Deep learning methods for protein function prediction
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Abstract
Predicting protein function from protein sequence, structure, interaction, and other relevant information is important for generating hypotheses for biological experiments and studying biological systems, and therefore has been a major challenge in protein bioinformatics. Numerous computational methods had been developed to advance protein function prediction gradually in the last two decades. Particularly, in the recent years, leveraging the revolutionary advances in artificial intelligence (AI), more and more deep learning methods have been developed to improve protein function prediction at a faster pace. Here, we provide an in‐depth review of the recent developments of deep learning methods for protein function prediction. We summarize the significant advances in the field, identify several remaining major challenges to be tackled, and suggest some potential directions to explore. The data sources and evaluation metrics widely used in protein function prediction are also discussed to assist the machine learning, AI, and bioinformatics communities to develop more cutting‐edge methods to advance protein function prediction.
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Self-citation
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- Authors
- Boadu, F., Lee, A., & Cheng, J.
- Affiliations
- University of Missouri (US)
- Corresponding author
- Cheng, Jianlin
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2024-07-12
- Volume
- 25
- Issue
- 1-2
- Pages
- e2300471-e2300471
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- PMC
- PMC11735672
- OpenAlex
- W4400576356
- Cited by
- 53
- References
- 103
- Citations by year
- 2026: 25 · 2025: 23 · 2024: 3 · 2022: 1 · 2012: 1
- FWCI
- 2.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- PROTEOMICS (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Division of Biological Infrastructure (2308699)
- National Science Foundation (DBI2308699, CCF2343612)
- National Institutes of Health (R01GM093123, R01GM146340)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Keywords
- protein function prediction, deep learning, protein sequences, protein structure, protein-protein interaction, evaluation metrics, data sources
- MeSH terms
- Deep Learning, Humans, Proteins, Computational Biology, Databases, Protein
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