Recombinant Bdellovibrio bacteriovorus D-alanine--D-alanine ligase (ddl)

What is the role of D-alanine--D-alanine ligase in Bdellovibrio bacteriovorus and how does it differ from other bacterial Ddl enzymes?

D-alanine--D-alanine ligase (Ddl) in B. bacteriovorus catalyzes the ATP-dependent formation of the D-alanyl-D-alanine dipeptide, which is essential for bacterial cell wall peptidoglycan biosynthesis during the cytoplasmic stage. This enzyme plays a critical role in the predator's unique lifecycle, particularly during the intraperiplasmic growth phase when the bacterium must synthesize new cell wall material for filamentous growth and subsequent septation into multiple progeny cells .

While the core catalytic function is conserved across bacterial species, B. bacteriovorus Ddl may have unique properties related to its predatory lifestyle. Like other Ddl enzymes, it requires ATP and is activated by K+ ions. The enzyme consists of three domains: an N-terminal domain, a central domain, and a C-terminal domain, with the ATP-binding site formed by the ATP-grasp fold .

Comparative analysis with other bacterial Ddl enzymes shows:

Methodological approach for functional characterization: Express recombinant B. bacteriovorus Ddl in E. coli, purify using affinity chromatography, and measure enzyme activity through ATP consumption or D-Ala-D-Ala formation using HPLC or coupled enzyme assays. Compare kinetic parameters with Ddl enzymes from non-predatory bacteria to identify functional differences.

What are the optimal conditions for expressing and purifying recombinant B. bacteriovorus Ddl?

Expression and purification of recombinant B. bacteriovorus Ddl requires careful optimization due to potential toxicity and solubility issues. The following protocol has proven effective based on similar ATP-grasp enzymes:

Expression system:

• Vector: pET-based expression vectors (pET28a with N-terminal His-tag)

• Host strain: E. coli BL21(DE3) or Rosetta(DE3) for rare codon optimization

• Growth conditions: LB media supplemented with appropriate antibiotics

Induction protocol:

• Grow cultures at 37°C until OD600 reaches 0.6-0.8

• Induce with 0.1-0.5 mM IPTG

• Continue growth at 18-20°C for 16-18 hours (lower temperature improves protein solubility)

Purification strategy:

• Cell lysis: Sonication or French press in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM DTT, and protease inhibitors

• IMAC: Ni-NTA affinity chromatography with imidazole gradient (20-250 mM)

• Size exclusion: Further purification via gel filtration (Superdex 200)

• Storage buffer: 25 mM Tris-HCl pH 7.5, 100 mM NaCl, 5 mM MgCl2, 1 mM DTT, 10% glycerol

Critical considerations:

• Expression of B. bacteriovorus proteins in E. coli has been demonstrated successfully, as noted in prior research with plasmids bearing broad-range origin of replication RSF1010

• Add 5-10 mM MgCl2 to all buffers to stabilize the ATP-binding domain

• Include K+ (50-100 mM KCl) in activity assays as Ddl is K+-dependent

• Consider co-expression with chaperones if solubility is problematic

Typical yields are 5-10 mg of purified protein per liter of culture, with >95% purity as assessed by SDS-PAGE.

How can we assess the kinetic parameters of recombinant B. bacteriovorus Ddl?

Accurate determination of kinetic parameters for recombinant B. bacteriovorus Ddl requires robust assay methods. Several complementary approaches are recommended:

Coupled enzyme assay

This method links ADP production to NADH oxidation for spectrophotometric monitoring:

• Reaction mixture:

  • 50 mM HEPES pH 7.5

  • 10 mM MgCl2

  • 50 mM KCl

  • 2.5 mM phosphoenolpyruvate

  • 0.2 mM NADH

  • 2-5 U/ml pyruvate kinase

  • 2-5 U/ml lactate dehydrogenase

  • Variable ATP (0.01-2 mM)

  • Variable D-Ala (0.05-10 mM)

  • Purified Ddl (50-200 nM)

• Measurement: Monitor decrease in absorbance at 340 nm (NADH oxidation)

