A7MPJ7 AF NFT

Acetyl-coenzyme A synthetase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihc7qryswzgheamqi6zowhe7a5ohzknuitfjocaaek7e4mlvtz5jq/metadata.json
IPFS of structure file: 
bafybeig5c66opxywibzu2nyiyucz4lyjcvtn4czm4c4kbqqaohzqd5s2aa/AF-A7MPJ7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7782F4817b37743d738e9aF842bf74bd50f2015E
First NFT owner address (bech32): 
genesis1w7p0fqtmxa6r6uuwntuy90m5h4g0yq27nqe34a
Current NFT ID #7957 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
7957
Accession (UniProt):
A7MPJ7
Name::
Acetyl-coenzyme A synthetase
Experimental Method:
Predicted structure
Sequence source:
Cronobacter sakazakii (strain ATCC BAA-894)

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 A7MPJ7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A7MPJ7|ACSA_CROS8 Acetyl-coenzyme A synthetase OS=Cronobacter sakazakii (strain ATCC BAA-894) OX=290339 GN=acs PE=3 SV=1 MSQIHKHPIPAAIAERCLITPEQYDAKYQQSVHDPDAFWGEQGKILDWITPYQTVKNTSFAPGNVSIKWYEDGTLNLAANCLDRHLSERGDQTAIIWEGDDATQSKHISYRELHRDVCRFANTLTSLGIKKGDVVAIYMPMVPEAAVAMLACARIGAVHSVIFGGFSPEAVAGRIIDSSARLVITADEGVRAGRAIPLKKNVDDALKNPGVTSVEHVVVFKRTGGNIEWHEGRDLWWSELVKKASAHHQPVEMNAEDPLFILYTSGSTGKPKGVLHTTGGYLVYAATTFLYAFDYHPGDIYWCTADVGWVTGHSYLLYGPLACGATTLMFEGVPNWPSANRMAQVVDKHKVNILYTAPTAIRALMAEGDKAIDGTDRSSLRILGSVGEPINPEAWEWYWKKIGNEKCPVIDTWWQTETGGFMITPLPGATELKAGSATRPFFGVQPALVDNEGNPQQGATEGNLVITDSWPGQARTLFGDHERFEQTYFSTFKNMYFSGDGARRDEDGYYWITGRVDDVLNVSGHRLGTAEIESALVSHPKIAEAAVVGIPHSIKGQAIYAYVTLNHGEEPSAELYSEVRNWVRKEIGPLATPDVLHWTDSLPKTRSGKIMRRILRKIAAGDTSNLGDTSTLADPGVVEKLLEEKQSIAMPS