P71082 AF NFT

Putative multidrug export ATP-binding/permease protein YgaD

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeigxgfig53iears6nkinxvmljjni4b4dnfyaskhyaf733ormwof43u/metadata.json
IPFS of structure file: 
bafybeihndy6gbsjh7yc2765ak7awne7ccrb4iapgqnlwnu5trjas7b7ati/AF-P71082-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xBF78C9477c2720e82674bE2b804A22DBB3A2463f
First NFT owner address (bech32): 
genesis1hauvj3muyuswsfn5hc4cqj3zmwe6y33lxrgrr9
Current NFT ID #530307 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
530307
Accession (UniProt):
P71082
Name::
Putative multidrug export ATP-binding/permease protein YgaD
Experimental Method:
Predicted structure
Sequence source:
BACILLUS SUBTILIS (STRAIN 168)

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 P71082 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P71082|YGAD_BACSU Putative multidrug export ATP-binding/permease protein YgaD OS=Bacillus subtilis (strain 168) OX=224308 GN=ygaD PE=3 SV=2 MGVMKRYMQFVKPYKKQIFVTVLIGIVKFSIPLALPLLLKYVVDDIIQGGGTASDKTTSLFTIMAIMFALFLILRPPVEYYRQYFAQWTASKVLYDIRAKLFDHIQKLSLRFYANTRTGEVISRVINDVEQTKDFVITGLMNIWLDMLTILIVISIMLTLDVKLTLISIVLFPLYGISVKYFYGRLRKLTRERSQALAQVQGHLHERIQGMPVIRSFAIEDHEQAQFNEKNGHFLDKAIRHTNWNAKTFAVVNTITDLAPLIVIACAGYFVINGPLTVGTMVAFVGYIDRMYNPVRRLINSSTTLTQSIASMDRVFEFIDEPYELTDKPNAIKADQIRGGVEFQNVSFQYEKDKENILHDVSLKVNRGETVALVGMSGGGKSTLVSLIPRFYDVTSGRLLIDGTDIRDYEARSLRNQVGMVLQDTFLFSETIRENIAIGKPDATLEEIIEAAKAANAHEFIMSFPEGYETRVGERGVKLSGGQKQRISIARVFLKNPPLLILDEATSALDLESEHYIQEAMDKLAKDRTTFVVAHRLSTITHADKIVVMENGTIIEIGTHDELMDYESQYKHLFTIQNLN