Q7WAE4 AF NFT

Argininosuccinate lyase

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeidnnseff44gbjvgpf4p5g5thya7vt24fqt4nuc4ealwtpkgxb7qgu/metadata.json

IPFS of structure file:

bafybeify42slmvg4r42yk5ggadqyqfw2uimiyykwlzbkanstrmkxni5due/AF-Q7WAE4-F1-model_v2.pdb

First NFT owner address (eip-55):

0x7Da715D0e1613a441fC7E6dC49611D7Aa50e31AD

First NFT owner address (bech32):

genesis10kn3t58pvyayg878umwyjcga02jsuvddaze6pl

Current NFT ID #22972 owner (web3):

0x7Da715D0e1613a441fC7E6dC49611D7Aa50e31AD

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
22972
Accession (UniProt):
Q7WAE4
Name::
Argininosuccinate lyase
Experimental Method:
Predicted structure
Sequence source:
BORDETELLA PARAPERTUSSIS (STRAIN 12822 / ATCC BAA-587 / NCTC 13253)

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 Q7WAE4 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q7WAE4|ARLY_BORPA Argininosuccinate lyase OS=Bordetella parapertussis (strain 12822 / ATCC BAA-587 / NCTC 13253) OX=257311 GN=argH PE=3 SV=2 MANTSHSSQDQFANKAQAWSARFSEPVSDLVKRYTASVDFDKRMARHDIRGSLAHADMLAAQGIISAQDLADIQRGMQQILSEIDAGSFQWLLDLEDVHLNIEKRLVELVGDAGKRLHTGRSRNDQVATDIRLWLRDEIDTLVDLLRQLRHALATVALENAATIMPGFTHLQVAQPVTFGHHLLAYAEMFGRDAERLADCRRRVNRLPLGAAALAGTSYPIDRERVARTLGFDGVCRNSLDAVSDRDFGIEFCAAGALIMTHISRLSEELVLWMSPRVGFIDLADRFCTGSSIMPQKKNPDVPELARGKTGRVNGHLVALLTLMKGQPLAYNKDNQEDKEGLFDTADTLRDTLTIFADMAGGIKVKADNMRAAALQGFATATDLADYLVKRGLPFRDAHEIVAHAVRDCEQRGCDLADLSLADLQAYHPSIGEDIHQVLTLEGSVAARKHIGGTAPERVREEAQRVLAETAGA

MOLNFT AF v1_w

NFT data

MOLNFT Q7WAE4 research and analysis (ICN3D with VR)

FASTA sequences

We use cookies on this site to enhance your user experience