Evidence map›Paper›PMID 42286792›Full record

ArticleBioinformatics (Oxford, England)2026

PEPE: scalable extraction of multi-modal protein language model representations.

Jahn Zhong, Niccolò Cardente, Geir Kjetil Sandve, Habib Bashour, Maria Francesca Abbate, Victor Greiff

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jahn ZhongDepartment of Immunology, University of Oslo, Oslo, Norway.ORCID 0000-0002-7247-1392
Niccolò CardenteDepartment of Immunology, University of Oslo, Oslo, Norway.ORCID 0009-0009-4021-1631
Geir Kjetil SandveScientific Computing and Machine Learning section, Department of Informatics, University of Oslo, Oslo, Norway.ORCID 0000-0002-4959-1409
Habib BashourDepartment of Immunology, University of Oslo, Oslo, Norway.ORCID 0000-0001-6660-1843
Maria Francesca AbbateDepartment of Immunology, University of Oslo, Oslo, Norway.ORCID 0009-0006-1718-8769
Victor GreiffDepartment of Immunology, University of Oslo, Oslo, Norway.ORCID 0000-0003-2622-5032

Funding

Norwegian Cancer Society #215817Research Council of Norway projects #300740Research Council of Norway projects #331890
6 · The paper itself

Abstract

summaryProtein language models (PLMs) capture intricate amino-acid dependencies, producing embeddings that encode rich structural, functional, and evolutionary information. Despite their potential, current extraction workflows rely on arbitrary choices, with respect to embedding layer, pooling, and padding, that frequently yield suboptimal representations for feature extraction and downstream analyses. Large-scale embedding generation is further limited by inefficiencies in computation and memory: (i) accumulating all model outputs in memory before writing to disk causes severe bottlenecks, and (ii) repeatedly embedding identical sequences to extract different modes introduces redundant computation and drastically reduces throughput and scalability. We introduce PEPE (Parallel Extraction for Protein Embeddings), a command-line tool and Python library that enables efficient, high-throughput, and multimodal extraction from protein language models. PEPE's parallelized and streaming-based architecture achieves runtimes several orders of magnitude faster than sequential approaches. Unlike conventional methods-whose peak memory usage scales linearly with output size and fails when memory capacity is exceeded-PEPE maintains stable, low memory consumption, enabling multimodal embedding extraction even beyond available RAM. PEPE supports a wide range of state-of-the-art and custom PLMs through a simple, flexible interface. By combining scalability, robustness, and ease of use, PEPE allows researchers to generate massive, information-rich embedding datasets efficiently, and facilitate the discovery of optimal representations for structural, functional, and evolutionary downstream tasks. By streamlining the generation of diverse embedding configurations, PEPE provides researchers with the necessary data to identify high-performing latent states for specific biological contexts without requiring additional computational resources. AVAILABILITY AND IMPLEMENTATION: PEPE is a command-line tool written in Python and published under MIT license. The source code and documentation are available at https://github.com/csi-greifflab/pepe-cli. PEPE is also available for installation from PyPI under https://pypi.org/project/pepe-cli and deposited on Zenodo at https://zenodo.org/records/20268104.

Indexed as

Computational BiologyProteinsSoftwareProteins

Identifiers

PMID42286792
PMCPMC13326402

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.