B1YLN8 AF NFT

Probable glycine dehydrogenase (decarboxylating) subunit 2

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeiaevafxqtrycchfstw56l3szq5y5jqsjzfbhoqnmywx2r5qvhzw4e/metadata.json
IPFS of structure file: 
bafybeibjwf6xwn663ucjnfiojhsujqkyinnigxrs6c4hjaziv6rgajvf3a/AF-B1YLN8-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xc4D46881AFD81F838F24Bb4F822bcC9938d48395
First NFT owner address (bech32): 
genesis1cn2x3qd0mq0c8reyhd8cy27vnyudfqu4apsgvw
Current NFT ID #145094 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
145094
Accession (UniProt):
B1YLN8
Name::
Probable glycine dehydrogenase (decarboxylating) subunit 2
Experimental Method:
Predicted structure
Sequence source:
Exiguobacterium sibiricum (strain DSM 17290 / CIP 109462 / JCM 13490 / 255-15)

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 B1YLN8 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B1YLN8|GCSPB_EXIS2 Probable glycine dehydrogenase (decarboxylating) subunit 2 OS=Exiguobacterium sibiricum (strain DSM 17290 / CIP 109462 / JCM 13490 / 255-15) OX=262543 GN=gcvPB PE=3 SV=1 MHKTSEQTLIFEISKAGRVAYSLPLPTVDEVAAEELLPAHLLRQEDVELPEVSELDLVRHYTALSNRNHGVDSGFYPLGSCTMKYNPKINEDMARLPGFAHIHPLQPVESVQGALGLMYDLQEKLAVITGMDEVTLQPAAGAHGEWTGLMLIKAYHHARGDFKRTKVLVPDSAHGTNPASASVAGFDTVTVLSDERGLVDLADLKSKVGEDTAALMLTNPNTLGLFESDIVEIAKAVHEAGGKLYYDGANSNAIMGIARPGDMGFDVVHLNLHKTFTGPHGGGGPGSGPVGVKKDLIPYLPKPIVAKTAEGFVLDYDRPESIGRVKPFYGNFGINVRAYSYIRTMGGAGLARVSKEAVLNANYMLARLKGAYDAPYDVYCKHEFVLSGRRQKALGVRTLDIAKRLLDFGYHPPTIYFPLNVEECIMIEPTETESKETLDDFCDAMLQIAKEVEETPDVVLNAPHTTVVKRMDETLAARKPILRYQPKQEVHV