ArticleScientific reports2022
ProteinGLUE multi-task benchmark suite for self-supervised protein modeling.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Large-scale multi-omic biosequence transformers for modeling protein-nucleic acid interactions.PloS one · 2026Article
- Large-Scale Multi-omic Biosequence Transformers for Modeling Protein-Nucleic Acid Interactions.ArXiv · 2025Article
- Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review.JMIR medical informatics · 2025Article
- Protein sequence modelling with Bayesian flow networks.Nature communications · 2025Article
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- Protein engineering in the deep learning era.mLife · 2024Review
- PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications.Journal of cheminformatics · 2024Article
- Article
- Evaluating generalizability of artificial intelligence models for molecular datasets.bioRxiv : the preprint server for biology · 2024Article
- ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction.bioRxiv : the preprint server for biology · 2023Article
- Ten quick tips for sequence-based prediction of protein properties using machine learning.PLoS computational biology · 2022Article
- Collectively encoding protein properties enriches protein language models.BMC bioinformatics · 2022Article
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6 authors.
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Abstract
Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue .
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