Studying Target-Engagement of Anti-Infectives by Solvent-Induced Protein Precipitation and Quantitative Mass Spectrometry

Abstract

Antimicrobial resistance (AMR) poses a serious threat to global health. The rapid emergence of resistance contrasts with the slow pace of antimicrobial development, emphasizing the urgent need for innovative drug discovery approaches. This study addresses a critical bottleneck in early drug development by introducing integral solvent-induced protein precipitation (iSPP) to rapidly assess the target-engagement of lead compounds in extracts of pathogenic microorganisms under close-to-physiological conditions. iSPP measures the change in protein stability against solvent-induced precipitation in the presence of ligands. The iSPP method for bacteria builds upon established SPP procedures and features optimized denaturation gradients and minimized sample input amounts. The effectiveness of the iSPP workflow was initially demonstrated through a multidrug target-engagement study. Using quantitative mass spectrometry (LC-MS/MS), we successfully identified known drug targets of seven different antibiotics in cell extracts of four AMR-related pathogens: the three Gram-negative bacteria Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa and the Gram-positive bacterium Staphylococcus aureus. The iSPP method was ultimately applied to demonstrate target-engagement of compounds derived from target-based drug discovery. We employed five small molecules targeting three enzymes in the 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway─a promising focus for anti-infective drug development. The study showcases iSPP adaptability and efficiency in identifying anti-infective drug targets, advancing early-stage drug discovery against AMR.

Keywords: MEP pathway; antibiotics; mass spectrometry; proteomics; solvent-induced precipitation; target identification.

Scheme 1. MEP Pathway and Its Associated Enzyme Idi

Pyr, pyruvate; GAP, glyceraldehyde 3-phosphate; DXS, 1-deoxy-d-xylulose-5-phosphate synthase; DXP, 1-deoxy-d-xylulose 5-phosphate; DXR, 1-deoxy-d-xylulose 5-phosphate reductoisomerase; MEP, 2-C-methylerythritol 4-phosphate; IspD, 2-C-methyl-d-erythritol 4-phosphate cytidylyltransferase; CTP, cytidine triphosphate; PPi, inorganic diphosphate; CDP-ME, 4-diphosphocytidyl-2-C-methylerythritol; IspE, 4-diphosphocytidyl-2-C-methyl-d-erythritol kinase; CDP-MEP, 4-diphosphocytidyl-2-C-methyl-d-erythritol 2-phosphate; IspF, 2-C-methyl-d-erythritol 2,4-cyclodiphosphate synthase; CMP, cytidine monophosphate; MEcPP, 2-C-methyl-d-erythritol 2,4-cyclodiphosphate; IspG, 4-hydroxy-3-methyl-but-2-en-1-yl diphosphate synthase; HMBPP, (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate; IspH, 4-hydroxy-3-methylbut-2-enyl diphosphate reductase; IDP, isopentenyl diphosphate; DMADP, dimethylallyl diphosphate; Idi, Isopentenyl-diphosphate delta-isomerase

Figure 1

Solvent profiling of the Escherichia coli and Klebsiella pneumoniae proteomes. (A) Schematic representation of the iSPP approach for target–engagement studies (both compound incubation and soluble fractions pooling were omitted during the solvent profiling experiments). Workflow graphic created with BioRender.com. (B) Denaturation curve of the E. coli and K. pneumoniae proteomes. For each data point, the median value of all quantified proteins is shown. (C) Distribution of C_M values for E. coli proteins with high-quality denaturation curves (1984 proteins, R² ≥ 0.8, and plateau ≤ 0.3). (D) Pearson correlation of C_M values for 1249 predicted orthologs identified in our experiments between E. coli and K. pneumoniae, showing a strong positive correlation (r = 0.71). Proteins are colored based on the density of the points. (E) Heatmap representation of all E. coli proteins quantified in the performed experiment (2438 proteins). For each protein, its relative abundance (fold change) at the indicated %AEA (v/v) compared to the lowest concentration (0%) is shown. Proteins are sorted by intensity (in descending order).

