Engineering Ag43 Signal Peptides with Bacterial Display and Selection

Darius Wen-Shuo Koh; Jian-Hua Tay; Samuel Ken-En Gan

doi:10.3390/mps6010001

Engineering Ag43 Signal Peptides with Bacterial Display and Selection

Methods Protoc. 2022 Dec 23;6(1):1. doi: 10.3390/mps6010001.

Authors

Darius Wen-Shuo Koh¹, Jian-Hua Tay¹, Samuel Ken-En Gan^{1

2

3

4}

Affiliations

¹ Antibody & Product Development Laboratory, Agency for Science, Technology, and Research (A*STAR), Singapore 138671, Singapore.
² James Cook University, Singapore, Singapore 387380, Singapore.
³ Zhejiang Bioinformatics International Science and Technology Cooperation Center, Wenzhou-Kean University, Wenzhou 325015, China.
⁴ Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics, Wenzhou-Kean University, Wenzhou 325015, China.

Abstract

Protein display, secretion, and export in prokaryotes are essential for utilizing microbial systems as engineered living materials, medicines, biocatalysts, and protein factories. To select for improved signal peptides for Escherichia coli protein display, we utilized error-prone polymerase chain reaction (epPCR) coupled with single-cell sorting and microplate titer to generate, select, and detect improved Ag43 signal peptides. Through just three rounds of mutagenesis and selection using green fluorescence from the 56 kDa sfGFP-beta-lactamase, we isolated clones that modestly increased surface display from 1.4- to 3-fold as detected by the microplate plate-reader and native SDS-PAGE assays. To establish that the functional protein was displayed extracellularly, we trypsinized the bacterial cells to release the surface displayed proteins for analysis. This workflow demonstrated a fast and high-throughput method leveraging epPCR and single-cell sorting to augment bacterial surface display rapidly that could be applied to other bacterial proteins.

Keywords: Ag43 autotransporter; engineering signal peptides; error-prone PCR; protein surface display; single-cell screening.

Grants and funding

This work was supported by the Wenzhou Science and Technology Bureau, Key Lab Program, Wenzhou Municipal Key Laboratory for Applied Biomedical and Biopharmaceutical Informatics, Wenke Jiji [2021] No. 4, To Wenzhou-Kean University.