Recombinant Acinetobacter sp. Pantothenate synthetase (panC)

What is pantothenate synthetase (PanC) and what is its function in Acinetobacter species?

Pantothenate synthetase (PanC; EC 6.3.2.1) is an essential enzyme encoded by the panC gene that catalyzes the final step in the biosynthesis of pantothenate (vitamin B5). In Acinetobacter species, as in other bacteria, PanC catalyzes the ATP-dependent condensation of D-pantoate and β-alanine to form pantothenate, which is a key precursor for the biosynthesis of coenzyme A (CoA) and acyl carrier protein (ACP) . These cofactors are essential for bacterial growth and metabolism, participating in numerous cellular processes including fatty acid biosynthesis and energy production.

Why is PanC considered an attractive drug target in bacterial pathogens?

PanC is considered an attractive drug target for several compelling reasons:

• Essentiality: The enzyme is essential for the in vitro growth of bacterial pathogens, including Acinetobacter species .

• Absence in mammals: Mammals lack the pantothenate biosynthesis pathway, obtaining vitamin B5 through their diet instead . This absence creates a significant selectivity window, reducing the risk of off-target effects.

• Structural uniqueness: The structure of bacterial PanC differs significantly from any mammalian enzymes .

• Pathway criticality: As pantothenate is a precursor to CoA and ACP, inhibiting PanC blocks essential metabolic pathways in bacteria .

• Conservation among bacteria: While variations exist, the enzyme is conserved across bacterial species, allowing for potential broad-spectrum antibacterial development .

How does the structure and mechanism of PanC function differ between Acinetobacter species and other bacteria?

Pantothenate synthetase in Acinetobacter baumannii exhibits several distinctive features compared to other bacterial species:

What genomic context surrounds the panC gene in Acinetobacter species?

In Acinetobacter species, the panC gene exists within a dynamic genomic context affected by recombination events. Analysis of the Acinetobacter pan-genome reveals:

• Genomic plasticity: A. baumannii demonstrates significant genomic plasticity, with homologous recombination occurring across approximately 20% of the genome .

• Core vs. accessory genome: The panC gene is typically part of the core genome of approximately 2700 coding sequences (CDSs) shared among members of the Acinetobacter calcoaceticus-baumannii (Acb) complex .

• Recombination hotspots: Certain regions of the A. baumannii genome, particularly those encoding cell surface-associated proteins, are more prone to homologous recombination .

• Strain variation: Different clinical isolates of A. baumannii may exhibit slight variations in the panC gene due to homologous recombination between strains .

• Horizontal gene transfer: The pan-genome analysis suggests that some genes, including potentially modified variants of metabolic genes like panC, can be acquired through horizontal gene transfer followed by homologous recombination .

What are the optimal conditions for expressing and purifying recombinant Acinetobacter sp. PanC?

Based on reported methodologies for Acinetobacter baumannii PanK and other bacterial PanC proteins, the following conditions are recommended:

Expression system:

• Host: E. coli BL21(DE3) or similar expression strain

• Vector: pET series with His-tag for purification

• Induction: 0.5-1.0 mM IPTG at OD600 of 0.6-0.8

• Temperature: 18-25°C for 16-18 hours (to maximize soluble protein)

Purification protocol:

• Cell lysis in buffer containing 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10% glycerol, 1 mM DTT

• Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin

• Size exclusion chromatography using Superdex 75/200 column

• Final buffer: 25 mM Tris-HCl pH 7.5, 150 mM NaCl, 5% glycerol, 1 mM DTT

Quality assessment should include SDS-PAGE, dynamic light scattering to confirm monodispersity (similar to the reported hydrodynamic radius corresponding to 29.55 kDa for AbPanK) , and enzymatic activity assays to ensure functional protein.

How can high-throughput methods be optimized for screening inhibitors of Acinetobacter sp. PanC?

A robust high-throughput screening (HTS) approach for Acinetobacter sp. PanC inhibitors should incorporate:

Primary assay design:

• Coupling enzyme approach: Utilize a coupled enzymatic assay similar to that described for M. tuberculosis PanC , involving myokinase, pyruvate kinase, and lactate dehydrogenase to measure ATP consumption spectrophotometrically.

