Cell Surface Protein Identification
Our advanced deep learning method predicts whether a protein is located at the cell surface based solely on its amino acid sequence. Leveraging the highly curated UniProt dataset, our model cross-checks its predictions with high-confidence subcellular localization data from the subcellular localization database.
Versatility and Adaptation
Our deep learning approach is designed to handle proteins of varying lengths, enhancing its ability to capture intricate sequence patterns. By fine-tuning the model specifically for our classification task, we improve its ability to distinguish between surface and non-surface proteins. This adaptation leverages in herent features of protein sequences, boosting the model’s discriminatory capabilities for applications in genomic therapies.
Ensemble Model for Enhanced Accuracy
The final ensemble model consists of 100 ESM-2 learners, obtained through rigorous cross-validation and hyperparameter optimization. This ensemble approach ensures robust and reliable predictions, making our technology highly effective for specialized applications in protein research and therapeutic development.
