P10387 AF NFT

Glutenin, high molecular weight subunit DY10

MOLNFT AF v1 smart contract in GenesisL1 blockchain: 
0xBf7491af3407816DFa88a5EA4c82e8A2B1D721eD
IPFS of NFT metadata: 
bafybeicoeeeqsmwbpdowgs7omyw2ahmdlhuhkztt3nmmjdqfhawvpr3djq/metadata.json
IPFS of structure file: 
bafybeif2cnt75x6xdplooikuw4pazgxle5k3xamalpfibks5navenbqssm/AF-P10387-F1-model_v2.pdb
First NFT owner address (eip-55): 
0x8e0Ed1a2abB4f8A7c38D04F8e2b968fA53514393
First NFT owner address (bech32): 
genesis13c8drg4tknu20sudqnuw9wtglff4zsunn96gz6
Current NFT ID #152873 owner (web3):

First owner GALLERY | EVM EXPLORER | COSMOS EXPLORER

MOLNFT AF v1

NFT id:
152873
Accession (UniProt):
P10387
Name::
Glutenin, high molecular weight subunit DY10
Experimental Method:
Predicted structure
Sequence source:
TRITICUM AESTIVUM

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 P10387 research and analysis (ICN3D with VR)

FASTA sequences

>sp|P10387|GLT0_WHEAT Glutenin, high molecular weight subunit DY10 OS=Triticum aestivum OX=4565 GN=GLU-D1-2B PE=3 SV=1 MAKRLVLFAAVVIALVALTTAEGEASRQLQCERELQESSLEACRQVVDQQLAGRLPWSTGLQMRCCQQLRDVSAKCRSVAVSQVARQYEQTVVPPKGGSFYPGETTPLQQLQQGIFWGTSSQTVQGYYPGVTSPRQGSYYPGQASPQQPGQGQQPGKWQEPGQGQQWYYPTSLQQPGQGQQIGKGQQGYYPTSLQQPGQGQQGYYPTSLQHTGQRQQPVQGQQPEQGQQPGQWQQGYYPTSPQQLGQGQQPRQWQQSGQGQQGHYPTSLQQPGQGQQGHYLASQQQPGQGQQGHYPASQQQPGQGQQGHYPASQQQPGQGQQGHYPASQQEPGQGQQGQIPASQQQPGQGQQGHYPASLQQPGQGQQGHYPTSLQQLGQGQQTGQPGQKQQPGQGQQTGQGQQPEQEQQPGQGQQGYYPTSLQQPGQGQQQGQGQQGYYPTSLQQPGQGQQGHYPASLQQPGQGQPGQRQQPGQGQHPEQGKQPGQGQQGYYPTSPQQPGQGQQLGQGQQGYYPTSPQQPGQGQQPGQGQQGHCPTSPQQSGQAQQPGQGQQIGQVQQPGQGQQGYYPTSVQQPGQGQQSGQGQQSGQGHQPGQGQQSGQEQQGYDSPYHVSAEQQAASPMVAKAQQPATQLPTVCRMEGGDALSASQ