Identification of cell wall synthesis inhibitors active against Mycobacterium tuberculosis by competitive activity-based protein profiling

Abstract

The identification and validation of a small molecule's targets is a major bottleneck in the discovery process for tuberculosis antibiotics. Activity-based protein profiling (ABPP) is an efficient tool for determining a small molecule's targets within complex proteomes. However, how target inhibition relates to biological activity is often left unexplored. Here, we study the effects of 1,2,3-triazole ureas on Mycobacterium tuberculosis (Mtb). After screening ∼200 compounds, we focus on 4 compounds that form a structure-activity series. The compound with negligible activity reveals targets, the inhibition of which is functionally less relevant for Mtb growth and viability, an aspect not addressed in other ABPP studies. Biochemistry, computational docking, and morphological analysis confirms that active compounds preferentially inhibit serine hydrolases with cell wall and lipid metabolism functions and that disruption of the cell wall underlies biological activity. Our findings show that ABPP identifies the targets most likely relevant to a compound's antibacterial activity.

Keywords: Mycobacterium; activity-based probe; chemoproteomics; inhibitor; serine hydrolase; triazole urea; tuberculosis.

Graphical abstract

Figure 1

A triazole urea library yielded compounds that inhibit Mtb growth in both glycerol and cholesterol Autoluminescent Mtb was incubated for 7 days with 10 μM compound in modified Roisin’s medium with (A) glycerol or (B) cholesterol as the sole carbon source. Percent inhibition was calculated by normalizing the autoluminescence signal to DMSO vehicle-treated control. The Z′ scores were (A) 0.59 and (B) 0.49. Data shown are representative of two biological replicates.

Figure 2

AA691 and AA692 are active against both replicating and hypoxia-induced non-replicating Mtb Mtb was treated with (A) isoniazid, (B) rifampicin, (C) AA691, or (D) AA692 in modified Roisin’s medium containing glycerol and enumerated at each time point. ∗p < 0.05, ∗∗p < 0.005, ∗∗∗p < 0.0005, ∗∗∗∗p < 0.001 by one-way ANOVA with Dunnett correction for each time point versus t = 0 (replicating) or versus vehicle control (hypoxia-induced non-replicating). Data shown are the mean ± S.D. for three biological replicates. See also Figures S2 and S3.

Figure 3

Inhibition of individual serine hydrolases recapitulates structure-activity relationships reported by competitive ABPP Residual activity of purified serine hydrolases after incubation with the indicated compounds was determined for (A–C) TesA, (D) FbpA, (E) Rv0183, and (F) Fas. The molar excess needed to reduce activity by 50% (x_I50) was calculated by fitting the inhibition curve. Data shown are two independent experiments (A) (TesA) or the average ± SD of three independent experiments (all others). ∗p < 0.05 by one-way ANOVA with Dunnett correction for a given concentration versus vehicle-treated control.

Figure 4

AA691 and AA692 make more contacts and are positioned closer to the catalytic serine than AA701 and AA702 in the TesA active site (A) In silico molecular docking of AA691, AA692, AA701, and AA702 into the crystallographic structure of TesA in a van der Waals surface representation. Hydrophobic residues are highlighted in white. Superimposition of the top-scoring docking position of (B) AA691 (yellow) and AA692 (cyan) and (C) AA701 (pale green) and AA702 (wheat) in the vicinity of the catalytic Ser104 (magenta). Ligplot⁺ (Laskowski and Swindells, 2011) analyses showing the ligand-protein interactions for (D) AA691, (E) AA692, (F) AA701, and (H) AA702 in the TesA active site with hydrogen bonds (purple, green dashed lines) and hydrophobic interactions (red) indicated. See also Figure S5.

Figure 5

AA691 and AA692 cause morphological changes in Mtb similar to those induced by cell wall inhibitors Mtb was incubated with 50 μM (low dose [LD]) or 500 μM (high dose [HD]) of the designated compounds prior to imaging for morphological features. Following MorphEUS analysis of the resulting profiles, the nearest neighbor frequency (connection strength) based on (A), (C) broad categories for mode of action, or (B) individual compounds is highest among drugs that cause similar types of cellular change. (A) Shows data at both LD and HD; (B and C) are joint profiles from the simultaneous analysis of LD and HD results (except for THL, for which the HD profile is shown). Asterisks indicate two most frequent neighbors for each compound. See also Figure S6.