CysPresso: a classification model utilizing deep learning protein representations to predict recombinant expression of cysteine-dense peptides
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Abstract
Background: Cysteine-dense peptides (CDPs) are an attractive pharmaceutical scaffold that display extreme biochemical properties, low immunogenicity, and the ability to bind targets with high affinity and selectivity. While many CDPs have potential and confirmed therapeutic uses, synthesis of CDPs is a challenge. Recent advances have made the recombinant expression of CDPs a viable alternative to chemical synthesis. Moreover, identifying CDPs that can be expressed in mammalian cells is crucial in predicting their compatibility with gene therapy and mRNA therapy. Currently, we lack the ability to identify CDPs that will express recombinantly in mammalian cells without labour intensive experimentation. To address this, we developed CysPresso, a novel machine learning model that predicts recombinant expression of CDPs based on primary sequence. Results: We tested various protein representations generated by deep learning algorithms (SeqVec, proteInfer, AlphaFold2) for their suitability in predicting CDP expression and found that AlphaFold2 representations possessed the best predictive features. We then optimized the model by concatenation of AlphaFold2 representations, time series transformation with random convolutional kernels, and dataset partitioning. Conclusion: Our novel model, CysPresso, is the first to successfully predict recombinant CDP expression in mammalian cells and is particularly well suited for predicting recombinant expression of knottin peptides. When preprocessing the deep learning protein representation for supervised machine learning, we found that random convolutional kernel transformation preserves more pertinent information relevant for predicting expressibility than embedding averaging. Our study showcases the applicability of deep learning-based protein representations, such as those provided by AlphaFold2, in tasks beyond structure prediction.
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- Authors
- Ouellet, S., Ferguson, L., Lau, A. Z., & Lim, T. K. Y.
- Affiliations
- MRC Laboratory of Molecular Biology (GB)
- University of Toronto (CA)
- University of Cambridge (GB)
- Corresponding author
- Lim, Tony K. Y.
- Venue
- BMC Bioinformatics
- Published
- 2023-05-16
- Volume
- 24
- Issue
- 1
- Pages
- 200-200
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC10189939
- OpenAlex
- W4376872947
- Cited by
- 5
- References
- 40
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3
- FWCI
- 0.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- UK Research and Innovation
- University of Cambridge
- Alliance de recherche numérique du Canada
- Natural Sciences and Engineering Research Council of Canada (RGPIN 2017 ?, rgpin-2017-06596)
- Medical Research Council
- H2020 Marie Skłodowska-Curie Actions
- Compute Ontario
- 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- time series transformation, gene therapy, mRNA therapeutics
- MeSH terms
- Deep Learning, Algorithms, Animals, Cysteine, Mammals, Peptides, Proteins
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