Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe
Open accessPublished version of a preprint
Abstract
Stability of proteins at high temperature has been a topic of interest for many years, as this attribute is favourable for applications ranging from therapeutics to industrial chemical manufacturing. Our current understanding and methods for designing high-temperature stability into target proteins are inadequate. To drive innovation in this space, we have curated a large dataset, learn2thermDB, of protein-temperature examples, totalling 24 million instances, and paired proteins across temperatures based on homology, yielding 69 million protein pairs - orders of magnitude larger than the current largest. This important step of pairing allows for study of high-temperature stability in a sequence-dependent manner in the big data era. The data pipeline is parameterized and open, allowing it to be tuned by downstream users. We further show that the data contains signal for deep learning. This data offers a new doorway towards thermal stability design models.
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Cites
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Flagged first, then by citations
- Authors
- Komp, E., Alanzi, H. N., Francis, R., Vuong, C., Roberts, L., Mosallanejad, A., & Beck, D. A. C.
- Affiliations
- University of Washington (US)
- Corresponding author
- Komp, Evan
- Beck, David A. C.
- Venue
- Scientific Data
- Published
- 2023-10-07
- Volume
- 10
- Issue
- 1
- Pages
- 682-682
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC10560248
- OpenAlex
- W4387424814
- Cited by
- 7
- References
- 12
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 1
- Type
- data paper
- Language
- en
- Update notices
- correction (2023-10-31)
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (OAC-1934292)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › thermodynamics and calorimetric analyses
- Keywords
- protein thermal stability, prokaryotic proteins, protein design, deep learning, protein sequence data
- MeSH terms
- Prokaryotic Cells, Proteins, Temperature, Protein Stability
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