ArticleBioinformatics advances2022
The field of protein function prediction as viewed by different domain scientists.
Article in Bioinformatics advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- How not to be seen: predicting unseen enzyme functions using contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- On the state of protein function prediction: a report on the fourth CAFA challenge.bioRxiv : the preprint server for biology · 2026Article
- Advances in Protein Function Prediction from the Fifth CAFA Challenge.bioRxiv : the preprint server for biology · 2026Article
- How Not to be Seen: Predicting Unseen Enzyme Functions using Contrastive Learning.bioRxiv : the preprint server for biology · 2026Article
- Limitations of current machine learning models in predicting enzymatic functions for uncharacterized proteins.G3 (Bethesda, Md.) · 2025Article
- Protein functional site annotation using local structure embeddings.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Systematic Analysis ofGenes · 2025Article
- Functional profiling of the sequence stockpile: a protein pair-based assessment of in silico prediction tools.Bioinformatics (Oxford, England) · 2025Article
- Identification of type VI secretion system effector-immunity pairs using structural bioinformatics.Molecular systems biology · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Essential gene complement of Planctopirus limnophila from the bacterial phylum Planctomycetes.Nature communications · 2023Article
- COLLAPSE: A representation learning framework for identification and characterization of protein structural sites.Protein science : a publication of the Protein Society · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Experimental biologists, biocurators, and computational biologists all play a role in characterizing a protein's function. The discovery of protein function in the laboratory by experimental scientists is the foundation of our knowledge about proteins. Experimental findings are compiled in knowledgebases by biocurators to provide standardized, readily accessible, and computationally amenable information. Computational biologists train their methods using these data to predict protein function and guide subsequent experiments. To understand the state of affairs in this ecosystem, centered here around protein function prediction, we surveyed scientists from these three constituent communities. Results: We show that the three communities have common but also idiosyncratic perspectives on the field. Most strikingly, experimentalists rarely use state-of-the-art prediction software, but when presented with predictions, report many to be surprising and useful. Ontologies appear to be highly valued by biocurators, less so by experimentalists and computational biologists, yet controlled vocabularies bridge the communities and simplify the prediction task. Additionally, many software tools are not readily accessible and the predictions presented to the users can be broad and uninformative. We conclude that to meet both the social and technical challenges in the field, a more productive and meaningful interaction between members of the core communities is necessary. Availability and implementation: Data cannot be shared for ethical/privacy reasons. Supplementary information: Supplementary data are available at
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Registered trials
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