Recombinant Acinetobacter sp. Phenylalanine--tRNA ligase beta subunit (pheT), partial

What is the phenylalanine-tRNA ligase beta subunit (pheT) and what is its function in Acinetobacter species?

Phenylalanine-tRNA ligase (PheRS) is a class II aminoacyl-tRNA synthetase that specifically attaches phenylalanine to its cognate tRNA molecules, a critical step in protein synthesis. In most bacteria including Acinetobacter species, PheRS exists as a heterotetramer (α₂β₂) where the beta subunit (encoded by the pheT gene) contains the catalytic domain responsible for aminoacylation activity.

The pheT subunit specifically contributes to:

• ATP binding and phenylalanine activation

• Transfer of activated phenylalanine to tRNA^Phe

• Quality control through editing mechanisms that prevent misacylation

• Structural stability of the enzyme complex

In Acinetobacter species, which are increasingly recognized as significant hospital-acquired pathogens, the pheT gene product plays an essential role in protein synthesis and bacterial survival. Recent research has demonstrated that Acinetobacter pittii, a member of this genus, has become an emerging pathogen responsible for outbreaks in healthcare settings worldwide .

What recombinant expression systems are most effective for producing Acinetobacter sp. pheT protein?

For effective recombinant expression of Acinetobacter sp. pheT, several expression systems have been evaluated, with E. coli-based systems demonstrating the highest efficiency. The methodology typically involves:

• Gene cloning: The pheT coding sequence is PCR-amplified from Acinetobacter genomic DNA with appropriate restriction sites incorporated into primers.

• Vector selection: pET-based expression vectors (particularly pET28a) with N-terminal His-tags facilitate purification and are commonly employed due to their tight regulation under T7 promoter control.

• Host selection: E. coli BL21(DE3) and its derivatives are preferred due to reduced protease activity and compatibility with T7-based expression.

• Expression optimization protocol:

  • Cultivation at 16-18°C after induction (rather than 37°C) significantly improves soluble protein yield

  • Induction with 0.1-0.5 mM IPTG at mid-log phase (OD₆₀₀ = 0.6-0.8)

  • Addition of 5-10% glycerol to the culture medium enhances protein stability

For genetic modification of the pheT gene itself, recombineering methods utilizing the bacteriophage λ Red system have proven highly effective. This system employs the phage recombination genes gam, bet, and exo, which work together to facilitate precise genetic modifications .

How can researchers confirm the functionality of recombinant Acinetobacter pheT protein?

Confirming the functionality of recombinant pheT requires multiple complementary approaches:

• Enzymatic activity assay: The aminoacylation activity is measured through ATP-PPi exchange assays or tRNA charging assays. The standard protocol involves:

  • Incubating purified recombinant pheT with:

    • ATP (2 mM)

    • L-phenylalanine (100 μM)

    • Total or purified tRNA^Phe (10 μM)

    • Reaction buffer (100 mM HEPES pH 7.5, 10 mM MgCl₂, 50 mM KCl)

  • After incubation (15-30 minutes at 37°C), reactions are analyzed by:

    • TCA precipitation for radioactive assays

    • HPLC analysis for non-radioactive methods

    • Gel electrophoresis for detection of charged tRNA

• Structural verification: Circular dichroism spectroscopy to confirm proper folding, comparing spectra with native protein profiles.

• Complementation assays: Introduction of recombinant pheT into conditional pheT mutants to verify rescue of growth phenotypes.

• Protein-protein interaction studies: Co-immunoprecipitation or two-hybrid assays to confirm proper interaction with the alpha subunit (pheS) and formation of the functional heterotetramer.

Similar methodological approaches have been successfully applied to other bacterial systems, including validation of genetic variants affecting antimicrobial resistance in H. pylori .

What are the optimal genetic engineering strategies for creating site-directed mutations in Acinetobacter sp. pheT to study structure-function relationships?

