PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide Prediction Article Swipe
Shuai Zeng
,
Duolin Wang
,
Dong Xu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.04.565642
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.11.04.565642
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 show that PEFT-SP using LoRA enhances state-of-the-art results, leading to a maximum MCC2 gain of 0.372 for SPs with small training samples and an overall MCC2 gain of 0.048. Furthermore, we also employed two other PEFT methods, i.e., Prompt Tunning and Adapter Tuning, into ESM-2 for SP prediction. More elaborate experiments show that PEFT-SP using Adapter Tuning can also improve the state-of-the-art results with up to 0.202 MCC2 gain for SPs with small training samples and an overall MCC2 gain of 0.030. LoRA requires fewer computing resources and less memory compared to Adapter, making it possible to adapt larger and more powerful protein models for SP prediction.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.11.04.565642
- https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.11.04.565642.full.pdf
- OA Status
- green
- Cited By
- 13
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388399759
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388399759Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.11.04.565642Digital Object Identifier
- Title
-
PEFT-SP: Parameter-Efficient Fine-Tuning on Large Protein Language Models Improves Signal Peptide PredictionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-05Full publication date if available
- Authors
-
Shuai Zeng, Duolin Wang, Dong XuList of authors in order
- Landing page
-
https://doi.org/10.1101/2023.11.04.565642Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.11.04.565642.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2023/11/05/2023.11.04.565642.full.pdfDirect OA link when available
- Concepts
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Adapter (computing), Leverage (statistics), Computer science, Artificial intelligence, Machine learning, Computer hardwareTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
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2025: 6, 2024: 6, 2023: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5082428303 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I76835614 |
| citation_normalized_percentile.value | 0.89218477 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |