ArticleBioinformatics (Oxford, England)2024
Insights into the inner workings of transformer models for protein function prediction.
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 11 papers.
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Who cites it
11 citing papers in PubMed, 15 citations in OpenAlex.
- HaloTag-Based FACS-Compatible Assay for Intracellular Haloalkane Detection with a Potential Application to Halogenase Screening.Analytical chemistry · 2026Article
- Opening the black box: insights into ubiquitin-mediated control of innate antiviral immunity and AI-enhanced therapeutics.Frontiers in immunology · 2026Review
- The Human Omnibus of Targetable Pockets.Journal of cheminformatics · 2025Article
- InterPLM: discovering interpretable features in protein language models via sparse autoencoders.Nature methods · 2025Article
- Multistage attention-based extraction and fusion of protein sequence and structural features for protein function prediction.Bioinformatics (Oxford, England) · 2025Article
- Focused learning by antibody language models using preferential masking of non-templated regions.Patterns (New York, N.Y.) · 2025Article
- Review
- PIPENN-EMB ensemble net and protein embeddings generalise protein interface prediction beyond homology.Scientific reports · 2025Article
- Fine-tuning of conditional Transformers improvesComputational and structural biotechnology journal · 2025Article
- ESM-scan-A tool to guide amino acid substitutions.Protein science : a publication of the Protein Society · 2024Article
- The promises of large language models for protein design and modeling.Frontiers in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
motivationWe explored how explainable artificial intelligence (XAI) can help to shed light into the inner workings of neural networks for protein function prediction, by extending the widely used XAI method of integrated gradients such that latent representations inside of transformer models, which were finetuned to Gene Ontology term and Enzyme Commission number prediction, can be inspected too.
resultsThe approach enabled us to identify amino acids in the sequences that the transformers pay particular attention to, and to show that these relevant sequence parts reflect expectations from biology and chemistry, both in the embedding layer and inside of the model, where we identified transformer heads with a statistically significant correspondence of attribution maps with ground truth sequence annotations (e.g. transmembrane regions, active sites) across many proteins. AVAILABILITY AND IMPLEMENTATION: Source code can be accessed at https://github.com/markuswenzel/xai-proteins.
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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.