P33276 AF NFT

Uncharacterized lipoprotein MYCGA2980

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeih66cuqjlwwfnvv7z73rhsztemppjqooexxj2htaxjwenmuajyitq/metadata.json
IPFS of structure file: 
bafybeic5ds3cohsk67khfoqcerokuckcoz7fpjdf4uj5srshdrxl3ko5ja/AF-P33276-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xfD2aa30896c43ba48ACc736586610789048c2c7a
First NFT owner address (bech32): 
genesis1l542xzykcsa6fzkvwdjcvcg83yzgctr6dmckr0
Current NFT ID #514228 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
514228
Accession (UniProt):
P33276
Name::
Uncharacterized lipoprotein MYCGA2980
Experimental Method:
Predicted structure
Sequence source:
Mycoplasma gallisepticum (strain R(low / passage 15 / clone 2))

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 P33276 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P33276|Y298_MYCGA Uncharacterized lipoprotein MYCGA2980 OS=Mycoplasma gallisepticum (strain R(low / passage 15 / clone 2)) OX=710127 GN=MYCGA2980 PE=3 SV=3 MGLSRKKIFTWPLLLTGMAVVSTTFSSCSVFNFFSNLPTTTDNNNYGPTVQNLSAFLPTSDYKKIYDLSFSLEFNNSGGYNPSRLMSNNEGVTAENVNRPYRVFGTGWLFDWQAQPVDENDQNAKWTGYFATNLHVAEALLNPLDNKNYRPAWYKNELPLPGVDQTLYFNLGKWDENLAVQNKHNPKSLTYLPLSNLPKTVYTATSFYKESPKWITPIEADNPGIREYIDFAVLSITLDLSWTMRNGEKVHRYNDEYQLYNRWIMPAMNVAKALWDNKGVLNQPATPPNKKEEDDQLTDQLPYHGFFDRSNYTNPNVSLNNFSVYLGGYPYYANWPTTPQYTKFSVPSLSRRQIPITSDSRGSPGWTINALNPTELDGQKIASSTSLSSTGGIRAGIYNADIARNFQLVYRNVKYKQYGYGYIIQNSNLSAGSSGSLALTSNNQALGIYFGTVSVDAKKEATFGLVASLFNPNKIQVNVVTGNNVFETDTIQPYDLIYGNDLMTDDYGSYIRSLQTLNLSSRLLTKIKDNLPKKEGSTNQANQQTNQTNRSTDATKKDSSSDETNKNPLAELLSDIFKNLPNWN