• Data analysis: Use Michaelis-Menten, Lineweaver-Burk, or non-linear regression analysis

HPLC quantification of D-Ala-D-Ala

Direct quantification of product formation:

• Reaction conditions:

  • 50 mM Tris-HCl pH 8.0

  • 10 mM MgCl2

  • 50 mM KCl

  • 5 mM ATP

  • Variable D-Ala concentrations

  • Purified Ddl enzyme

• Sampling: Quench reaction at various timepoints with equal volume of methanol

• Analysis: Derivatize samples with FDAA (Marfey's reagent) and analyze by reverse-phase HPLC

• Quantification: Compare peak areas to D-Ala-D-Ala standards

Expected kinetic parameters (based on related Ddl enzymes):

Inhibition studies:

For D-cycloserine inhibition, use various inhibitor concentrations (0.01-1 mM) and determine IC50 and Ki values through Dixon plots. D-cycloserine inhibits Ddl competitively with respect to D-Ala, as demonstrated in other bacterial Ddl studies .

How does B. bacteriovorus Ddl activity change during different stages of its predatory lifecycle?

B. bacteriovorus undergoes a complex lifecycle alternating between free-swimming attack phase and intraperiplasmic growth phase. Understanding Ddl activity during these different stages requires specialized techniques:

Methodological approach:

• Synchronized cultures: Use prey/predator synchronized cultures with defined time points (e.g., 0, 15, 30, 60, 120, 180, 240 min post-infection)

• RNA expression analysis: Quantify ddl transcript levels using RT-qPCR or RNA-seq at each timepoint

• Protein-level analysis:

  • Western blotting with anti-Ddl antibodies

  • Activity assays from cell extracts at different timepoints

  • Fluorescent tagging of Ddl to track localization during predation cycle

• In situ activity monitoring: Use fluorescent D-amino acid (FDAA) analogs to visualize areas of active peptidoglycan synthesis

Expected pattern of Ddl activity:

Current research indicates that B. bacteriovorus filament growth involves complex peptidoglycan synthesis patterns, with possible "dispersed growth along the cell by addition of new material in multiple patches" . FDAA labeling studies have shown that sites of peptidoglycan insertion can be visualized directly, suggesting that Ddl activity might be spatially regulated during the predatory cycle .

To correlate Ddl activity with growth patterns, combine fluorescent D-amino acid labeling with time-lapse microscopy of fluorescently tagged Ddl to track both enzyme localization and activity simultaneously.

What structural features of B. bacteriovorus Ddl contribute to its substrate specificity and potential for inhibitor design?

Understanding the structural features of B. bacteriovorus Ddl is crucial for rational inhibitor design and explanation of its substrate specificity:

Key structural elements:

• ATP-binding pocket: The ATP-grasp fold forms a distinctive binding pocket composed of residues from all three domains, with specific residues coordinating Mg2+ ions necessary for catalysis .

• D-Ala binding sites: Two distinct binding sites with different affinities:

  • D-Ala1 site: Higher affinity, typically Km in the range of 0.1-0.5 mM

  • D-Ala2 site: Lower affinity, typically Km in the range of 1.0-5.0 mM

• Active site residues: Conserved catalytic residues typically include lysine (for ATP binding) and glutamate/aspartate residues (for D-Ala coordination).

• Omega loop: This flexible loop region closes over the active site upon substrate binding and is critical for catalysis.

Methodological approaches for structural determination:

• X-ray crystallography:

  • Crystallize purified B. bacteriovorus Ddl in various states:

    • Apo-enzyme

    • Enzyme-ATP complex

    • Enzyme-ATP-D-Ala complex

    • Enzyme-inhibitor complex (e.g., with D-cycloserine)

  • Optimal crystallization conditions typically include PEG 3350-8000 (10-20%), pH 6.5-8.0, with additives like MgCl2 and KCl

• Cryo-EM for structure determination if crystallization proves challenging

• In silico modeling:

  • Homology modeling based on available Ddl structures (e.g., 4C5A, 4ME6)

  • Molecular dynamics simulations to analyze conformational changes during catalysis

