B4SCX5 AF NFT

DNA ligase

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeibt5tln45y66bkv6544vyzxngr53224gko5argrp27plypdjx4q6i/metadata.json
IPFS of structure file: 
bafybeieo7cxvrz6yytgzvdewn5mdj533oomxyo3qpugnbpp5yb3fxgtcna/AF-B4SCX5-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x7FC89EC214E88EEb38fAa127e1E19d1b331AC902
First NFT owner address (bech32): 
genesis10lyfass5az8wkw865yn7rcvarve34jgzt72gd4
Current NFT ID #102692 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1_w

NFT id:
102692
Accession (UniProt):
B4SCX5
Name::
DNA ligase
Experimental Method:
Predicted structure
Sequence source:
Pelodictyon phaeoclathratiforme (strain DSM 5477 / BU-1)

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 B4SCX5 research and analysis (ICN3D with VR)

FASTA sequences

>sp|B4SCX5|DNLJ_PELPB DNA ligase OS=Pelodictyon phaeoclathratiforme (strain DSM 5477 / BU-1) OX=324925 GN=ligA PE=3 SV=1 MTDIIRAAEAIAQLRREIERHTHLYYVEAKPELSDFEFDQLLDQLITLERQFPQLLTPDSPSQRVGGAITREFPAVQHREPMLSLSNSYSITEVEEFYNRVRKLLALEGVVEQEMVAELKFDGVAISLIYQDGILVRGATRGDGRQGDDITANLKTVATIPLRLEETLVAALQGEERAIEVRGEVFMRKEDFERLNESRPDEDRFANPRNATAGSLKLQDSGEVARRSMSFVAYYLKGLKDESTPHIHRLELLARLGFFTGDHYRLCNTLEEINTYIAEWAEKRWQLPYETDGVVLKLNDVPFREKLGATAKSPRWAIAYKYPAQQARSVLQNVLFQVGRLGTITPVAELEPVLLAGSTVSRSTLHNFDEIERLGLMLRDRVIIEKSGEVIPKVVRTLFEERPADAMPILPPTHCPSCGAPLVRPENEVSYSCPNEEECPAQIKGRLLHFASRNAMDIQTLGDALVEQLVAKGLVKDPGDLYFLQEPQLEKLERMGPKSAQNILRALEKSREKSYERLLYALGIRHVGRATARELSQACPSIDLLREASEEQLATIPDIGPVVARSIIDYFAKPSWPHLLEKLRTAALQLSASEPKEQVNRNFEGVTVLFTGSLERYDRQKASELVLERGGRVVGSVSKKTGMVVAGQDPGSKLQKANKLGVRVVSEDEFEAML