A8C984 AF NFT

Myosin light chain kinase 3

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeieuqhbye4gslbkl5yody4muoeqgpo6j3mso3c4edivwy57gmjwgoy/metadata.json
IPFS of structure file: 
bafybeicq4watmuhpeybnl7jlewhch7gtribip3y7bpsckutvxm2andfova/AF-A8C984-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x0787bC904B7be1a8Bd84a0CF1b68B9B4567D88eb
First NFT owner address (bech32): 
genesis1q7rmeyzt00s630vy5r83k69ek3t8mz8tr6xpum
Current NFT ID #255458 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
255458
Accession (UniProt):
A8C984
Name::
Myosin light chain kinase 3
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 A8C984 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A8C984|MYLK3_DANRE Myosin light chain kinase 3 OS=Danio rerio OX=7955 GN=mylk3 PE=2 SV=1 MGTSLYRSTLLSTGGFSVSDYIRKFTKNKPDVNNIQPNVGFCHQSTQTSQQVKEVISAELELQQPEVTLPNDPSSQHSPEAHTGASEPLKPVSAGESSKALQKAKEISVKSSEPTHVQTFAPAVHLEQIDVHNVPKQETNSLVAAPADAKCVIETMTKVEKDIALQQERALERADDSHVANKVFLKSIIPGQQLEKIEDPQQQAEVSLFVDEDSLEKSVPGQHLDKKMEVLQKQAKDLPVVDEDSLNLSAPGQRQLEEKVDVPEKAEKKIDEAHKKMEEVPRKDELCIEKPVIRHAGIKTQHEETMGMETTVPKHDESKCPTETYVEKTENKNADVTLESDKIMICAVSPQNTSLDEDEKSKAAPLRKVESTLLIIDDSPPLPAPFDHRIVSAKQVPINSYYAVNPVEVLGGGRFGQVHKCAELSSGLTLAAKIIKVRGMKERDEVKNEIGVMNQLNHVNLIQLYDAFESRTNLTLIMEYVEGGELFERIIDESYQLTELDAIVFTRQICEGVQYLHQQYILHLDLKPENILCVNSTGNQIKIIDFGLARKYRPREKLKVNFGTPEFLAPEVVNYDFVSFPTDMWSVGVITYMLLSGLSPFMGDNDAETMNNILHAKWEFDTEAFENVSEEAKDFISSLLVSAKCSRLSASGCMKHSWLNNLEDKAKMYKVRLKSQMMLQRYLVAHRQWKKHFYAVAAANRLKRFQQSRSISTPN