Q80VY9 AF NFT

ATP-dependent RNA helicase DHX33

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeic2bstqqkjw7xgvpoj4jp2wdhn4ea7natjsjczkwgchxct4vs35fq/metadata.json
IPFS of structure file: 
bafybeih2otcpe4atazjdfewp4pnzitxxzo23yz4lklwyvsdh3uvfy34v7q/AF-Q80VY9-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xbDA05C32aD6eE153C8C5A78dfa48d7Ba25694fea
First NFT owner address (bech32): 
genesis1hks9cv4ddms48jx957xl5jxhhgjkjnl2ggp9vq
Current NFT ID #97922 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
97922
Accession (UniProt):
Q80VY9
Name::
ATP-dependent RNA helicase DHX33
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 Q80VY9 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q80VY9|DHX33_MOUSE ATP-dependent RNA helicase DHX33 OS=Mus musculus OX=10090 GN=Dhx33 PE=1 SV=1 MPEEASLPPAKRFRPGSCPPGRRVVMLLTAGGGGGAGGGRRQTPPLAQPSASPYREALELQRRSLPIFRARGQLLAQLRNLDNAVLIGETGSGKTTQIPQYLYEGGISRQGIIAVTQPRRVAAISLATRVSDEKRTELGKLVGYTVRFEDVTSEDTRIKFLTDGMLLREAISDSLLRKYSCVILDEAHERTIHTDVLFGVVKTAQKRRKELGKLPLKVIVMSATMDVDLFSQYFNRAPVLYLEGRQHPIQIFYTKQPQQDYLHAALVSVFQIHQEAPASQDILVFLTGQEEIEAMSKTCRDIARHLPDGCPSMLVLPLYASLPYSQQLRVFQGAPKGYRKVIISTNIAETSITITGIKYVVDTGMVKAKKYNPDSGLEVLAVQRVSKTQAWQRTGRAGREDSGICYRLYTEDEFEKFEKMTVPEIQRCNLASVILQLLAMKVPNVLTFDFMSKPSPDHIEAAIAQLDLLGALEHKDDQLTLTPIGRKMAAFPLEPRFAKTILLSSKFHCTEEILTIVSLLSVDSVLYNPPARRDEVQSVRKKFISSEGDHITLLNIYRTFKNIGGNKDWCKENFVNSKNMLLVAEVRAQLREICLKMSMPIMSSRGDMESVRRCMAHSLFMNTAELQTDGTYATTDTHQPVAIHPSSVLFHCKPACVVYTSLLYTNKCYMRDLCVVDAEWLYEAAPDYFRRKLRTARN