  • Docking studies to identify potential inhibitor binding sites

Insights from related Ddl structures:

Based on crystal structures of D-alanyl-D-alanine ligase from other bacteria , critical features likely include:

• A catalytic triad for ATP binding and phosphoryl transfer

• Specific residue(s) that determine D-alanine specificity over other amino acids

• Conformational changes upon substrate binding that align substrates for catalysis

The study of X. oryzae Ddl revealed that "compared with d-alanyl-d-alanine and ATP-bound TtDDL structure, the γ-phosphate of ATP in XoDDL structure was shifted outside toward solution" , suggesting potential species-specific differences in catalytic mechanism that might also be present in B. bacteriovorus Ddl.

How does inhibition of B. bacteriovorus Ddl affect its predatory lifecycle?

Inhibition of Ddl in B. bacteriovorus likely has profound effects on its predatory lifecycle, as peptidoglycan synthesis is critical for growth inside prey cells. Understanding these effects requires specialized experimental approaches:

Experimental methodologies:

• Chemical inhibition studies:

  • Treat B. bacteriovorus with sub-lethal concentrations of D-cycloserine (0.1-10 μg/ml)

  • Observe effects on predatory efficiency, intraperiplasmic growth, and progeny formation

  • Quantify predation using prey viability assays (e.g., CFU counts, ATP measurements)

• Genetic approaches:

  • Generate conditional ddl mutants using inducible promoters

  • Create point mutations in the active site that reduce catalytic efficiency

  • Use the established genetic tools for B. bacteriovorus manipulation

• Microscopic analysis:

  • Phase-contrast and fluorescence microscopy to observe morphological changes

  • FDAA labeling to visualize alterations in peptidoglycan synthesis

  • Electron microscopy to examine ultrastructural changes

Expected effects of Ddl inhibition:

Quantitative assessment:

Measure the following parameters to quantify the impact of Ddl inhibition:

• Predation efficiency: Reduction in prey viability over time

• Growth rate: Length of predator filament inside prey

• Progeny formation: Number of progeny cells per bdelloplast

• Predatory cycle duration: Time from attachment to progeny release

Studies on other bacteria show that D-cycloserine, a Ddl inhibitor, specifically prevents the synthesis of new cell walls but does not damage mature cell walls . In B. bacteriovorus, this would likely manifest as an inability to complete the intraperiplasmic growth and division phases without affecting the attack phase or initial prey invasion.

What are the methodological challenges in studying Ddl activity during the intraperiplasmic stage of B. bacteriovorus?

Studying Ddl activity during the intraperiplasmic stage presents unique challenges due to the complex predator-prey interaction:

Major methodological challenges:

• Access to intraperiplasmic predator:

  • B. bacteriovorus resides inside the prey periplasm, making direct access difficult

  • Extraction procedures may disrupt natural enzyme activity

• Synchronization of predatory events:

  • Achieving high synchrony requires carefully controlled predator-prey ratios

  • Even in synchronized cultures, there's inherent variability in the precise timing of predatory events

• Distinguishing predator vs. prey activities:

  • Prey bacteria also contain Ddl enzymes

  • Need to differentiate between predator and prey enzyme activities

• Visualizing intraperiplasmic processes:

  • Limited resolution of conventional microscopy for visualizing events inside bdelloplasts

  • Fluorescent labeling may affect natural processes

Solutions and methodological approaches:

• Synchronized cultures:

  • Use prey/predator ratio adjustment for synchronization (typically 3:1 to 10:1)

  • Filter predators to ensure homogeneous attack-phase populations

  • Use centrifugation to synchronize attachment phase

• Genetic approaches:

  • Express fluorescently tagged Ddl in B. bacteriovorus

  • Create prey with distinctive cell walls that can be differentiated from predator structures

• Advanced imaging techniques:

  • Super-resolution microscopy (STORM, PALM) for higher resolution

  • Structured illumination microscopy for improved visualization

  • Live-cell imaging with fluorescent D-amino acids to track active peptidoglycan synthesis

• Host-independent (HI) mutants:

  • Use HI strains of B. bacteriovorus to study Ddl without prey complications

  • Validate findings by comparing with wild-type predatory strains

• Selective enzyme assays:

  • Use species-specific antibodies for immunoprecipitation

  • Develop assays with differential sensitivity to predator vs. prey enzymes

How can recombinant B. bacteriovorus Ddl be utilized in screening for novel antimicrobial compounds?

Recombinant B. bacteriovorus Ddl offers a valuable platform for screening novel antimicrobials, potentially leading to compounds that could work synergistically with predatory bacteria or target specific bacterial pathogens:

High-throughput screening methodologies:

• Enzyme-based primary screening:

  • Coupled enzyme assay: ATP consumption linked to NADH oxidation

    • Assay components: ATP, D-Ala, MgCl2, KCl, phosphoenolpyruvate, NADH, pyruvate kinase, lactate dehydrogenase

    • Detection: Spectrophotometric monitoring at 340 nm

    • Format: 384-well plates, Z' factor >0.7 achievable

  • Malachite green assay: Detection of released phosphate

    • Assay components: ATP, D-Ala, purified enzyme

    • Detection: Colorimetric measurement at 620 nm

• Fragment-based screening:

  • Thermal shift assays to identify stabilizing compounds

  • NMR-based fragment screening

  • Surface plasmon resonance for binding analysis

• Structure-based virtual screening:

  • In silico docking of compound libraries to Ddl active site

  • Molecular dynamics simulations to evaluate binding stability

  • Pharmacophore-based screening

Secondary validation assays:

• IC50 determination: Dose-response curves for hit compounds

• Mechanism of action studies: Enzyme kinetics to determine competitive, non-competitive, or uncompetitive inhibition

• Selectivity profiling: Activity against Ddl enzymes from different bacterial species

• Antimicrobial activity testing: MIC determination against various bacterial pathogens

• Cytotoxicity assessment: Mammalian cell viability assays

• Synergy testing: Combinations with predatory B. bacteriovorus against target pathogens

Data from a model screening campaign:

Recent studies with Ddl inhibitors like IMB-0283 have shown that "the lethal effect...on Mtb was found to act intracellularly in a DdlA-dependent manner. Specifically, IMB-0283 prevented the synthesis of neonatal cell walls but did not damage mature cell walls" . This highlights the potential for Ddl inhibitors to have specific effects on actively growing bacteria.

What genetic modifications can enhance the expression and activity of recombinant B. bacteriovorus Ddl?

Genetic engineering approaches can significantly improve the expression, stability, and catalytic efficiency of recombinant B. bacteriovorus Ddl:

Codon optimization strategies:

• Host-specific codon optimization:

  • Analyze codon usage bias in expression host (e.g., E. coli)

  • Replace rare codons with synonymous frequent codons

  • Adjust GC content to match expression host

• mRNA secondary structure optimization:

  • Eliminate strong secondary structures in mRNA, especially near translation start site

  • Remove internal Shine-Dalgarno-like sequences that may cause translational pausing

Fusion partner approaches:

• Solubility-enhancing tags:

  • MBP (Maltose Binding Protein): Significantly enhances solubility

  • SUMO: Improves folding and solubility, cleavable with SUMO protease

  • Thioredoxin: Small tag that can improve solubility

• Affinity tags for purification:

  • His6-tag: Standard for IMAC purification

  • Strep-tag II: Gentle elution conditions with desthiobiotin

  • FLAG-tag: High-specificity purification

Active site modifications:

• Stability-enhancing mutations:

  • Identify flexible regions using molecular dynamics simulations

  • Introduce disulfide bridges to stabilize flexible loops

  • Replace surface-exposed hydrophobic residues

• Activity-enhancing mutations:

  • Mutate residues in the second D-Ala binding site to improve substrate binding

  • Optimize metal-coordinating residues for better catalysis

  • Modify the ATP-binding pocket for improved ATP binding and hydrolysis

Methodological considerations:

• Expression vector selection:

  • Use tightly controlled inducible promoters to minimize toxicity

  • Consider low-copy vectors if protein is toxic to host

  • Incorporate strong ribosome binding sites

• Host strain selection:

  • Rosetta strains for rare codon supplementation

  • BL21(DE3)pLysS for reduced basal expression

  • Arctic Express for low-temperature expression enhancement

• Induction optimization:

  • Lower IPTG concentrations (0.1-0.2 mM) often improve soluble protein yield

  • Lower temperature induction (16-20°C) enhances proper folding

  • Longer induction times at lower temperatures (16-20 hours)

Historically, genetic manipulation of B. bacteriovorus has been successful: "Homologous recombination has predominantly been employed to elucidate biological functions by creating knock-out or knock-in mutants. Additionally, derivative ori RSF1010 plasmids have been employed to complement these mutations" . These approaches can be adapted for optimizing recombinant Ddl expression.

How does the structure and function of B. bacteriovorus Ddl compare with D-alanine--D-alanine ligases from other bacterial species?

Comparative analysis of B. bacteriovorus Ddl with other bacterial D-alanine--D-alanine ligases reveals important similarities and differences that impact function and potential inhibitor specificity:

Structural comparison:

Based on known Ddl structures and sequence analysis, we can predict key differences:

Functional comparison:

• Kinetic parameters:

• Inhibitor sensitivity:

Methodological approach for comprehensive comparison:

• Sequence alignment and phylogenetic analysis:

  • Multiple sequence alignment of Ddl sequences from diverse bacterial species

  • Phylogenetic tree construction to determine evolutionary relationships

  • Conservation analysis of catalytic residues

• Homology modeling:

  • Construct 3D model of B. bacteriovorus Ddl based on crystal structures

  • Validate model using energy minimization and Ramachandran plots

  • Compare active site geometry with known structures

• Enzyme characterization:

  • Express and purify recombinant enzymes from multiple species

  • Perform side-by-side kinetic analysis under identical conditions

  • Test inhibitor panels against all enzymes

The unique predatory lifestyle of B. bacteriovorus may have influenced the evolution of its Ddl enzyme. Unlike most bacteria, B. bacteriovorus undergoes a complex lifecycle with periods of rapid growth and multiple simultaneous cell divisions, which might be reflected in the properties of its cell wall synthesis enzymes .

What role might B. bacteriovorus Ddl play in the predator's resistance to certain antibiotics?

B. bacteriovorus demonstrates natural resistance to β-lactam antibiotics, which could be connected to unique properties of its peptidoglycan synthesis pathway, including potential distinctive features of its Ddl enzyme:

Antibiotic resistance mechanisms in B. bacteriovorus:

• Natural β-lactam resistance:

  • "The natural resistance of bdellovibrios to β-lactam antibiotics also opens up the possibility for treatments using these bacteria in conjunction with penicillin"

  • This resistance suggests unique features in peptidoglycan synthesis and remodeling

• Potential Ddl-related resistance mechanisms:

  • Modified binding site architecture reducing antibiotic affinity

  • Altered substrate specificity or binding kinetics

  • Potential regulatory adaptations that enable function under antibiotic stress

Experimental approaches to investigate Ddl's role in antibiotic resistance:

• Comparative sensitivity testing:

  • Determine MICs of cell wall-targeting antibiotics against B. bacteriovorus

  • Compare sensitivity of wild-type vs. Ddl-modified strains

• Biochemical characterization:

  • Test sensitivity of purified recombinant B. bacteriovorus Ddl to various antibiotics

  • Determine if enzyme can function with modified substrates (e.g., D-alanyl-D-lactate)

• Structural analysis:

  • Investigate if B. bacteriovorus Ddl has structural features similar to VanA-type ligases

  • Examine ATP binding pocket for modifications that might affect antibiotic binding

• Gene expression analysis:

  • Analyze ddl expression under antibiotic stress

  • Determine if B. bacteriovorus upregulates ddl in response to cell wall antibiotics

Potential mechanisms based on known Ddl variants:

Studies with other bacterial Ddl enzymes have shown that "The E. coli DdlB gained a weak D-ala-D-lac depsipeptide activity following Tyr216 and Ser150 substitutions with phenylalanine and alanine of LmDdl2, respectively" . If B. bacteriovorus Ddl has natural variations at these positions, it might contribute to altered antibiotic sensitivity profiles.

Additionally, "Ddl serves as a marker to predict vancomycin resistance" , suggesting that analyzing the specific features of B. bacteriovorus Ddl might provide insights into its natural antibiotic resistance profile.

Research protocol for investigating potential vancomycin resistance:

• Sequence analysis:

  • Compare B. bacteriovorus Ddl sequence with known VanA, VanB, and VanG-type ligases

  • Focus on residues known to determine substrate specificity (D-Ala vs. D-Lac)

• Site-directed mutagenesis:

  • Create mutations at key positions (particularly the position equivalent to Tyr216 in E. coli DdlB)

  • Express and characterize mutant enzymes

• Substrate specificity testing:

  • Test wild-type and mutant enzymes for ability to use alternative substrates

  • Measure formation of D-Ala-D-Ala vs. D-Ala-D-Lac or D-Ala-D-Ser

• Heterologous expression:

  • Express B. bacteriovorus Ddl in antibiotic-sensitive bacteria

  • Test if expression confers resistance to vancomycin or other antibiotics

What are the challenges and solutions for crystallizing recombinant B. bacteriovorus Ddl for structural studies?

Obtaining high-quality crystals of recombinant B. bacteriovorus Ddl presents several challenges that must be overcome for successful structural determination:

Major crystallization challenges:

• Protein purity and homogeneity:

  • Trace contaminants can inhibit crystal formation

  • Heterogeneous post-translational modifications

  • Partial degradation products

• Conformational flexibility:

  • ATP-grasp enzymes typically undergo significant conformational changes

  • Flexible loop regions can hinder crystal packing

  • Multiple conformational states in solution

• Solubility and stability issues:

  • Aggregation at high concentrations needed for crystallization

  • Limited stability over crystallization time periods

  • Buffer incompatibilities

Methodological solutions:

• Protein production optimization:

  • Multi-step purification: Combine affinity chromatography with ion exchange and size exclusion

  • Limited proteolysis: Remove flexible regions that hinder crystallization

  • Constructs design: Generate truncations or internal deletions of flexible regions

  • Surface engineering: Replace surface-exposed hydrophobic residues with hydrophilic ones

• Crystallization strategies:

  • Ligand co-crystallization: Include ATP/ADP, D-Ala, D-Ala-D-Ala, or inhibitors

  • Crystal seeding: Use microseeds or cross-seeding from related proteins

  • Alternative precipitants: Test PEG of different molecular weights, salts, and organic solvents

  • Additive screening: Include detergents, polyamines, or other stabilizing agents

• Advanced approaches:

  • Fusion partners: Crystallization chaperones like T4 lysozyme or rubredoxin

  • Surface entropy reduction: Replace entropy-rich surface residues (Lys, Glu) with alanines

  • Heavy atom derivatives: Pre-derivatization with heavy atoms for phasing

  • Antibody-assisted crystallization: Use Fab fragments to stabilize specific conformations

Recommended crystallization conditions based on related Ddl structures:

Alternative structural approaches:

If crystallization proves exceptionally challenging:

• Cryo-electron microscopy:

  • Single-particle analysis for high-resolution structure

  • Requires minimal sample amount compared to crystallography

  • Can handle some degree of conformational heterogeneity

• NMR spectroscopy:

  • Solution structure determination

  • Provides dynamic information

  • Limited by protein size (typically <30 kDa domains)

Based on successful structures of related Ddl enzymes, like the D-cycloserine-bound structure (4C5A) and the ATP-bound structure (4ME6) , similar approaches should eventually yield structural insights into B. bacteriovorus Ddl, enabling structure-based drug design and mechanistic studies.

How can isothermal titration calorimetry be applied to study the binding of substrates and inhibitors to B. bacteriovorus Ddl?

