ArticleGenome research2024
Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- Democratizing protein language model training, sharing and collaboration.Nature biotechnology · 2026Article
- Bridging linguistic reasoning and biophysical reality toward peptide engineering via instruction-tuned language modeling.Bioinformatics (Oxford, England) · 2026Article
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- Applications of large-scale artificial intelligence models in bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- A Co-attention Mechanism of Jointly Capturing Sequence and Structure Features for Signal Peptide Prediction.Computational and structural biotechnology journal · 2026Article
- Transfer learning on protein language models improves antimicrobial peptide classification.Scientific reports · 2025Article
- In silico prediction of variant effects: promises and limitations for precision plant breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- ProtLoc-GRPO: Cell line-specific subcellular localization prediction using a graph-based model and reinforcement learning.bioRxiv : the preprint server for biology · 2025Article
- OPUS-BFactor: Predicting Protein B-Factor with Sequence and Structure Information.Molecules (Basel, Switzerland) · 2025Article
- Artificial Intelligence-Assisted Breeding for Plant Disease Resistance.International journal of molecular sciences · 2025Review
- SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction.Journal of chemical information and modeling · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- Enhancing Structure-Aware Protein Language Models with Efficient Fine-Tuning for Various Protein Prediction Tasks.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Comprehensive Prediction of Protein Localization and Signal Peptides Using MULocDeep.Methods in molecular biology (Clifton, N.J.) · 2025Article
- PEZy-miner: An artificial intelligence driven approach for the discovery of plastic-degrading enzyme candidates.Metabolic engineering communications · 2024Article
Corrections and comments
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4 authors.
Funding
Abstract
Signal peptides (SPs) play a crucial role in protein translocation in cells. The development of large protein language models (PLMs) and prompt-based learning provide 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 pretrained PLMs. We integrated low-rank adaptation (LoRA) into ESM-2 models to better leverage the protein sequence evolutionary knowledge of PLMs. Experiments show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum Matthews correlation coefficient (MCC) gain of 87.3% for SPs with small training samples and an overall MCC gain of 6.1%. Furthermore, we also employed two other PEFT methods, prompt tuning and adapter tuning, in ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using adapter tuning can also improve the state-of-the-art results by up to 28.1% MCC gain for SPs with small training samples and an overall MCC gain of 3.8%. LoRA requires fewer computing resources and less memory than the adapter tuning during the training stage, making it possible to adapt larger and more powerful protein models for SP prediction.
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