Q5AHZ7 AF NFT

Mediator of RNA polymerase II transcription subunit 17

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeia65fcivmlubv5lllnsyl7gu6rvk5y3nl4g6xctikwciioujzmz2m/metadata.json
IPFS of structure file: 
bafybeiactuuqu4mr7h2wn4yho4rgllwd3swotf3rgvga5oaa3ggfdal6ai/AF-Q5AHZ7-F1-model_v2.pdb
First NFT owner address (eip-55): 
0xbCEB7C33AF4075d7025591e4E6639A681c12E6aD
First NFT owner address (bech32): 
genesis1hn4hcva0gp6awqj4j8jwvcu6dqwp9e4dxguva6
Current NFT ID #228863 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
228863
Accession (UniProt):
Q5AHZ7
Name::
Mediator of RNA polymerase II transcription subunit 17
Experimental Method:
Predicted structure
Sequence source:
CANDIDA ALBICANS (STRAIN SC5314 / ATCC MYA-2876)

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 Q5AHZ7 research and analysis (ICN3D with VR)

FASTA sequences

>sp|Q5AHZ7|MED17_CANAL Mediator of RNA polymerase II transcription subunit 17 OS=Candida albicans (strain SC5314 / ATCC MYA-2876) OX=237561 GN=SRB4 PE=3 SV=3 MVEKQFNIDLELNDTGHIDPFLQDEDKLKLEELIPRILFERKSFLNVTEDSLRKEIDNSLKISEEDALDTEESREDTVEADQQEVFNKHKFELSKNINNALNETQLSLDFVSLLISSVKPSLAKSTISPHLSKFVKPTSLNSDRLGQDSNDNQESKATDSFGQGWKLESLGKITDLFREASTNLNDQVIKERRYWNMINLVLANDEVLFRMRDPQNNARAIGVKYGYGDSGSNFHDQGLALLRKDNQTGEISFHPISSINNAKIVEKVSRFIRVKILSQIDGDYMLTGQSIFNFDFEKSKQSIINDIEKARFFLFEEDLFHQLIREAKLLVNYNVSIISNKIIIEINNIIIEIESIVYDELNEEELENYYQNVNEYSTLHNKKCQLILNYLKLMLCCYYKYNLKLKQKVPTALTKWKQSNSHPLILRPLVGNMRHELNLLNMKSVLDRLMHAHESELSYSKLDVEKFINLATRSKKQNPFQKSIEKPISKFHLVLCNKTSNMLDVNIQLTTNESFVNLIINMTIIRFETEDDFKNNVNGINVLQLGFSDFNEIEECLDWSIQNFV