Isothermal Titration Calorimetry (ITC) provides comprehensive thermodynamic parameters for binding interactions, making it invaluable for characterizing substrate and inhibitor binding to B. bacteriovorus Ddl:

Experimental design for ITC studies:

• Sample preparation requirements:

  • Purified B. bacteriovorus Ddl: 10-50 μM in cell (2-3 mL)

  • Ligand solution: 10-50× protein concentration in syringe (250-300 μL)

  • Buffer matched precisely between protein and ligand solutions

  • Degassed solutions to prevent air bubble formation

• Experimental parameters:

  • Temperature: Typically 25°C (298K)

  • Reference power: 5-10 μcal/sec

  • Injection volume: 1-2 μL (first injection), 8-10 μL (subsequent)

  • Injection duration: 4-8 seconds

  • Spacing between injections: 120-180 seconds

  • Stirring speed: 750-1000 rpm

• Control experiments:

  • Ligand-to-buffer injections to determine heat of dilution

  • Buffer-to-protein injections to check for dilution effects

  • Positive control using a known binder (e.g., ATP)

ITC applications for B. bacteriovorus Ddl:

• Substrate binding studies:

  • Determine binding affinity (Kd) of ATP, D-Ala

  • Measure thermodynamic parameters (ΔH, ΔS, ΔG)

  • Establish binding stoichiometry for each substrate

  • Assess magnesium and potassium ion effects on binding

• Inhibitor binding characterization:

  • Screen potential inhibitors identified from high-throughput assays

  • Determine structure-activity relationships based on binding parameters

  • Distinguish between competitive and non-competitive inhibitors

• Mechanistic investigations:

  • Establish binding order of substrates

  • Study cooperative effects between binding sites

  • Assess temperature dependence to derive complete thermodynamic profile

Expected data and interpretation:

Data analysis framework:

• Model selection:

  • One-site binding model for simple interactions

  • Two-site binding model if sites have distinct affinities

  • Sequential binding model for cooperative effects

• Parameter extraction:

  • Binding constant (Ka), converted to dissociation constant (Kd = 1/Ka)

  • Enthalpy change (ΔH)

  • Stoichiometry (n)

  • Calculate entropy change: ΔS = (ΔH-ΔG)/T where ΔG = -RTlnKa

• Comparison with kinetic data:

  • Correlate Kd values with Km values from enzyme kinetics

  • Assess relationship between binding affinity and inhibitory potency

ITC studies of D-cycloserine binding to Ddl enzymes have revealed the mechanistic basis for inhibition, showing that "D-cycloserine inhibits Ddl competitively with respect to D-Ala" . Similar studies with B. bacteriovorus Ddl could elucidate whether the predatory lifestyle has influenced the binding properties of this essential enzyme.

How does the expression of recombinant B. bacteriovorus Ddl in host bacteria affect cell wall integrity and antibiotic susceptibility?

Expressing recombinant B. bacteriovorus Ddl in host bacteria can significantly impact their cell wall composition, integrity, and antibiotic susceptibility profiles, providing insights into both the enzyme's function and potential biotechnological applications:

Experimental design to assess effects:

• Expression systems:

  • Inducible expression vectors (pBAD, pET, pTrc)

  • Range of expression levels (low, medium, high)

  • Expression in multiple bacterial species (E. coli, P. aeruginosa, etc.)

• Cell wall analysis methods:

  • Peptidoglycan composition analysis:

    • HPLC analysis of muropeptide profiles

    • Mass spectrometry to identify specific peptidoglycan fragments

    • Fluorescent D-amino acid incorporation patterns

  • Cell morphology assessment:

    • Phase contrast and electron microscopy

    • Cell size and shape measurements

    • Detection of morphological abnormalities

• Antibiotic susceptibility testing:

  • Minimum inhibitory concentration (MIC) determination

  • Disk diffusion assays

  • Time-kill curves

  • Antibiotic combinations (synergy/antagonism)

Expected effects on host bacteria:

• Cell wall composition changes:

  • Altered peptidoglycan cross-linking patterns

  • Modified muropeptide profiles

  • Potential incorporation of non-canonical peptides

• Cellular effects:

  • Potential growth rate changes

  • Morphological abnormalities (filamentation, bulging)

  • Stress response activation

• Antibiotic susceptibility shifts:

  • Altered sensitivity to cell wall-targeting antibiotics

  • Potential resistance to some antibiotics

  • Possible hypersensitivity to others

Data from model experiments:

Studies with other bacterial Ddl enzymes have shown that heterologous expression can significantly impact antibiotic sensitivity. For example, "heterologous expression of dipeptide ligase in vancomycin-resistant lactobacilli increases their sensitivity to vancomycin in a dose-dependent manner" .

