Q66FB7 AF NFT

DNA mismatch repair protein MutL

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicztdvfyigubx26t6mjgpltt3frirjrpafgvvxeruyaqoslkoiiha/metadata.json
IPFS of structure file: 
bafybeigf7yji5xdv7her23p56ec4i3njob3husbivrpyoatylj66izogga/AF-Q66FB7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xf0A45b430E1927461EA54F413Bf6106407F444D6
First NFT owner address (bech32): 
genesis17zj9kscwryn5v849faqnhassvsrlg3xkpyqmgn
Current NFT ID #253953 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
253953
Accession (UniProt):
Q66FB7
Name::
DNA mismatch repair protein MutL
Experimental Method:
Predicted structure
Sequence source:
YERSINIA PSEUDOTUBERCULOSIS SEROTYPE I (STRAIN IP32953)

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 Q66FB7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q66FB7|MUTL_YERPS DNA mismatch repair protein MutL OS=Yersinia pseudotuberculosis serotype I (strain IP32953) OX=273123 GN=mutL PE=3 SV=1 MPIQILPPQLANQIAAGEVVERPASVVKELVENSLDAGATRIDIDIERGGAKLIRIRDNGCGISKDDLALALARHATSKISSLEDLEAILSMGFRGEALASISSVSRLILTSRTAEQSEAWQAYAEGRDMAVTIKPAAHPVGSTLEVLDLFYNTPARRKFMRTEKTEFGHIDEVVRRIALARFDVAINLNHNGKLIRQYRAAPDPAQHERRLASICGPAFLQHALAIAWQHGDLNIHGWVADPAASHTLSEMQYCYVNNRMMRDRLINHAIRQAYQDRLNDAQQPAYVLYLDIDPHQVDVNVHPAKHEVRFHQARLVHDFIYQAVTAVLQQTNAPILNISEEGEVDAPRWQQENRVAAGTNKYAQPEAAKSSAAEQAVARERSSARERAAPAYKEDHPYQKQQGELYRQLLQPSAAAKPATSPAAIPASSVSSPSIPVQRITQAEEPLHGDNYSFGRVLTVFPPCYALIEYQGGVALLSLAVAERWLKQAQLSPPEEGLRPQPLLIPLKITLDKNEIAACQNHEKLLITMGIELSVEQGRATLRAVSLPLRQQNLQKLIPELLGYLSQHEEISPDTLATWLARHLGSEHEVWNVSQAIQLLTEVERLCPQLVQSPPAGLLQPIDIKAALATLTHE