Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.

Autor: Komp E; Department of Chemical Engineering, University of Washington, Seattle, USA. komp.evan@gmail.com., Alanzi HN; Department of Chemical Engineering, University of Washington, Seattle, USA., Francis R; Department of Chemical Engineering, University of Washington, Seattle, USA., Vuong C; Department of Biochemistry, University of Washington, Seattle, USA., Roberts L; Department of Chemical Engineering, University of Washington, Seattle, USA., Mosallanejad A; Department of Chemical Engineering, University of Washington, Seattle, USA., Beck DAC; Department of Chemical Engineering, University of Washington, Seattle, USA. dacb@uw.edu.; eScience Institute, University of Washington, Seattle, USA. dacb@uw.edu.; Paul G. Allen School of Computer Science, University of Washington, Seattle, USA. dacb@uw.edu.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2023 Oct 07; Vol. 10 (1), pp. 682. Date of Electronic Publication: 2023 Oct 07.
DOI: 10.1038/s41597-023-02553-w
Abstrakt: 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.
(© 2023. Springer Nature Limited.)
Databáze: MEDLINE
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