Q5IS75 Neuronal acetylcholine receptor subunit beta-3 MolNFT, Molecular NFT on GenesisL1

Neuronal acetylcholine receptor subunit beta-3

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeiafykmpyr3dyxbjoan2qnidzsofcjjl6q6dw7cvs3yfkz7yeadkea/metadata.json

IPFS of structure file:

bafybeihicbvuf4pmshrurhjfyzv2shqztifx5a3jgt6qpgxcvozphvj4jq/AF-Q5IS75-F1-model_v2.pdb

First NFT owner address (eip-55):

0xBF78C9477c2720e82674bE2b804A22DBB3A2463f

First NFT owner address (bech32):

genesis1hauvj3muyuswsfn5hc4cqj3zmwe6y33lxrgrr9

Current NFT ID #5869 owner (web3):

0x1ECE227C47465CF7Fe12bd8e27544d9998F96c20

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
5869
Accession (UniProt):
Q5IS75
Name::
Neuronal acetylcholine receptor subunit beta-3
Experimental Method:
Predicted structure
Sequence source:
PAN TROGLODYTES

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 Q5IS75 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q5IS75|ACHB3_PANTR Neuronal acetylcholine receptor subunit beta-3 OS=Pan troglodytes OX=9598 GN=CHRNB3 PE=2 SV=1 MLPDFMLVLIVLGIPSSATTGFNSIAENEDALLRHLFQGYQKWVRPVLHSNDTIKVYFGLKISQLVDVDEKNQLMTTNVWLKQEWTDHKLRWNPDDYGGIHSIKVPSESLWLPDIVLFENADGRFEGSLMTKVIVKSNGTVVWTPPASYKSSCTMDVTFFPFDRQNCSMKFGSWTYDGTMVDLILINENVDRKDFFDNGEWEILNAKGMKGNRRDGVYSYPFITYSFVLRRLPLFYTLFLIIPCLGLSFLTVLVFYLPSDEGEKLSLSTSVLVSLTVFLLVIEEIIPSSSKVIPLIGEYLLFIMIFVTLSIIVTVFVINVHHRSSSTYHPMAPWVKRLFLQKLPKLLCMKDHVDRYSSPEKEESQPVVKGKVLEKKKQKQLSDGEKVLVAFLEKAADSIRYISRHVKKEHFISQVVQDWKFVAQVLDRIFLWLFLIVSVTGSVLIFTPALKMWLHSYH

Q5IS75 AF NFT

MOLNFT AF v1

NFT data

MOLNFT Q5IS75 research and analysis (ICN3D with VR)

FASTA sequences

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