Q1C0N0 AF NFT

Acetyl-coenzyme A synthetase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeihkinblm3jz6ha5zqzvwgrmc4kaxxotjkjmrn33zqjerugmwqn3he/metadata.json
IPFS of structure file: 
bafybeig6corq3cabedc5r7ddo7cbcph6ggc7tiesedz4nz4xbzgahbefg4/AF-Q1C0N0-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x0D671490EE06E75975e90D7F99F73B20BE618744
First NFT owner address (bech32): 
genesis1p4n3fy8wqmn4ja0fp4lenaemyzlxrp6y5ma6w5
Current NFT ID #8085 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
8085
Accession (UniProt):
Q1C0N0
Name::
Acetyl-coenzyme A synthetase
Experimental Method:
Predicted structure
Sequence source:
Yersinia pestis bv. Antiqua (strain Antiqua)

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 Q1C0N0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q1C0N0|ACSA_YERPA Acetyl-coenzyme A synthetase OS=Yersinia pestis bv. Antiqua (strain Antiqua) OX=360102 GN=acs PE=3 SV=1 MSQIHKHPIPAAIAEHALITPEKYQHYYQQSVQNPDEFWGEQGKIIDWIKPYKTVKNTSFDPGHVSIRWFEDGTLNLAANCLDRHLAERGDQTAIIWEGDDPNQSKTVTYKQLHHDVCQFANVLKSLGVKKGDVVAIYMPMVPEAAVAMLACARIGAVHSVIFGGFSPDAVAGRIIDSHSKLVITADEGIRAGRAIPLKKNVDEALKNPAITSIKNVVVFQRTGNASYWEDGRDVWWHDLIKEASADCPPEEMNAEDPLFILYTSGSTGKPKGVVHTTGGYLVYAALTFKYVFDYHPGDIYWCTADVGWVTGHSYLLYGPLACGAITLMFEGVPNYPGVNRLSQVVDKHKVNILYTAPTAIRALMAEGDKAIEGTKRDSLRIMGSVGEPINPEAWEWYYNKIGNSKCPIVDTWWQTETGGFMITPLPGATELKAGSATRPFFGVQPALVDNLGNPQEGVAEGNLVITDSWPGQARTLFGDHERFEQTYFSTFKGMYFSGDGARRDEDGYYWITGRVDDVLNVSGHRLGTAEIESALVAHPKIAEAAVVGVPHNIKGQAIYAYITLNHGEEPTPELYTEVRNWVRKEIGPLATPDILHWTDSLPKTRSGKIMRRILRKIATGDTSNLGDTSTLADPSVVEKLLEEKQSMQTPS