Recombinant Mycobacterium tuberculosis decaprenylphosphoryl-beta-D-ribose oxidase (dprE1)

Basic Research Questions

• What is DprE1 and what is its role in Mycobacterium tuberculosis?

  DprE1 (Decaprenylphosphoryl-β-D-ribose 2'-epimerase) is an essential flavoenzyme in Mycobacterium tuberculosis that catalyzes a critical step in cell wall biosynthesis. Specifically, DprE1 works in conjunction with DprE2 to catalyze the epimerization of decaprenyl-phospho-ribose (DPR) to decaprenyl-phospho-arabinose (DPA), which serves as the precursor for arabinogalactan and lipoarabinomannan synthesis. These components are essential structural elements of the mycobacterial cell wall . DprE1 initiates the first step of this epimerization process by oxidizing DPR, making it indispensable for mycobacterial viability and thus an attractive drug target .

• Why is DprE1 considered a promising drug target for TB treatment?

  DprE1 has emerged as one of the most promising targets for tuberculosis drug development for several key reasons. First, it is essential for mycobacterial survival, as it catalyzes a vital step in cell wall component synthesis . Second, it has a periplasmic localization that makes it relatively accessible to inhibitors . Third, multiple inhibitor scaffolds targeting DprE1 have demonstrated potent antimycobacterial activity both in vitro and in vivo . Importantly, several DprE1 inhibitors (BTZ-043, Macozinone/PBTZ-169, TBA-7371 & OPC-167832) have already progressed to clinical development stages, validating the druggability of this target . The presence of Cys387 in the active site provides a basis for developing selective covalent inhibitors, further enhancing its appeal as a drug target .

• What methodologies are used for recombinant DprE1 expression and purification?

  Successful production of recombinant DprE1 for research purposes typically follows a multi-stage methodology:

  Expression Systems:

  • Escherichia coli is the most common expression host, using codon-optimized DprE1 gene constructs

  • Expression vectors typically include N-terminal or C-terminal affinity tags (His6, MBP, GST) for purification

  • Specialized E. coli strains such as BL21(DE3), Rosetta, or Arctic Express may be used to enhance proper folding

  Expression Conditions:

  • IPTG induction at lower temperatures (16-18°C) often improves soluble protein yield

  • Extended expression periods (16-24 hours) at reduced temperatures are preferable

  • Supplementation with FAD or riboflavin may enhance cofactor incorporation

  Purification Protocol:

  • Initial capture via affinity chromatography (Ni-NTA for His-tagged constructs)

  • Ion-exchange chromatography for removal of contaminants

  • Size-exclusion chromatography to obtain homogeneous protein

  • Buffer optimization to maintain enzyme stability (typically includes glycerol and reducing agents)

  Activity Verification:

  • Spectrophotometric assays monitoring FAD reduction

  • Thermal shift assays to confirm proper folding and ligand binding capacity

  • Mass spectrometry to verify protein integrity

  This methodological approach provides properly folded, active DprE1 enzyme suitable for inhibitor screening, structural studies, and enzymatic characterization .

• What are the major chemical classes of DprE1 inhibitors and their mechanisms of action?

  DprE1 inhibitors can be categorized into two major mechanistic classes with several distinct chemical scaffolds:

  Covalent Inhibitors:

  • Benzothiazinones (BTZ): Form a covalent bond with Cys387 through a nitro group that is reduced to a reactive nitroso derivative

  • Nitrobenzothiazoles (NBTO): Operate through similar covalent mechanisms targeting Cys387

  • Dinitrobenzamides (DNBs): Contain nitro groups that enable covalent modification of the active site

  • Nitroquinoxalines (NQs): Also function through covalent modification of Cys387

  • 3-Nitro-1,2,4-triazoles (NTZs): Employ similar covalent mechanisms as other nitro-containing compounds

  Non-covalent Inhibitors:

  • Azaindole derivatives: Bind through hydrogen bonding and hydrophobic interactions without covalent modification

  • Morpholino-pyrimidines: Interact with the binding site through non-covalent interactions

  • Hydantoin-based inhibitors: Form hydrogen bonds and hydrophobic interactions with specific residues in the binding pocket

  The covalent inhibitors typically demonstrate higher potency with MIC values in the nanomolar to low micromolar range, while non-covalent inhibitors often require higher concentrations but may present fewer toxicity concerns .

• How do researchers assess DprE1 inhibition in experimental settings?

