLVMQWLCNHEDQNMQRMAVAIISILAAKLSTEQTAQLGAELFIVKQLLHIVRQKTCQSTVDATLKFTLSALWNLTDESPTTCRHFIENQGLELFIKVLESFPSESSIQQKVLGLLNNIAEVSELHGELMVQSFLDHIRTLLHSPEVEVSYFAAGILAHLTSRGEKVWTLELTLRNTLLQQLHSAILKWPTPECEMVAYRSFNPFFPLLECFQTPGVQLWAAWAMQHVCSKNAGRYCSMLLEEGGLQHLEAITSHPKTHSDVRRLTESILDGLQRHRARTGYTAIPKTQAHREKCNP