Peer-reviewedCC BY 4.0iScience
An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions
Related
- SPINET: Sheaf Protein Inverse Folding Network
- Learning inhibitor interactions from protein deep mutational scanning data and deep learning models
- Predicting protein structures at any folding stage from sequence
- The interaction between NC(p7)1-55 and p6 may regulate interactions with nucleic acids during assembly through modulation of Gag folding.
- Protein Folding Interference Enables Selective Cyclin D1 Degradation
BibTeX
@article{mao2026inverseproteinfolding,
title = {{An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions}},
author = {Sihong Mao and Siwei Meng and Ye Xiang},
journal = {iScience},
year = {2026},
month = oct,
doi = {10.1016/j.isci.2026.117693},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2589004226030749},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An inverse-protein-folding pre-trained model for predicting the mutational effects in protein-protein interactions
AU - Sihong Mao
AU - Siwei Meng
AU - Ye Xiang
PY - 2026
DA - 2026/10/01
JO - iScience
DO - 10.1016/j.isci.2026.117693
UR - https://linkinghub.elsevier.com/retrieve/pii/S2589004226030749
SN - 2589-0042
ER -