AAontology: An Ontology of Amino Acid Scales for Interpretable Machine Learning
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Abstract
Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology-a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations-using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design.
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Flagged first, then by citations
- Authors
- Breimann, S., Kamp, F., Steiner, H., & Frishman, D.
- Affiliations
- German Center for Neurodegenerative Diseases (DE)
- Ludwig-Maximilians-Universität München (DE)
- Technical University of Munich (DE)
- Corresponding author
- Frishman, Dmitrij
- Venue
- Journal of Molecular Biology
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 436
- Issue
- 19
- Pages
- 168717
- ISSN
- 0022-2836 (ISSN-L)
- OpenAlex
- W4400953933
- Cited by
- 15
- References
- 13
- Citations by year
- 2026: 10 · 2025: 5
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Molecular Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- DZNE Pub (submittedVersion, cc-by)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fractal and DNA sequence analysis
- Keywords
- aa index, ontology (information science), amino acid physicochemical properties, protein prediction, model interpretability, scale classification, clustering, protein function, mutation analysis
- MeSH terms
- Machine Learning, Amino Acids, Proteins, Cluster Analysis, Computational Biology, Databases, Protein
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