Figure 2

iSPP approach in Escherichia coli cell lysates to identify the protein targets of model drugs. E. coli lysates were incubated with vehicle, rifampicin (A), or methotrexate (D) and then exposed to the AEA gradient 20–34% v/v. Fosmidomycin (B), ampicillin (C), or the corresponding vehicle control-incubated cell lysates were exposed to the AEA gradient 14–28% v/v. All drugs were tested at a concentration of 10 μM. Data are presented as a volcano plot to highlight changes in protein abundance of each drug over vehicle sample vs statistical significance. We implemented criteria to ensure robust identification and selection of proteins exhibiting statistically significant changes in response to the experimental conditions by setting thresholds of a log₂ fold change (log₂FC) > |1.0| and a p-value < 0.05 (dashed lines). Red, stabilized proteins with log₂FC > 1.0 and p-value < 0.05; blue, destabilized proteins with log₂FC < −1.0 and p-value < 0.05; gray/black, proteins with −1.0 < log₂FC < 1.0 and p-value < 0.05 and proteins with p-value > 0.05.

Figure 3

iSPP approach in Klebsiella pneumoniae cell lysates. K. pneumoniae cell lysates were incubated with vehicle, piperacillin (A), imipenem (B), fosmidomycin (C), or methotrexate (D) and then exposed to the AEA gradient 12–29.5% v/v (10 μM for all drugs). Data are presented as a volcano plot to highlight changes in abundance of each drug over vehicle sample vs statistical significance. The thresholds were set to a log₂ fold change (log₂FC) > |0.5| and a p-value < 0.05 (dashed lines). Red, stabilized proteins with log₂FC > 0.5 and p-value < 0.05; blue, destabilized proteins with log₂FC < −0.5 and p-value < 0.05; gray/black, proteins with −0.5 < log₂FC < 0.5 and p-value < 0.05 and proteins with p-value > 0.05.

Figure 4

iSPP approach in Pseudomonas aeruginosa cell lysates. P. aeruginosa cell lysates were incubated with vehicle, piperacillin (A), imipenem (B), fosmidomycin (C), or methotrexate (D) and then exposed to the AEA gradient 12–29.5% v/v (10 μM for all drugs). Data are presented as a volcano plot to highlight changes in abundance of each drug over vehicle vs statistical significance. The thresholds were set to a log2 fold change (log₂FC) > |0.5| and a p-value < 0.05 (dashed lines). Red, stabilized proteins with log₂FC > 0.5 and p-value < 0.05; blue, destabilized proteins with log₂FC < −0.5 and p-value < 0.05; gray/black, proteins with −0.5 < log₂FC < 0.5 and p-value < 0.05 and proteins with p-value > 0.05.

Scheme 2. Chemical Structures and Enzymatic Inhibition of Reverse β-Aza Fosmidomycin Analogues 1–3, AMBPP 4, and Propargyl Diphosphate 5

Ec, Escherichia coli ; Aa, Aquifex aeolicus

Figure 5

iSPP approach in Escherichia coli cell lysates to identify (A–C) the putative targets of fosmidomycin analogues (1–3, 10, 30, and 30 μM, respectively) and (D, E) diphosphate derivatives (4 and 5, 50 and 100 μM, respectively). E. coli cell lysates were incubated with vehicle or the above-mentioned compounds and then exposed to the AEA gradient 14–28% v/v. Data are presented as a volcano plot to highlight changes in abundance of each compound over vehicle vs statistical significance. The thresholds were set to a log₂ fold change (log₂FC) > |0.5| and a p-value < 0.05. Red, stabilized proteins with log₂FC > 0.5 and p-value < 0.05; blue, destabilized proteins with log₂FC < −0.5 and p-value < 0.05; gray/black, proteins with −0.5 < log₂FC < 0.5 and p-value < 0.05 and proteins with p-value > 0.05.