The expression of B. bacteriovorus Ddl in E. coli could potentially similarly modify its cell wall composition and antibiotic sensitivity profile. This has both research utility (understanding Ddl function) and potential biotechnological applications (sensitizing resistant bacteria to antibiotics).

What bioinformatic approaches can identify novel inhibitors of B. bacteriovorus Ddl with potential therapeutic applications?

Computational approaches offer powerful tools for identifying potential inhibitors of B. bacteriovorus Ddl, accelerating the drug discovery process:

Structure-based virtual screening strategy:

• Preparation of target structure:

  • Generate homology model if crystal structure unavailable

  • Identify and prepare binding sites (ATP site, D-Ala sites)

  • Add hydrogen atoms, assign proper protonation states

  • Energy minimization to resolve steric clashes

• Compound library preparation:

  • Curate diverse chemical libraries (e.g., ZINC, ChEMBL)

  • Filter for drug-likeness (Lipinski's Rule of Five)

  • Generate multiple conformers for flexible docking

  • Calculate physicochemical properties

• Molecular docking workflow:

  • Initial high-throughput virtual screening (HTVS) mode

  • Standard precision (SP) docking for top 10-20% compounds

  • Extra precision (XP) docking for top 1000 compounds

  • Consensus scoring using multiple algorithms

• Post-docking analysis:

  • Binding energy calculation

  • Analysis of key protein-ligand interactions

  • Visual inspection of top-ranked poses

  • Clustering to ensure chemical diversity

Ligand-based virtual screening approaches:

• Pharmacophore modeling:

  • Develop pharmacophore using known Ddl inhibitors

  • Include essential features (H-bond donors/acceptors, hydrophobic regions)

  • Validate using known active and inactive compounds

  • Screen virtual libraries against the pharmacophore

• Quantitative structure-activity relationship (QSAR):

  • Develop predictive models using known inhibitors

  • Validate models using test compounds

  • Apply to virtual screening hits for activity prediction

• Shape-based screening:

  • Use known inhibitors as 3D-shape queries

  • Screen for compounds with similar spatial arrangements

  • Combine with electrostatic potential matching

Advanced computational methods:

• Molecular dynamics simulations:

  • Assess stability of docked poses

  • Identify cryptic binding pockets

  • Evaluate water-mediated interactions

  • Calculate binding free energies (MM-GBSA, FEP)

• Machine learning approaches:

  • Train ML models on known Ddl inhibitors

  • Use deep learning for feature extraction

  • Implement neural networks for activity prediction

  • Apply transfer learning from related targets

Case study protocol:

• Initial virtual screening:

  • Target the ATP-binding site of B. bacteriovorus Ddl

  • Screen 1 million compounds from ZINC database

  • Select top 10,000 compounds by docking score

• Refinement:

  • Apply pharmacophore filters

  • Calculate MM-GBSA binding energies for top 1,000 compounds

  • Visual inspection of top 200 compounds

  • Select 50-100 diverse compounds for testing

• Experimental validation:

  • Enzyme inhibition assays

  • Determination of IC50 values

  • Binding confirmation (thermal shift, ITC)

  • Antibacterial activity testing

Expected outcomes and success rates:

Successful application of these approaches has been demonstrated with other Ddl enzymes. For M. tuberculosis DdlA, such methods led to the discovery of IMB-0283, "a safe and low-toxicity inhibitor of DdlA...with potent anti-TB activity both in vitro and in vivo" . Similar success might be achieved with B. bacteriovorus Ddl, potentially identifying compounds that could work synergistically with the predatory bacterium in therapeutic applications.