O43032 AF NFT

Signal recognition particle receptor subunit alpha homolog

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeialeczxue25xybmgs5zv4glt3x7enimcr5rfqct5gay4322k5vlyi/metadata.json
IPFS of structure file: 
bafybeicfee6llpc7hxbuim6w7ao3mf4iioar33vopuhfigz4bda2vhrvs4/AF-O43032-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xFB6bF9B65B1865F07ACb06897Cd13401125BdF0f
First NFT owner address (bech32): 
genesis1ld4lndjmrpjlq7ktq6yhe5f5qyf9hhc0xwpyp4
Current NFT ID #436832 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
436832
Accession (UniProt):
O43032
Name::
Signal recognition particle receptor subunit alpha homolog
Experimental Method:
Predicted structure
Sequence source:
SCHIZOSACCHAROMYCES POMBE (STRAIN 972 / ATCC 24843)

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 O43032 research and analysis (ICN3D with VR)

FASTA sequences

>sp|O43032|SRPR_SCHPO Signal recognition particle receptor subunit alpha homolog OS=Schizosaccharomyces pombe (strain 972 / ATCC 24843) OX=284812 GN=srp101 PE=3 SV=1 MIDLFAIVNKGGIVLWKKTNSLVNLKCLQVLFHEAFLSEQRTVNNTVTFDRYTMQYQEATQYSIVFVVVFQDLKCMAYSQSLLNSAHNIFLNLFKEKLEDRQVPNEAEVEKLFAPIFNRKSAQLENETDTKSLPVEANNDNSARKKNEYEMKKKGAQSKQTNAPKKGKKQLRKWDDQITEEEQAALNYSSQASSASQTVDNSQLSSIVGNNNKFQKTGSGDVIISDLEMDPNQTISNKSASSAFSLFSNLIGGKYLKEEDLSPILKQMQEHLTKKNVANSIALELCESVKASLINKKVGSFDTVKNTVNKAFRDRLTQILTPSTSLDLLHSIRSVRKNENRPYTISLIGVNGVGKSTTLAKIAYWLLSNNFRILVAACDTFRSGAIEQLGVHVKNLQSLKGSSIELFAQGYGKDSSFVVKNAVEYAKQNSFDVILIDTAGRRHNDQRLMGSLEKFTKATKLDKIFQVAEALVGTDSLAQAKHFQASLYHRPLDGFIISKVDTVGQLVGVMVGMVYAVRVPIIFVGIGQTYSDLRTLSVDWVVDQLMK