• Fluorescence-based alternatives: Consider fluorescence-based assays monitoring changes in intrinsic tryptophan fluorescence upon inhibitor binding.

• Thermal shift assays: Employ differential scanning fluorimetry to identify compounds that alter the thermal stability of PanC.

Assay optimization parameters:

• Buffer composition: 50 mM HEPES pH 7.5, 10 mM MgCl2, 20 mM KCl

• Enzyme concentration: 20-50 nM (determined by activity titration)

• Substrate concentrations: At or slightly below Km values

• DMSO tolerance: Validate up to 2% final concentration

• Z' factor: Optimize to achieve >0.7 for robust screening

• Controls: ATP competitive inhibitors as positive controls

Secondary assay cascade:

• Dose-response confirmation in primary assay

• Orthogonal binding assays (ITC, SPR)

• Mode of inhibition studies

• Whole-cell activity against Acinetobacter species

• Cytotoxicity against mammalian cell lines

This approach has proven successful in identifying novel inhibitor classes for related enzymes, such as the 3-biphenyl-4-cyanopyrrole-2-carboxylic acids identified for M. tuberculosis PanC .

What structural features distinguish Type III pantothenate kinase in A. baumannii from other bacterial species, and how does this inform inhibitor design?

While the search results focus more on pantothenate synthetase than kinase, the information on Type III pantothenate kinase (PanK) from A. baumannii provides relevant structural insights:

Distinctive structural features of A. baumannii PanK:

• Low sequence identity: AbPanK exhibits only about 28% sequence identity with PanK enzymes from other bacterial species .

• Substrate binding pocket differences: AbPanK shows strong affinity for pantothenate (Kd = 1.2 × 10^-8 M) but moderate affinity for ATP (Kd = 3.7 × 10^-3 M) .

• Crystallographic parameters: AbPanK crystallizes in P2 space group with unique cell dimensions that differ from other bacterial PanK structures .

Implications for inhibitor design:

• Target unique binding sites: Focus on regions of structural divergence between AbPanK and human homologs.

• Exploit differential substrate affinities: The differential binding affinities for pantothenate versus ATP suggest that pantothenate-competitive inhibitors might be particularly effective.

• Structure-based design approach: Using the crystallographic data, design compounds that optimize interactions with the unique features of the AbPanK binding pocket.

• Selective inhibition: The structural differences provide opportunities for developing inhibitors selective for Acinetobacter species over other bacteria or human enzymes.

How can homologous recombination approaches be used to study PanC function in Acinetobacter species?

Homologous recombination, a prominent feature in Acinetobacter genomics, provides powerful tools for studying PanC function:

Experimental approaches:

• Gene replacement strategies: Use homologous recombination to replace the native panC gene with modified versions (point mutations, deletions) to study structure-function relationships.

• Recombineering methods: Adapt recombineering techniques used in other bacteria for efficient genetic manipulation of Acinetobacter panC.

• CRISPR-Cas9 assisted recombination: Utilize CRISPR-Cas9 to enhance the efficiency of homologous recombination for panC modification.

Specific applications:

• Essential gene analysis: Create conditional panC mutants using recombination-based approaches to study the essentiality of different PanC domains.

• Domain swapping: Generate chimeric PanC proteins with domains from different bacterial species to identify species-specific functional regions.

• Reporter gene fusions: Create transcriptional or translational fusions to study panC regulation under different conditions.

• In vivo structure-function analysis: Introduce specific mutations based on structural data to test hypotheses about catalytic mechanisms.

Considerations for Acinetobacter:

• The natural competence of many Acinetobacter species facilitates DNA uptake for recombination .

• The presence of CRISPR systems in some Acinetobacter strains may affect recombination efficiency .

• Homologous recombination frequencies vary across different genomic regions and between different Acinetobacter strains .

What are the most reliable assays for measuring PanC activity in vitro, and how should they be optimized?

