PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction

Exploring foci of: bioRxiv (Cold Spring Harbor Laboratory) PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction November 2023 • Shuai Zeng, Duolin Wang, Dong Xu Abstract Signal peptides (SP) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) provides a new opportunity for SP prediction, especially for the categories with limited annotated data. We present a Parameter-Efficient Fine-Tuning (PEFT) framework for SP prediction, PEFT-SP, to effectively utilize pre-trained PLMs. We implanted low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments … Open Article Page

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