Q9X6B0 AF NFT

Catalase-peroxidase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihsyjdp5dfrcoy5qdjp4zoa6h5apbxe662rnrdcd2wdrn5efdcjwe/metadata.json
IPFS of structure file: 
bafybeigln4pfwbdnmkchs2h5unfn7276biuirwvf4pt3jcl5lp36belbya/AF-Q9X6B0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x527Fc9Ea796aC165D96B0eDa7256bB7d212B69Df
First NFT owner address (bech32): 
genesis12flun6nedtqktkttpmd8y44m05sjk6wl0g7e56
Current NFT ID #198732 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
198732
Accession (UniProt):
Q9X6B0
Name::
Catalase-peroxidase
Experimental Method:
Predicted structure
Sequence source:
YERSINIA PESTIS

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 Q9X6B0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q9X6B0|KATG_YERPE Catalase-peroxidase OS=Yersinia pestis OX=632 GN=katG PE=1 SV=1 MLKKILPVLITLAIVHNTPTAWAAEAPKTDSFYLPKSLDLSPLRLHNIESNPYGKDFNYAQQFKTLDLEAVKKDIKTVLTTSQDWWPADYGNYGPFFIRMAWHGAGTYRIYDGRGGADGGQQRFEPLNSWPDNANLDKARRLLWPIKKKYGAKISWGDLMVLTGNVALESMGFKTLGFAGGREDDWQSDLVYWGAGNKMLSDNRDKNGKLPKPLAATQMGLIYVNPEGPNGKPDPVAAAKDIREAFARMAMNDEETVALIAGGHTFGKAHGAASPEKCLGAAPGEAGLEQQGLGWANKCGSGNGKDTITSGLEGAWTTDPTHFTMQYLSNLYKHEWVLTKSPAGAWQWKPKNAANVVPDATDPTKFHPLMMFTTDIALKVDPEYKKITTRFLENPEEFKMAFARAWFKLTHRDMGPAARYLGDEVPKETFIWQDPLPAANYKMIDSADISELKDKILKTGLSDTKLIKTAWASASTFRGTDFRGGDNGARIRLAPQKDWPVNDPAELHSVLAALMEVQNNFNKDRSDGKKVSLSDLIVLGGNAAIEDAAKKAGYSISIPFTPGRTDASQEETDVSSFAVLEPTADGFRNYYDAKRNTLSPIASLIDRANKLELTVPEMTVLIGGLRVLDVNSGGSKAGVLTNTPGQLNNNFFVNLLDMSTKWTKSPKAEGYFDGYDRKTGKLKWTASSVDLVFGSNPELRAVAEVYASDDAKEKFVHDFTKVWEKVMNLDRFDIKNN