ArticleBioinformatics (Oxford, England)2024
Fine-tuning protein embeddings for functional similarity evaluation.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Quantifying uncertainty in protein representations across models and tasks.Nature methods · 2026Article
- Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viability.Scientific reports · 2026Article
- Learnable protein representations in computational biology for predicting drug-target affinity.Journal of cheminformatics · 2026Review
- Evaluating sequence and structural similarity metrics for predicting shared paralog functions.NAR genomics and bioinformatics · 2025Article
- AOP-DRL: A deep representation learning framework for the computational prediction of antioxidant peptides.Computational and structural biotechnology journal · 2025Article
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2 authors.
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Abstract
motivationProteins with unknown function are frequently compared to better characterized relatives, either using sequence similarity, or recently through similarity in a learned embedding space. Through comparison, protein sequence embeddings allow for interpretable and accurate annotation of proteins, as well as for downstream tasks such as clustering for unsupervised discovery of protein families. However, it is unclear whether embeddings can be deliberately designed to improve their use in these downstream tasks.
resultsWe find that for functional annotation of proteins, as represented by Gene Ontology (GO) terms, direct fine-tuning of language models on a simple classification loss has an immediate positive impact on protein embedding quality. Fine-tuned embeddings show stronger performance as representations for K-nearest neighbor classifiers, reaching stronger performance for GO annotation than even directly comparable fine-tuned classifiers, while maintaining interpretability through protein similarity comparisons. They also maintain their quality in related tasks, such as rediscovering protein families with clustering. AVAILABILITY AND IMPLEMENTATION: github.com/mofradlab/go_metric.
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