Aggregating residue-level protein language model embeddings with optimal transport Article Swipe
Navid Naderializadeh
,
Rohit Singh
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioadv/vbaf060
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1093/bioadv/vbaf060
Motivation Protein language models (PLMs) have emerged as powerful approaches for mapping protein sequences into embeddings suitable for various applications. As protein representation schemes, PLMs generate per-token (i.e. per-residue) representations, resulting in variable-sized outputs based on protein length. This variability poses a challenge for protein-level prediction tasks that require uniform-sized embeddings for consistent analysis across different proteins. Previous work has typically used average pooling to summarize token-level PLM outputs, but it is unclear whether this method effectively prioritizes the relevant information across token-level representations. Results We introduce a novel method utilizing optimal transport to convert variable-length PLM outputs into fixed-length representations. We conceptualize per-token PLM outputs as samples from a probabilistic distribution and employ sliced-Wasserstein distances to map these samples against a reference set, creating a Euclidean embedding in the output space. The resulting embedding is agnostic to the length of the input and represents the entire protein. We demonstrate the superiority of our method over average pooling for several downstream prediction tasks, particularly with constrained PLM sizes, enabling smaller-scale PLMs to match or exceed the performance of average-pooled larger-scale PLMs. Our aggregation scheme is especially effective for longer protein sequences by capturing essential information that might be lost through average pooling. Availability and implementation Our implementation code can be found at https://github.com/navid-naderi/PLM_SWE.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioadv/vbaf060
- https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbaf060/62493443/vbaf060.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408717486
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408717486Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioadv/vbaf060Digital Object Identifier
- Title
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Aggregating residue-level protein language model embeddings with optimal transportWork 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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2024-12-26Full publication date if available
- Authors
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Navid Naderializadeh, Rohit SinghList of authors in order
- Landing page
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https://doi.org/10.1093/bioadv/vbaf060Publisher landing page
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https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbaf060/62493443/vbaf060.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformaticsadvances/advance-article-pdf/doi/10.1093/bioadv/vbaf060/62493443/vbaf060.pdfDirect OA link when available
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Pooling, Embedding, Computer science, Security token, Variable (mathematics), Representation (politics), Theoretical computer science, Data mining, Artificial intelligence, Mathematics, Political science, Politics, Law, Computer security, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Our | 182, 206 |
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| abstract_inverted_index.PLMs | 25, 171 |
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| abstract_inverted_index.space. | 132 |
| abstract_inverted_index.tasks, | 163 |
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| abstract_inverted_index.enabling | 169 |
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