ArticleBioinformatics advances2021
Light attention predicts protein location from the language of life.
Article in Bioinformatics advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.
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Who cites it
69 citing papers in PubMed.
- EmmaEmb: A quantitative framework for analyzing embedding spaces in molecular biology.Patterns (New York, N.Y.) · 2026Article
- Developing SCL2205: a protein sequence-based spatial modelling dataset for the protein language model frontier.Bioinformatics (Oxford, England) · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- Technologies to measure and modulate protein subcellular localization.Nature reviews. Molecular cell biology · 2026Review
- A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.Nature methods · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Predicting Peptide Aggregation with Protein Language Model Embeddings.Journal of chemical information and modeling · 2026Article
- Out-of-distribution generalization enhances protein function annotation for low-homology sequences.Briefings in bioinformatics · 2026Article
- pLM-Repeat: Exploiting the sequence representations of protein language models for sensitive repeat detection.Protein science : a publication of the Protein Society · 2026Article
- BioGraphX: bridging the sequence-structure gap via physicochemical graph encoding for interpretable subcellular localization prediction.Bioinformatics advances · 2026Article
- Supervised Learning of Protein Melting Temperature: Cross-Species vs. Species-Specific Prediction.Proteins · 2025Article
- DeepCOI: a large language model-driven framework for fast and accurate taxonomic assignment in animal metabarcoding.Genome biology · 2025Article
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
- Paying attention to attention: High attention sites as indicators of protein family and function in language models.PLoS computational biology · 2025Article
- Application of Protein Structure Encodings and Sequence Embeddings for Transporter Substrate Prediction.Molecules (Basel, Switzerland) · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Pool PaRTI: a PageRank-based pooling method for identifying critical residues and enhancing protein sequence representations.Bioinformatics (Oxford, England) · 2025Article
- Prediction of protein subcellular localization in single cells.Nature methods · 2025Article
- Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Summary: Although knowing where a protein functions in a cell is important to characterize biological processes, this information remains unavailable for most known proteins. Machine learning narrows the gap through predictions from expert-designed input features leveraging information from multiple sequence alignments (MSAs) that is resource expensive to generate. Here, we showcased using embeddings from protein language models for competitive localization prediction without MSAs. Our lightweight deep neural network architecture used a softmax weighted aggregation mechanism with linear complexity in sequence length referred to as light attention. The method significantly outperformed the state-of-the-art (SOTA) for 10 localization classes by about 8 percentage points (Q10). So far, this might be the highest improvement of Availability and implementation: The novel models are available as a web-service at http://embed.protein.properties. Code needed to reproduce results is provided at https://github.com/HannesStark/protein-localization. Predictions for the human proteome are available at https://zenodo.org/record/5047020. Supplementary information: Supplementary data are available at
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Registered trials
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.