PySupercharge: a python algorithm for enabling ABC transporter bacterial secretion of all proteins through amino acid
Yerin Kim1, Danny Kim1, Nguyen-Mihn Hieu1
1Department of Chemistry and Biology, Korea Science Academy of Korea Advanced Institute of Science and Technology, Busan, South Korea.
Microbial Cell Factories
|April 20, 2024
Summary
Protein secretion via ATP-binding cassette (ABC) transporters is hindered by excessive positive charge. We developed PySupercharge to modify protein charge, enabling secretion of previously non-secretable proteins.
Area of Science:
- Biotechnology
- Molecular Biology
- Protein Engineering
Background:
- Bacterial protein production and secretion via ATP-binding cassette (ABC) transporters offer high capacity and efficiency.
- Certain proteins fail to secrete due to excessive positive charge in specific amino acid sequence regions.
- Removing excessive positive charge is key to enabling universal protein secretion through ABC transporters.
Purpose of the Study:
- To establish 'linear charge density' as a criterion for protein secretion possibility through ABC transporters.
- To develop a computational tool for enabling the secretion of proteins with high linear charge density.
- To demonstrate the applicability of the developed method to various non-secretable proteins.
Main Methods:
- Introduced 'linear charge density' as the primary criterion for assessing protein secretion feasibility.
- Developed a novel algorithm, PySupercharge, for analyzing and modifying protein sequences.
- PySupercharge selectively converts positively charged amino acids to neutral or negatively charged ones based on linear charge density analysis.
Main Results:
- Confirmed that linear charge density accurately predicts protein secretion through ABC transporters.
- Successfully applied the criterion to non-secretable proteins including SARS-CoV-2 spike proteins, botulinum toxin light chain, and human growth factors.
- PySupercharge demonstrated efficacy in enabling the secretion of these challenging proteins.
Conclusions:
- PySupercharge minimizes functional and structural stability loss by utilizing sequence conservation data.
- The PySupercharge tool is available via a web server for research applications.
- Validated PySupercharge's effectiveness in secreting previously non-secretable proteins, recommending it for future protein production research.
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