Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction Article Swipe
Shuai Zeng
,
Duolin Wang
,
Lei Jiang
,
Dong Xu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.279132.124
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/gr.279132.124
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1101/gr.279132.124
- OA Status
- green
- Cited By
- 22
- References
- 45
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4401021891Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/gr.279132.124Digital Object Identifier
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Parameter-efficient fine-tuning on large protein language models improves signal peptide predictionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-26Full publication date if available
- Authors
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Shuai Zeng, Duolin Wang, Lei Jiang, Dong XuList of authors in order
- Landing page
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https://doi.org/10.1101/gr.279132.124Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/11529868Direct OA link when available
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Adapter (computing), Leverage (statistics), Biology, Computer science, Artificial intelligence, Machine learning, Computational biology, Operating systemTop concepts (fields/topics) attached by OpenAlex
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22Total citation count in OpenAlex
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2025: 18, 2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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