Several complementary assays can be used to measure Acinetobacter PanC activity:

Spectrophotometric coupled assays:

• ATP consumption monitoring: The standard approach couples ATP consumption to NADH oxidation via auxiliary enzymes (myokinase, pyruvate kinase, lactate dehydrogenase) .

  • Optimization parameters: Enzyme ratios, buffer composition (pH 7.0-8.0), metal ion concentration (5-10 mM Mg²⁺)

  • Detection: 340 nm absorbance decrease

  • Sensitivity: Low-to-moderate (μM range)

Direct product formation assays:

• HPLC-based detection: Direct quantification of pantothenate formation

  • Sample preparation: Quench reaction with acid/base, derivatize if needed

  • Column: C18 reverse phase

  • Detection: UV absorbance (205-210 nm)

  • Sensitivity: Moderate-to-high (nM range)

• Mass spectrometry: LC-MS/MS detection of pantothenate

  • Advantages: High specificity, can detect intermediates

  • Mode: Negative ion mode, multiple reaction monitoring

  • Sensitivity: Very high (pM range)

  • Limitations: Requires specialized equipment

Isothermal titration calorimetry (ITC):

• Directly measures heat released during catalysis

• Provides thermodynamic parameters alongside kinetic data

• Particularly useful for determining binding constants and stoichiometry

Recommended consensus approach:
For thorough characterization, combine:

• Coupled spectrophotometric assay for routine kinetic measurements

• HPLC or LC-MS/MS validation of direct product formation

• ITC for detailed binding studies of substrates and inhibitors

Each assay should be validated with appropriate controls, including no-enzyme and no-substrate controls, to ensure specificity and reliability.

How can crystallographic studies of Acinetobacter sp. PanC be optimized for structural analysis?

Based on reported crystallographic studies of Acinetobacter proteins and related PanC enzymes, the following optimization strategies are recommended:

Initial crystallization screening:

• Protein preparation: Ensure >95% purity by SDS-PAGE, monodispersity by dynamic light scattering

• Concentration range: 5-15 mg/mL in 25 mM Tris-HCl pH 7.5, 150 mM NaCl, 1 mM DTT

• Commercial screens: Start with sparse matrix screens (Hampton, Molecular Dimensions)

• Techniques: Vapor diffusion (sitting and hanging drop), batch crystallization

• Temperature: Screen at both 4°C and 20°C

Optimization strategies:

• Additive screening: Use Hampton Additive Screen to improve crystal quality

• Seeding approaches: Microseed matrix screening for crystal optimization

• Co-crystallization: With substrates, products, or inhibitors (1-5 mM)

• Surface entropy reduction: Consider mutating surface residues to enhance crystal contacts

Data collection considerations:

• Cryoprotection: Test glycerol, ethylene glycol, and PEG 400 (10-25%)

• Remote data collection: Synchrotron radiation facilities for high-resolution data

• Strategy: Collect multiple datasets at different resolutions and orientations

Structure determination approaches:

• Molecular replacement: Using M. tuberculosis PanC structure as search model

• Experimental phasing: Consider selenomethionine labeling if molecular replacement fails

• Refinement strategy: Use maximum likelihood refinement with careful model building

Based on the reported crystallization of AbPanK, which crystallized in P2 space group with cell dimensions of a = 165 Å, b = 260 Å, and c = 197 Å and α = 90.0, β = 113.60, γ = 90.0 , expect large unit cells that may require special attention during data collection and processing.

What site-directed mutagenesis strategies are most informative for studying the catalytic mechanism of Acinetobacter sp. PanC?

Based on the known mechanism of pantothenate synthetase from related bacteria, the following site-directed mutagenesis approach is recommended:

Key residues for mutation based on homology and mechanistic understanding:

Experimental design considerations:

• Conservative vs. disruptive mutations: Include both types to distinguish between essential and modulatory roles

• Double mutant cycle analysis: Test combinations of mutations to identify cooperative interactions

• Structure-guided approach: Use available structural data from homologous PanC enzymes to inform mutation selection

• Rescue experiments: Test chemical rescue of activity in catalytic mutants

Kinetic characterization of mutants:

• Determine kcat and Km for both substrates (ATP and pantoate)

• Measure binding affinities using ITC or fluorescence methods

• Analyze rate-limiting step changes using pre-steady-state kinetics

• Test alternative substrates to probe binding pocket alterations

This approach will provide insights into the catalytic mechanism, substrate specificity determinants, and potential inhibitor design strategies for Acinetobacter sp. PanC.

