A3M4T0 Probable transcriptional regulatory protein A1S_1496 MolNFT, Molecular NFT on GenesisL1

Probable transcriptional regulatory protein A1S_1496

MOLNFT AF v1 smart contract in GenesisL1 blockchain:

0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD

IPFS of NFT metadata:

bafybeifk3hkfloiwauj7duksk42auiogcitfjbeuaew42uhcvbwzk7vhfy/metadata.json

IPFS of structure file:

bafybeia44xn4k4pghlw44fads56242ho4yopt4a6phqf5tzdfmrg6nbdny/AF-A3M4T0-F1-model_v2.pdb

First NFT owner address (eip-55):

0x3048035c88f5559a84b15475ED6c0013b3E8bF25

First NFT owner address (bech32):

genesis1xpyqxhyg742e4p9323676mqqzwe730e9qezsxd

Current NFT ID #507349 owner (web3):

0x2FCae8136DBc761f6F9310fF638e0F8Ad73E2B9d

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
507349
Accession (UniProt):
A3M4T0
Name::
Probable transcriptional regulatory protein A1S_1496
Experimental Method:
Predicted structure
Sequence source:
ACINETOBACTER BAUMANNII (STRAIN ATCC 17978 / CIP 53.77 / LMG 1025 / NCDC KC755 / 5377)

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 A3M4T0 research and analysis (ICN3D with VR)

FASTA sequences

>sp|A3M4T0|Y1496_ACIBT Probable transcriptional regulatory protein A1S_1496 OS=Acinetobacter baumannii (strain ATCC 17978 / CIP 53.77 / LMG 1025 / NCDC KC755 / 5377) OX=400667 GN=A1S_1496 PE=3 SV=2 MAGHSKWANIKHRKAKQDASRGKVFTKYIREIVTAAKLGGADPASNPRLRAVVEKALSVNMTRDTINRAIQRGVGGEDNDDLKEVTYEGYGVGGVAVLVETMTDNLNRTVPDVRHCFSKTNGNLGTAGSVAYLFTKRGEITFDDISLEDKIMDVALEAGAEDIEVSEDEILVITSPETFGEVQDALAAAGLKSDNAEVVMSPSTKAEITDIDQAKQVMKLIDMLEDLDDVQNVYTNVEFSDEVLAQLDA

A3M4T0 AF NFT

MOLNFT AF v1

NFT data

MOLNFT A3M4T0 research and analysis (ICN3D with VR)

FASTA sequences

We use cookies on this site to enhance your user experience