Enhancing the Cell-Free Expression of Native Membrane Proteins by In Silico Optimization of the Coding Sequence-An Experimental Study of the Human Voltage-Dependent Anion Channel.
MEMBRANES 2021;
11:membranes11100741. [PMID:
34677509 PMCID:
PMC8540592 DOI:
10.3390/membranes11100741]
[Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 09/12/2021] [Accepted: 09/13/2021] [Indexed: 11/16/2022]
Abstract
Membrane proteins are involved in many aspects of cellular biology; for example, they regulate how cells interact with their environment, so such proteins are important drug targets. The rapid advancement in the field of immune effector cell therapy has been expanding the horizons of synthetic membrane receptors in the areas of cell-based immunotherapy and cellular medicine. However, the investigation of membrane proteins, which are key constituents of cells, is hampered by the difficulty and complexity of their in vitro synthesis, which is of unpredictable yield. Cell-free synthesis is herein employed to unravel the impact of the expression construct on gene transcription and translation, without the complex regulatory mechanisms of cellular systems. Through the systematic design of plasmids in the immediacy of the start of the target gene, it was possible to identify translation initiation and the conformation of mRNA as the main factors governing the cell-free expression efficiency of the human voltage-dependent anion channel (VDAC), which is a relevant membrane protein in drug-based therapy. A simple translation initiation model was developed to quantitatively assess the expression potential for the designed constructs. A scoring function that quantifies the feasibility of the formation of the translation initiation complex through the ribosome–mRNA hybridization energy and the accessibility of the mRNA segment binding to the ribosome is proposed. The scoring function enables one to optimize plasmid sequences and semi-quantitatively predict protein expression efficiencies. This scoring function is publicly available as webservice XenoExpressO at University of Vienna, Austria.
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