How should contradictory kinetic data for Acinetobacter sp. PanC be reconciled?

When faced with contradictory kinetic data for Acinetobacter sp. PanC, researchers should consider a systematic approach to reconciliation:

Sources of variation in reported kinetic parameters:

• Experimental conditions: Differences in buffer composition, pH, temperature, and ionic strength can significantly alter kinetic parameters.

• Protein preparation: Variations in expression systems, purification methods, and storage conditions can affect enzyme activity.

• Assay methodology: Different assay formats (coupled vs. direct) may yield different apparent kinetic values.

• Strain differences: Natural sequence variations among Acinetobacter strains due to recombination events .

Systematic reconciliation approach:

• Standardized conditions: Re-evaluate kinetic parameters under identical conditions:

  • Buffer: 50 mM HEPES, pH 7.5, 10 mM MgCl₂

  • Temperature: 25°C

  • Substrate ranges: 0.1-10× Km

• Multiple methodologies: Confirm parameters using at least two independent assay methods

• Global data fitting: Apply global fitting of kinetic data to integrated rate equations

• Statistical analysis: Calculate 95% confidence intervals for all kinetic parameters

Case study example:
For Acinetobacter PanC, if different studies report Km values for pantoate ranging from 20-200 μM, reconciliation might involve:

• Direct comparison under identical conditions

• Examination of protein sequence to identify potential polymorphisms

• Consideration of homologous recombination events that might have altered the gene sequence

• Analysis of potential allosteric effects from different buffer components

How can evolutionary analysis of panC genes across Acinetobacter species inform drug design strategies?

Evolutionary analysis of panC genes provides critical insights for drug design:

Key evolutionary analyses to perform:

• Phylogenetic reconstruction: Build phylogenetic trees of panC sequences from diverse Acinetobacter strains

• Selection pressure analysis: Calculate dN/dS ratios to identify conserved vs. variable regions

• Recombination detection: Apply methods like SplitsTree analysis to identify recombination events

• Ancestral sequence reconstruction: Infer ancestral PanC sequences to understand evolutionary trajectories

Translation to drug design:

• Conservation-guided targeting: Highly conserved catalytic residues represent ideal drug targets due to:

  • Functional constraints limiting mutation

  • Essential nature of the conserved regions

  • Reduced likelihood of resistance development

• Resistance prediction: Regions with evidence of positive selection or recombination may be prone to resistance mutations

  • Example: If homologous recombination is detected in certain PanC domains, as seen in other A. baumannii genes , these regions may be more prone to acquiring resistance-conferring mutations

• Specificity engineering: Target Acinetobacter-specific sequence/structural features identified through comparative analysis

  • Areas with low sequence conservation across species but high conservation within Acinetobacter

  • Unique structural motifs absent in human proteins

• Pan-inhibitor development: Design broad-spectrum inhibitors targeting ultra-conserved features across all bacterial PanC variants

  • Conserved ATP-binding pocket features

  • Essential catalytic machinery preserved across evolution

This evolutionary approach has proven successful in developing therapeutics against other bacterial targets and can guide the rational design of PanC inhibitors with reduced resistance potential.

What statistical approaches are most appropriate for analyzing inhibitor screening data against Acinetobacter sp. PanC?

