Engineering cell-free systems by chemoproteomic-assisted phenotypic screening

Abstract

Phenotypic screening is a valuable tool to both understand and engineer complex biological systems. We demonstrate the functionality of this approach in the development of cell-free protein synthesis (CFPS) technology. Phenotypic screening identified numerous compounds that enhanced protein production in yeast lysate CFPS reactions. Notably, many of these were competitive ATP kinase inhibitors, with the exploitation of their inherent substrate promiscuity redirecting ATP flux towards heterologous protein expression. Chemoproteomic-guided strain engineering partially phenocopied drug effects, with a 30% increase in protein yield observed upon deletion of the ATP-consuming SSA1 component of the HSP70 chaperone. Moreover, drug-mediated metabolic rewiring coupled with template optimization generated the highest protein yields in yeast CFPS to date using a hitherto less efficient, but more cost-effective glucose energy regeneration system. Our approach highlights the utility of target-agnostic phenotypic screening and target identification to deconvolute cell-lysate complexity, adding to the expanding repertoire of strategies for improving CFPS.

Fig. 1. Yeast lysate CFPS phenotypic screening workflow to identify compounds that increase heterologous protein expression yield. (A) Schematic of the drug screening workflow. Drugs (100 μM, n = 1443) were incubated with yeast extract for 15 minutes prior to addition of nLuc DNA template and other essential components (amino acids, nucleotides, salts) to initiate expression. nLuc produced was measured after 2.5 h incubation. (B) Repeat experiment of selected drug candidates showing fold increase in nLuc production. n = 3 ± SD. Significance measured using two-tailed Student's t-test.

Fig. 2. Optimization of the drug incubation conditions. (A) Effect of CFPS temperature and drug pre-incubation was assessed on the final nLuc yield. A reaction mix containing nLuc template was added to the yeast extract fraction either directly with 100 μM bosutinib/vehicle or after 15 min pre-incubation of the extract. Extract pre-incubation and subsequent CFPS were performed either at 22 °C or 30 °C. n = 3 ± SD. *p < 0.05; **p < 0.01; ***p < 0.005 (two-tailed Student's t-test). (B) nLuc expression in yeast lysate measured over the indicated drug concentrations. n = 3 ± SD. (C), (D) Bosutinib, cerdulatinib, nafarelin acetate and dasatinib were added to CFPS reactions at their optimum concentrations individually or at the indicated combinations. The drug effect was assessed on the synthesis of nLuc (C) and p53 (D) by means of luminescence and western blot densitometry analysis, respectively. All readings were normalised to DMSO-treated controls. n = 3 ± SD.

Fig. 3. Characterization of the bosutinib effect on yeast CFPS. (A) nLuc synthesis in CFPS reactions treated with 0 μM and 100 μM bosutinib was monitored at varying time intervals from 0–5 h. n = 2 ± SD. (B) Corresponding ATP levels were measured from nLuc-synthesizing CFPS reactions, NTC controls (−/+ indicating addition of bosutinib) and CFPS reactions without the creatine phosphate secondary energy regeneration system. (C) Productivity of the yeast extract from cells harvested at OD₆₀₀ = 1.2 was assessed. CFPS reactions were treated with 100 μM bosutinib and set with either creatine phosphate or glucose as a secondary energy source. n = 3 ± SD. (D,E) nLuc and ATP levels in glucose-driven reactions were monitored from 0–8 h. n = 2 ± SD.

Fig. 4. Drug effects on mammalian cell-based CFPS. Bosutinib and dasatinib monohydrate were pre-incubated with the HeLa cell lysate fraction for 15 or 30 min. Subsequent nLuc production was assessed with respect to DMSO-treated controls. n ≥ 3 ± SD.

Fig. 5. Effect of bosutinib on proteins and pathways in CFPS identified by thermal proteomics. (A) Workflow for thermal proteomics on the cell free protein system. Yeast extracts were treated with different doses of bosutinib (vehicle, 0.01 nM, 0.1 nM, 1 nM, 100 nM, 1 μM, 10 μM, 100 μM and 1 mM) and subjected to heat stress at 55 °C. Proteins from the different conditions were processed and peptides were labelled using tandem mass tags (TMT). Liquid chromatography coupled with tandem mass-spectrometry (LC–MS/MS) acquisition was performed to profile proteins based on the dose response relationship. Data analysis was carried out to identify proteins and pathways affected by bosutinib treatment. (B) Heat map showing the relative protein changes normalized to the lowest drug concentration (0.01 nM) across 457 proteins identified from three TMT replicate experiments. Twenty proteins with greater than 1.2-fold increase in mean fold change are annotated in the heatmap. (C) Top 10 biological processes based on gene ontology (GO) enrichment analysis using 231 proteins with mean increase ≥10%. (D) Bar plot of the twenty proteins with greater than 1.2-fold increase in decreasing order of magnitude. SSB2 and SSA1 were identified as the top 2 proteins with mean fold change of 1.37 and 1.33, respectively. (E) Protein interaction network for the top 20 hits highlighted four chaperone proteins (SSB2, SSA1, SKP1, STI1) using the STRING database (proteins are coloured in red based on mean fold change with the color intensity reflecting a higher mean fold-change). (F) Individual loess curves fitted for drug dose against protein quantity measured by mass spectrometry for SSA1 and SSB2, respectively.

Fig. 6. Chemoproteomic-guided strain engineering. (A) Activity of extracts from BY4743-WT and BY4743-ΔSSA1 strains (without drug treatment and with bosutinib, nafarelin acetate, cerdulatinib and dasatinib) were compared in nLuc expressing CFPS reactions set using glucose as the secondary energy source. n = 4 ± SD. p values determined by two-tailed Student's t-test. (B) Drug effects on nLuc synthesis in CFPS reactions using WT and ΔSSA1 extracts were compared by determining the fold increase in yield with respect to corresponding drug-free controls. n = 4 ± SD. p values determined by two-tailed Student's t-test. (C), (D) CFPS reactions with WT and ΔSSA1 extracts were set for Mdm2 protein synthesis. Protein yields were assessed using densitometry analysis of the western blot image. The Mdm2 yield in the ΔSSA1 extract is normalised to that of the WT extract (C top). Representative blot of Mdm2 synthesized by drug treated extracts is presented (C bottom). Fold increase in Mdm2 levels is calculated with respect to the corresponding WT and ΔSSA1 controls (D). n = 2 ± SD. p values determined by two-tailed Student's t-test.