Key reaction components affect the kinetics and performance robustness of cell-free protein synthesis reactions

Colette J Whitfield,Jonathan E Fieldsend,Steven R Brown,Sarah A Goodchild,Christopher Grant,Dennis W Lendrem,Alice M Banks,Thomas P Howard,John Love,David A Fulton

doi:10.1016/j.csbj.2021.12.013

Abstract

Cell-free protein synthesis (CFPS) reactions have grown in popularity with particular interest in applications such as gene construct prototyping, biosensor technologies and the production of proteins with novel chemistry. Work has frequently focussed on optimising CFPS protocols for improving protein yield, reducing cost, or developing streamlined production protocols. Here we describe a statistical Design of Experiments analysis of 20 components of a popular CFPS reaction buffer. We simultaneously identify factors and factor interactions that impact on protein yield, rate of reaction, lag time and reaction longevity. This systematic experimental approach enables the creation of a statistical model capturing multiple behaviours of CFPS reactions in response to components and their interactions. We show that a novel reaction buffer outperforms the reference reaction by 400% and importantly reduces failures in CFPS across batches of cell lysates, strains of E. coli, and in the synthesis of different proteins. Detailed and quantitative understanding of how reaction components affect kinetic responses and robustness is imperative for future deployment of cell-free technologies.

Highlights

Cell-free systems have established themselves as key platforms for the delivery of synthetic biology projects
We wished to determine how cell lysate preparation and the concentration of components in cell-free protein synthesis reactions impact on the kinetics of cellfree protein synthesis (CFPS)
In agreement with work from Failmezger et al [31], our data indicated that while it is important to grow cells to a moderate to high cell density, leaving cells in stationary phase was not detrimental to Cell-free protein synthesis (CFPS) reactions (Fig S1). These trials confirmed previous analysis that sonication is a more effective method for generating functional CFEs than bead beating [32]. Both moderate and high sonication conditions resulted in a strong CFPS performance, though extracts produced in moderate conditions were slower to reach maximum protein synthesis rates

Summary

Introduction

Cell-free systems have established themselves as key platforms for the delivery of synthetic biology projects. By considering multiple responses relating to performance kinetics, for example yield, rate and longevity, it is possible to study multiple responses simultaneously and to identify response trade-offs and system limitations This systematic approach has previously been applied to optimise and understand various bioprocesses ranging from the optimisation of recombinant antibody production [24] and improving metabolic pathway efficiency [25], to modelling the ethanol biosynthetic pathway in yeast [26] and developing high-performance whole cell biosensors [27]. Combining laboratory automation with statistically-structured experimental design serves as an efficient means to obtain highly informative datasets that can be used to model complex systems Such approaches have been used to build predictive models to maximise protein production in cell-free systems [23,28] but in each case the optimisation was not focussed on reaction kinetics directly. Where different classes of reaction kinetics were observed [28], this could primarily be attributed to changes in gene constructs rather than buffer composition

Methods

Results

Conclusion