For robust analysis of high-throughput screening data for PanC inhibitors, the following statistical approaches are recommended:

Quality control metrics:

• Signal-to-background ratio: Aim for S/B > 3

• Coefficient of variation (CV): Maintain CV < 15% for controls

Hit identification methods:

• Percent inhibition cutoffs: Traditional approach using fixed threshold (e.g., >50% inhibition)

• Statistical thresholds: Define hits as compounds with activity > μₙ + 3σₙ

Dose-response analysis:

• Comparative IC₅₀ analysis: Apply extra sum-of-squares F-test to compare potencies

• Global fitting: For mechanism of action studies, fit all curves simultaneously

Advanced statistical considerations:

• Bayesian approaches: Incorporate prior knowledge of related compounds

• Machine learning classification: Train models on known inhibitors to identify novel scaffolds

• Structure-activity relationship (SAR) analysis: Use matched molecular pair analysis (MMPA) to identify key functional groups

Validation criteria:

• Reproducibility: ≥3 independent experiments

• Orthogonal assays: Confirm activity in ≥2 different assay formats

• Counter-screening: Test against related and unrelated enzymes for selectivity

• Statistical significance: Apply appropriate multiple testing corrections

These approaches have been successfully applied in screening campaigns for related enzymes, such as the identification of 3-biphenyl-4-cyanopyrrole-2-carboxylic acids as PanC inhibitors .

How can structural data from homologous PanC enzymes be used to inform studies of Acinetobacter sp. PanC?

Given the limited structural data specifically for Acinetobacter PanC, leveraging homologous enzyme structures is crucial:

Homology modeling approach:

• Template selection: Choose the highest resolution structures of the most closely related PanC enzymes (e.g., M. tuberculosis PanC, which has been crystallized with substrates and inhibitors)

• Sequence alignment: Create a multiple sequence alignment of PanC sequences, with manual curation of active site residues

• Model building: Generate multiple models using programs like MODELLER, Rosetta, or Swiss-Model

• Model validation: Use PROCHECK, MolProbity, and energy minimization to assess model quality

• Refinement: Local refinement of active site residues based on biochemical data

Structural insights application:

• Catalytic mechanism inference: Identify putative catalytic residues based on conserved spatial arrangements:

  • ATP binding pocket

  • Pantoate binding site

  • β-alanine binding region

  • Metal coordination sphere

• Substrate specificity determinants: Analyze binding pocket differences that might explain:

  • The strong affinity for pantothenate (Kd = 1.2 × 10^-8 M) in Acinetobacter PanK

  • Differences in substrate preferences compared to other bacterial enzymes

• Drug design implications: Use structural comparisons to:

  • Identify unique features of the Acinetobacter enzyme

  • Target regions of structural divergence from human enzymes

  • Design inhibitors that exploit specific binding pocket characteristics

Validation experiments:

• Site-directed mutagenesis of predicted key residues

• Binding studies with substrate analogs

• Inhibitor studies testing structure-based predictions

• Crystallization trials guided by homology model insights

This approach has been successful for other essential bacterial enzymes and can accelerate structural understanding of Acinetobacter PanC despite limited direct structural data.

What are the implications of CRISPR/Cas systems in Acinetobacter for genetic manipulation of panC?

The presence of CRISPR/Cas systems in some Acinetobacter strains has significant implications for genetic manipulation of panC:

Current understanding of CRISPR in Acinetobacter:

• Some Acinetobacter strains contain CRISPR arrays and CRISPR-associated genes (cas)

• Strains with CRISPR systems typically have fewer plasmids, suggesting active defense against foreign DNA

• These systems may limit natural homologous recombination in certain genomic regions

Implications for panC manipulation:

• Strain selection considerations:

  • CRISPR-containing strains may resist standard transformation methods

  • Non-CRISPR strains might be more amenable to genetic manipulation

  • Characterize CRISPR status before selecting experimental strains

• Strategy adaptations:

  • Design recombination templates that avoid CRISPR recognition

  • Consider anti-CRISPR proteins as molecular tools

  • Use endogenous CRISPR systems for precise genome editing

• Experimental design modifications:

  • Higher DNA concentrations may overcome CRISPR-mediated resistance

  • Methylation patterns may affect CRISPR recognition

  • Pulsed electroporation may improve transformation efficiency

• CRISPR-based approaches:

  • Repurpose endogenous systems for targeted panC modification

  • Design custom guide RNAs targeting specific panC regions

  • Combine with recombination templates for precise editing

Potential advantages:

• Native CRISPR systems could be repurposed for precise genetic manipulation

• Understanding CRISPR distribution may explain natural variation in panC sequences

• CRISPR biology provides insights into horizontal gene transfer limitations