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Protein representations: Encoding biological information for machine learning in biocatalysis
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Abstract
Enzymes offer a more environmentally friendly and low-impact solution to conventional chemistry, but they often require additional engineering for their application in industrial settings, an endeavour that is challenging and laborious. To address this issue, the power of machine learning can be harnessed to produce predictive models that enable the in silico study and engineering of improved enzymatic properties. Such machine learning models, however, require the conversion of the complex biological information to a numerical input, also called protein representations. These inputs demand special attention to ensure the training of accurate and precise models, and, in this review, we therefore examine the critical step of encoding protein information to numeric representations for use in machine learning. We selected the most important approaches for encoding the three distinct biological protein representations — primary sequence, 3D structure, and dynamics — to explore their requirements for employment and inductive biases. Combined representations of proteins and substrates are also introduced as emergent tools in biocatalysis. We propose the division of fixed representations, a collection of rule-based encoding strategies, and learned representations extracted from the latent spaces of large neural networks. To select the most suitable protein representation, we propose two main factors to consider. The first one is the model setup, which is influenced by the size of the training dataset and the choice of architecture. The second factor is the model objectives such as consideration about the assayed property, the difference between wild-type models and mutant predictors, and requirements for explainability. This review is aimed at serving as a source of information and guidance for properly representing enzymes in future machine learning models for biocatalysis. • Robust protein representations serve as crucial links between biology and data science for machine learning in biocatalysis. • Fixed representations impose specific inherent biases on the protein encoding through rule-based descriptors. • Learned representations from self-supervised deep learning models offer valuable biological information for supervised tasks. • Despite the ability to capture the temporal dimensions, protein dynamics representations are underexplored for enzyme models. • Choosing an encoding strategy requires considering the “model setup” and “model objective” to obtain the best representation.
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Metadata
- Authors
- Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H.
- Affiliations
- Technical University of Denmark (DK)
- Masaryk University (CZ)
- Corresponding author
- Welner, Ditte Hededam
- ORCID
- Venue
- Biotechnology Advances
- Publisher
- Elsevier BV
- Published
- 2024-12
- Volume
- 77
- Pages
- 108459
- ISSN
- 0734-9750 (ISSN-L)
- OpenAlex
- W4403064295
- Cited by
- 38
- References
- 278
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 4
- FWCI
- 1.55
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Biotechnology Advances (publishedVersion, cc-by)
- ChemRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion, cc-by-nc-nd)
- Funders
- Horizon 2020
- Novo Nordisk Foundation
- Ministry of Education Youth and Sports of the Czech Republic
- 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 › Enzyme Catalysis and Immobilization
- Keywords
- protein representation, biocatalysis, enzyme engineering, learned representations, protein dynamics, protein 3D structure, protein sequences, inductive bias, self-supervised deep learning, explainability
- MeSH terms
- Machine Learning, Enzymes, Proteins, Neural Networks, Computer, Biocatalysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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