Affiliation:
1. Computational Systems Biology, RPTU University of Kaiserslautern, 67663 Kaiserslautern, Germany
Abstract
Proteins are essential macromolecules that carry out a plethora of biological functions. The thermal stability of proteins is an important property that affects their function and determines their suitability for various applications. However, current experimental approaches, primarily thermal proteome profiling, are expensive, labor-intensive, and have limited proteome and species coverage. To close the gap between available experimental data and sequence information, a novel protein thermal stability predictor called DeepSTABp has been developed. DeepSTABp uses a transformer-based protein language model for sequence embedding and state-of-the-art feature extraction in combination with other deep learning techniques for end-to-end protein melting temperature prediction. DeepSTABp can predict the thermal stability of a wide range of proteins, making it a powerful and efficient tool for large-scale prediction. The model captures the structural and biological properties that impact protein stability, and it allows for the identification of the structural features that contribute to protein stability. DeepSTABp is available to the public via a user-friendly web interface, making it accessible to researchers in various fields.
Subject
Inorganic Chemistry,Organic Chemistry,Physical and Theoretical Chemistry,Computer Science Applications,Spectroscopy,Molecular Biology,General Medicine,Catalysis
Reference69 articles.
1. Protein stability: A crystallographer’s perspective;Deller;Acta Crystallogr. Sect. F Struct. Biol. Commun.,2016
2. Understanding and increasing protein stability;Biochim. Biophys. Acta,1995
3. Almeida, P. (2016). Proteins: Concepts in Biochemistry, Garland Science Taylor & Francis Group.
4. Thermal Stability of Proteins;Bischof;Ann. N. Y. Acad. Sci.,2006
5. Cell-wide analysis of protein thermal unfolding reveals determinants of thermostability;Leuenberger;Science,2017
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