A8F8M6 AF NFT

Probable glycine dehydrogenase (decarboxylating) subunit 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiay2cegc2q4yxzyiudw4ag5cyshhydw3rd76bz3wgjn6fegt5bojm/metadata.json
IPFS of structure file: 
bafybeiapkfy6z6tbsmf3bfwksx6orohzqf7udc6ubnmjfgq4imnklu27rm/AF-A8F8M6-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x9073C46348542FfE4327c3F363F9B51B22058e75
First NFT owner address (bech32): 
genesis1jpeugc6g2shluse8c0ek87d4rv3qtrn44vl0u5
Current NFT ID #145122 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
145122
Accession (UniProt):
A8F8M6
Name::
Probable glycine dehydrogenase (decarboxylating) subunit 2
Experimental Method:
Predicted structure
Sequence source:
PSEUDOTHERMOTOGA LETTINGAE (STRAIN ATCC BAA-301 / DSM 14385 / NBRC 107922 / TMO)

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 A8F8M6 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A8F8M6|GCSPB_PSELT Probable glycine dehydrogenase (decarboxylating) subunit 2 OS=Pseudothermotoga lettingae (strain ATCC BAA-301 / DSM 14385 / NBRC 107922 / TMO) OX=416591 GN=gcvPB PE=3 SV=1 MIIFEKSVPGRKAYRLPDEELQRIDPVFPEHLKRTRPLRLPELSEPDVVRHYTALAEKNYSVDKGFYPLGSCTMKYNPKLNEYVAGLEGFTDIHPYQPWESVQGALQVMYELKEFLCEITGMDEMTLQPAAGAHGELTGMLIVRAYHLSRNDKKRHVALVPDSAHGTNPASAAMAGFDVVEIKSTEDGLVDLEQLENHLNDEIAVLMLTNPNTLGLFEKDIVKIAEKVHQAGALLYYDGANLNAIMGKIKPGEMGFDIVHLNLHKTFSAPHGMGGPGSGPVGVKAFLSEFLPIPIIRKDGDKYYPDFKLPNSIGRTRSFYGNFLVLLKAYVYILSMGKDGLTRASEMAVLNANYLRSLISKFLKIASPGICMHEFVVDGSQFVKETGVKILDVAKRILDYGLHAPTVYFPLIVHEDMMIEPTETENKNTLDYFAKVLEKIVEEAKKTPEVVKTAPHTTPVKRLDDITATKKPVYRYRLS