  Comprehensive evaluation of DprE1 inhibition employs a multi-tiered methodology:

  Biochemical Assays:

  • Spectrophotometric assays monitoring FAD reduction during DprE1 catalysis

  • HPLC-based assays tracking substrate conversion

  • Thermal shift assays to detect inhibitor binding through protein stabilization

  Cellular Assays:

  • Determination of minimum inhibitory concentration (MIC) against M. tuberculosis strains

  • MIC shift assays comparing activity against wild-type and DprE1-overexpressing strains

  • Cell wall biosynthesis assays measuring incorporation of radiolabeled precursors

  Target Engagement:

  • Resistant mutant generation and sequencing to confirm DprE1 as the target

  • Cellular thermal shift assays for target engagement in intact cells

  • Biochemical assays with purified mutant enzymes to validate resistance mechanisms

  In Vivo Evaluation:

  • Mouse infection models to assess efficacy in acute and chronic TB infections

  • Pharmacokinetic/pharmacodynamic studies to determine optimal dosing

  • Toxicology studies to assess safety margins

  This methodological cascade allows researchers to confidently identify and characterize specific DprE1 inhibitors, separating them from compounds acting through other mechanisms .

Advanced Research Questions

• What computational strategies have proven most effective for discovering novel DprE1 inhibitors?

  Successful computational approaches for DprE1 inhibitor discovery have employed several integrated strategies:

  Structure-Based Methods:

  • Molecular docking to crystal structures (e.g., PDB: 4KW5, 4P8C) has identified high-affinity binders

  • Covalent docking protocols specifically for nitro-containing compounds

  • Molecular dynamics simulations (typically 100 ns) to assess stability of protein-ligand complexes

  • MM/GBSA binding energy calculations to estimate binding affinity (effective inhibitors often show binding energies below -60 kcal/mol)

  Ligand-Based Approaches:

  • Pharmacophore modeling based on known active compounds (e.g., ADRRR_1 hypothesis comprising one hydrogen bond acceptor, one hydrogen bond donor, and three aromatic rings)

  • 3D-QSAR models with robust statistics (Training set R² > 0.69, Test set Q² > 0.50)

  • Scaffold hopping to identify novel chemotypes maintaining essential interaction patterns

  Integrated Screening Workflows:

  • Computational Bioactivity Fingerprints (CBFP) combined with structure-based virtual screening

  • Sequential filtering: pharmacophore screening → docking → binding energy calculations

  • Machine learning models trained on known DprE1 inhibitors

  Virtual Screening Applications:

  • Large-scale screening of chemical libraries (e.g., ChemDiv, ChEMBL database with >2.3 million compounds)

  • Selection based on phase fitness scores and number of pharmacophore feature matches

  • Prioritization of diverse scaffolds to explore novel chemical space

  These computational methodologies have successfully identified compounds that demonstrated experimental activity against both DprE1 enzyme and M. tuberculosis in culture .

• How do the physicochemical properties of covalent and non-covalent DprE1 inhibitors differ?

  Analysis of DprE1 inhibitors reveals distinct physicochemical property profiles between covalent and non-covalent inhibitors:

  Property	Covalent Inhibitors	Non-covalent Inhibitors
  Mean C log P	3.77	3.28
  C log D range (10-90%)	2.18-3.77	1.83-3.60
  HBA (optimal)	Higher	Lower
  HBD (optimal)	Lower	Higher
  TPSA	Higher	Lower
  Rotatable bonds (median)	6.02	6.62
  Log S	Lower	Higher

  Covalent inhibitors typically display higher lipophilicity (right-shifted C log P distribution) compared to non-covalent inhibitors, with mean values of 3.77 versus 3.28 (p < 0.0001) . For C log D values, the covalent inhibitors show a slightly higher mean value of 2.99 versus 2.73 for non-covalent compounds (p = 0.002) .

  The number of rotatable bonds differs significantly between active and inactive non-covalent inhibitors (p < 0.0001), with active compounds having a mean of 6.62 rotatable bonds compared to 5.56 for inactive compounds . For most effective DprE1 inhibitors, rotatable bond values range from 4 (10th percentile) to 10 (90th percentile), with a median value of 6 .

  These findings suggest that optimization strategies should differ between covalent and non-covalent inhibitor series, with different target property profiles for each class .

• What methodological approaches can identify potential resistance mechanisms against DprE1 inhibitors?

  Comprehensive identification and characterization of resistance mechanisms requires a multi-faceted approach:

  Genetic Methods:

  • Generation of resistant mutants through serial passage in sub-inhibitory concentrations

  • Whole-genome sequencing to identify mutations in resistant strains

  • CRISPR-based genome editing to introduce suspected resistance mutations

  • Transcriptomic analysis to identify compensatory mechanisms

  Biochemical Characterization:

  • Site-directed mutagenesis of recombinant DprE1 to introduce resistance-associated mutations

  • Enzymatic assays with mutant proteins to quantify impact on inhibitor potency

  • Thermal shift assays to assess changes in inhibitor binding

  • Surface plasmon resonance to measure binding kinetics of inhibitors to wild-type and mutant enzymes

  Structural Biology:

  • X-ray crystallography of resistant mutant DprE1 with inhibitors

  • Computational modeling of mutation effects on binding interactions

  • Molecular dynamics simulations to assess dynamic effects of mutations

  Cellular Studies:

  • MIC determination against panels of resistant isolates

  • Time-kill kinetics to assess impact on bactericidal activity

  • Combination studies to identify strategies to overcome resistance

  Counter-strategies for resistance mitigation include developing inhibitors with alternative binding modes that maintain activity against common resistance mutations, dual-targeting approaches, and combination therapies that suppress resistance emergence .

• What are the key challenges in optimizing DprE1 inhibitors for clinical development?

  Researchers face several significant challenges when advancing DprE1 inhibitors toward clinical application:

  Balancing Physicochemical Properties:

  • Achieving sufficient lipophilicity for mycobacterial cell penetration (optimal C log P range: 2.4-5.0) while maintaining aqueous solubility

  • Optimizing molecular weight (typically higher for active compounds) while improving pharmacokinetic properties

  • Managing topological polar surface area (TPSA) to balance cell penetration with solubility

  Toxicity Mitigation:

  • For covalent inhibitors: reducing potential off-target reactivity of nitro groups while maintaining DprE1 inhibition

  • Addressing cytotoxicity concerns through selective targeting

  • Reducing hERG channel inhibition and other safety liabilities

  • Establishing sufficient selectivity over human enzymes

  Pharmacokinetic Optimization:

  • Improving metabolic stability, particularly for compounds with reactive groups

  • Enhancing oral bioavailability to enable convenient dosing

  • Achieving sufficient distribution to infection sites, particularly lung tissue

  • Balancing protein binding with free drug concentration

  Resistance Considerations:

  • Developing inhibitors with high genetic barriers to resistance

  • Creating compounds that maintain activity against common resistance mutations

  • Designing structures that engage multiple binding modes to reduce resistance potential

  Manufacturing Challenges:

  • For nitro-containing compounds: ensuring stability and safe handling

  • Developing scalable synthetic routes with acceptable yields

  • Ensuring consistent crystalline forms for stable drug product

  These challenges require integrated optimization strategies that consider both potency and developability properties throughout the lead optimization process .

• How can structure-activity relationship data inform the design of next-generation DprE1 inhibitors?

  Comprehensive analysis of structure-activity relationships across multiple inhibitor series reveals critical design parameters:

  Essential Pharmacophore Features:

  • Hydrogen bond acceptors to interact with key residues in the binding site

  • Aromatic/hydrophobic groups for π-stacking and hydrophobic interactions

  • For covalent inhibitors: properly positioned nitro group for interaction with Cys387

  • Specific spatial arrangement maintaining key interaction points

  Scaffold-Specific Insights:

  For Benzothiazinones (BTZ):

  • Nitro group at C8 position is critical for covalent inhibition mechanism

  • Piperazine ring modifications can improve physicochemical properties

  • Trifluoromethyl group enhances potency through electronic effects

  For Azaindole Derivatives:

  • Electron-withdrawing groups on phenyl rings enhance potency

  • Specific chain length linking aromatic groups is critical

  • Hydrogen bond donor-acceptor pattern in core structure is essential for binding

  For Hydantoin-Based Inhibitors:

  • Rigidification of flexible linkers improves binding affinity

  • Strategic placement of polar groups enhances solubility without compromising activity

  Cross-Series Analysis:

  • Consistent engagement of specific binding site regions across diverse scaffolds

  • Correlation between binding modes and resistance profiles

  • Lipophilicity optimum for cellular activity (excessive lipophilicity reduces efficacy)

  These structure-activity insights enable rational design strategies for novel DprE1 inhibitors, including scaffold hopping, bioisosteric replacement, and property-based optimization to improve both potency and drug-like characteristics .

• What role do computational bioactivity fingerprints play in DprE1 inhibitor discovery?

  Computational Bioactivity Fingerprints (CBFPs) represent an innovative approach for DprE1 inhibitor discovery that offers several methodological advantages:

  Fundamental Principles:

  • CBFPs encode the biological activity profiles of compounds using computational descriptors

  • They capture interaction patterns between molecules and their targets without requiring detailed structural knowledge

  • Enable comparisons between chemically diverse compounds based on bioactivity similarity rather than structural similarity

  Implementation Methodology:

  • Generation of molecular fingerprints based on 2D or 3D chemical features

  • Training machine learning models to correlate fingerprints with experimental DprE1 inhibition data

  • Development of bioactivity models that predict potential for DprE1 inhibition

  • Integration with structure-based methods for validation and refinement

  Applications in DprE1 Research:

  • Scaffold hopping to identify novel chemical classes with potential DprE1 inhibitory activity

  • Virtual screening of chemical libraries to prioritize compounds for experimental testing

  • Prediction of activity for newly designed compounds before synthesis

  • Systematic exploration of structure-activity landscapes

  Case Study Evidence:

  • In a study combining CBFP and structure-based virtual screening, researchers screened the ChemDiv library and identified compounds like B2 and H3 that demonstrated potent inhibition of M. smegmatis with MIC50 values below 1 μM

  • Compound H3 showed significant activity against M. tuberculosis (MIC = 1.25 μM) and low cytotoxicity against mammalian cells

  • Thermal shift assays confirmed that the identified compounds directly targeted DprE1

  This integrated approach demonstrates how CBFPs can enhance the discovery process for DprE1 inhibitors, particularly for identifying novel chemical scaffolds that maintain the essential bioactivity profile while exploring diverse chemical space .

• How do advanced enzymatic assays contribute to understanding DprE1 inhibition mechanisms?

  Sophisticated enzymatic assays provide critical insights into the mechanisms of DprE1 inhibition:

  Spectrophotometric FAD Reduction Assays:

  • Direct monitoring of the DprE1 catalytic cycle through FAD redox state changes

  • Enables kinetic analysis of inhibition (Ki determination, inhibition modality)

  • Allows distinction between competitive, non-competitive, and uncompetitive inhibition

  • Methodology includes continuous monitoring of absorbance at 450 nm (FAD) or use of redox-sensitive dyes

  Stopped-Flow Kinetics:

  • Captures rapid reaction phases in DprE1 catalysis

  • Resolves individual steps in the reaction mechanism

  • Determines rate-limiting steps affected by inhibitors

  • Provides insights into covalent inhibitor reaction rates

  Mass Spectrometry-Based Approaches:

  • Identifies covalent modification sites through peptide mapping

  • Determines stoichiometry of inhibitor binding

  • Characterizes reaction intermediates in inhibition mechanism

  • Monitors protein conformational changes upon inhibitor binding

  Surface Plasmon Resonance (SPR):

  • Measures real-time binding kinetics (kon and koff rates)

  • Distinguishes between different binding mechanisms

  • Determines residence times of inhibitors

  • Correlates binding parameters with antimycobacterial activity

  Thermal Shift Assays (TSA):

  • Confirms direct binding through protein stabilization

  • Allows comparative analysis of binding strength across inhibitor series

  • Identifies synergistic binding of multiple ligands

  • Detects conformational changes induced by inhibitor binding

  These advanced enzymatic approaches have revealed that effective DprE1 inhibitors typically display either irreversible covalent inhibition targeting Cys387 or high-affinity non-covalent binding with prolonged residence times, both leading to sustained enzyme inactivation .

• What approaches can optimize the pharmacokinetic properties of DprE1 inhibitors?

  Strategic optimization of DprE1 inhibitor pharmacokinetics employs multiple complementary approaches:

  Structure-Guided Modifications:

  • Introduction of metabolically stable groups at vulnerable positions

  • Strategic placement of solubilizing groups away from key binding interactions

  • Rigidification of flexible regions to reduce metabolic susceptibility

  • Fine-tuning of pKa values to optimize oral absorption and tissue distribution

  Prodrug Strategies:

  • Development of prodrugs to overcome solubility limitations

  • Targeted release in mycobacterial environments

  • Reduced systemic exposure to reactive groups

  • Enhanced tissue penetration, particularly to granulomas

  Formulation Approaches:

  • Nanoparticle formulations to improve solubility and stability

  • Lipid-based formulations for compounds with high logP values

  • Controlled-release systems for sustained exposure

  • Local delivery systems for targeted pulmonary administration

  Combinatorial Chemistry:

  • Parallel synthesis of analog libraries focused on pharmacokinetic improvements

  • Rapid assessment of structure-property relationships

  • Identification of optimal substitution patterns for metabolic stability

  Predictive Modeling:

  • In silico ADMET prediction to prioritize compounds for synthesis

  • Physiologically-based pharmacokinetic (PBPK) modeling to predict human pharmacokinetics

  • Quantitative structure-property relationship (QSPR) models for key parameters

  The optimization process must balance multiple parameters, as evidenced by successful DprE1 inhibitors that typically display:

  • C log P values between 2.4 and 5.0

  • Moderate topological polar surface area (70-120 Ų)

  • Limited number of hydrogen bond donors (0-2)

  • Controlled rotatable bond count (4-10)

  These approaches have successfully enhanced the pharmaceutical properties of DprE1 inhibitors while maintaining potent antimycobacterial activity .

• How can fragment-based approaches accelerate DprE1 inhibitor discovery?

  Fragment-based drug discovery (FBDD) offers powerful advantages for developing novel DprE1 inhibitors:

  Methodological Framework:

  • Fragment Library Design: Curation of diverse, rule-of-three compliant fragments (MW <300 Da, cLogP <3, HBD ≤3, HBA ≤3, rotatable bonds ≤3)

  • Primary Screening: Thermal shift assays, ligand-observed NMR, and surface plasmon resonance to detect fragment binding

  • Structural Characterization: X-ray crystallography of DprE1-fragment complexes to determine binding modes

  • Fragment Optimization: Structure-guided elaboration to enhance potency while maintaining ligand efficiency

  Strategic Applications for DprE1:

  • Targeting Specific Binding Site Regions:

    • Fragments binding near Cys387 (for covalent inhibitor development)

    • Fragments exploiting unique pockets not utilized by existing inhibitors

    • Fragments that can disrupt protein flexibility or induce conformational changes

  • Fragment Elaboration Approaches:

    • Fragment growing: Extending fragments into adjacent pockets

    • Fragment linking: Connecting fragments binding to proximal sites

    • Fragment merging: Combining features from overlapping fragments

  Advantages in DprE1 Context:

  • Efficient exploration of chemical space with fewer compounds

  • Identification of novel binding modes and interaction patterns

  • Better physicochemical properties in final compounds

  • Reduced attrition during optimization

  Case Study Applications:

  • Fragment-derived replacements for problematic nitro groups in covalent inhibitors

  • Identification of novel core structures while maintaining key interactions

  • Development of fragments targeting resistance-associated mutations

  This methodological approach has proven valuable for discovering non-covalent DprE1 inhibitors with diverse chemotypes and favorable drug-like properties, complementing traditional high-throughput screening approaches .

• What considerations are important when designing DprE1 inhibitors for drug-resistant tuberculosis?

  Developing DprE1 inhibitors effective against drug-resistant tuberculosis requires strategic considerations:

  Resistance Mechanism Analysis:

  • Characterization of mutations in DprE1 that confer resistance to existing inhibitors

  • Identification of cross-resistance patterns across inhibitor classes

  • Understanding of compensatory mechanisms employed by resistant strains

  • Mapping of resistance mutations onto DprE1 structure to visualize impact on binding site

  Strategic Design Approaches:

  • Targeting conserved residues essential for enzyme function

  • Developing inhibitors with multiple binding modes to increase resistance barrier

  • Creating compounds that form interactions with backbone atoms rather than mutable side chains

  • Designing flexible inhibitors that can adapt to binding site changes

  Combination Strategies:

  • Dual-targeting inhibitors affecting both DprE1 and a second essential target

  • Synergistic combinations with inhibitors of other pathways

  • Evaluation of combination potential with existing first-line and second-line TB drugs

  • Identification of combinations that suppress resistance emergence

  Proactive Resistance Assessment:

  • In vitro resistance generation studies during lead optimization

  • Testing against panels of clinical MDR/XDR-TB isolates

  • Computational prediction of resistance-conferring mutations

  • Hollow fiber infection model studies to assess resistance development under different dosing regimens

  These considerations are especially important given the emergence of extensively drug-resistant tuberculosis strains, where new mechanisms of action are crucial for effective treatment .