For precise genetic engineering of Acinetobacter sp. pheT, a specialized recombineering approach offers superior results compared to traditional methods. This technique employs the bacteriophage λ Red system consisting of:

• λ Red system components:

  • Gam protein: Prevents RecBCD nuclease from degrading linear DNA fragments

  • Beta protein: Promotes annealing of complementary DNA strands

  • Exo protein: Provides 5′→3′ dsDNA exonuclease activity

• Optimized protocol for Acinetobacter sp.:

  • Construction of a temperature-sensitive expression system using the λ pL promoter

  • Tight repression at 30-34°C with the cI857 temperature-sensitive repressor

  • Rapid induction at 42°C for precisely 15 minutes (longer induction increases off-target effects)

  • Return to 30°C to restore repression

• Design parameters for targeting homologies:

  • For dsDNA recombination: 40-60 bp homology arms are sufficient

  • For ssDNA oligonucleotide recombination: 70-mer oligos targeting the lagging strand of DNA replication provide highest efficiency

• Selection strategies:

  • Positive selection: Integration of antibiotic resistance cassettes

  • Counterselection: Use of sacB-based systems for scarless modifications

  • CRISPR-Cas9 screening for markerless mutations

The methodology yields precise modifications with minimal off-target effects, allowing researchers to create specific amino acid substitutions to investigate catalytic mechanisms, antibiotic resistance determinants, or species-specific structural features.

How can next-generation sequencing approaches be applied to study the evolution and diversity of pheT genes across Acinetobacter species?

Next-generation sequencing (NGS) provides powerful approaches for investigating pheT evolution and diversity across Acinetobacter species. A comprehensive methodology involves:

• Sample collection and preparation:

  • Clinical isolates from diverse geographical locations

  • Environmental samples from different ecological niches

  • Reference strains representing different Acinetobacter species

• Custom NGS panel design:

  • Target capture approach similar to the NGS-PHET panel described for H. pylori

  • Core genes: pheT plus flanking regions (±2kb)

  • Additional targets: genes encoding interacting partners and resistance determinants

• Sequencing protocol:

  • Library preparation using fragmentation methods that preserve GC-rich regions

  • Paired-end sequencing (2×150bp) on Illumina platforms

  • Minimum coverage depth of 100× for reliable variant detection

• Bioinformatic analysis pipeline:

  • Quality control and trimming using FastQC and Trimmomatic

  • Reference-based alignment using BWA-MEM or Bowtie2

  • De novo assembly using SPAdes for novel variant discovery

  • Variant calling using GATK or FreeBayes

  • Phylogenetic analysis with RAxML or IQ-TREE

  • Selection pressure analysis with PAML

• Data interpretation framework:

  • Identification of species-specific signatures

  • Detection of recombination events using ClonalFrameML

  • Correlation of genetic variants with phenotypic characteristics

This approach has successfully been applied to other bacterial systems, demonstrating the ability to identify both previously reported and novel variants with potential functional significance . For Acinetobacter species, similar approaches would be valuable given the emergence of A. pittii as a significant hospital-acquired pathogen .

What experimental approaches are most effective for determining the role of pheT in Acinetobacter pathogenicity and antibiotic resistance?

Investigating the role of pheT in Acinetobacter pathogenicity and antibiotic resistance requires a multi-faceted experimental approach:

• Conditional mutant construction:

  • CRISPR interference (CRISPRi) system for tunable repression

  • Antisense RNA expression systems

  • Degron-tagging for controlled protein degradation

• Phenotypic characterization protocols:

  • Growth curve analysis under varying antibiotic concentrations

  • Biofilm formation quantification using crystal violet staining

  • Virulence assessment in infection models:

    • Galleria mellonella larval infection model

    • Mouse pulmonary infection model

    • Human cell line adhesion/invasion assays

• Molecular mechanism investigation:

  • Ribosome profiling to assess translational impacts

  • Protein-protein interaction network mapping via pull-down assays coupled with mass spectrometry

  • Structural analysis of pheT-antibiotic interactions using X-ray crystallography or cryo-EM

• Resistance mechanism elucidation:

  • Directed evolution experiments under antibiotic selection pressure

  • Site-directed mutagenesis of predicted resistance hotspots

  • Heterologous expression of variant pheT alleles in susceptible backgrounds

• Comparative genomics approach:

  • Analysis of pheT sequence variations in resistant vs. susceptible isolates

  • Correlation with minimum inhibitory concentration (MIC) values

  • Identification of co-evolving genes

These approaches have proven effective in studying other bacterial systems, including analyzing antibiotic resistance profiles in clinical isolates of Acinetobacter pittii, which has been identified as responsible for outbreaks in different regions worldwide .

What are the challenges in designing inhibitors targeting Acinetobacter sp. pheT and how can they be addressed through structure-based drug design?

The design of inhibitors targeting Acinetobacter sp. pheT faces several challenges that can be addressed through advanced structure-based approaches:

• Key challenges:

  • Structural conservation across bacterial species may lead to broad-spectrum activity with potential microbiome disruption

  • The large size of the active site accommodating both ATP and phenylalanine

  • Conformational changes during catalysis that affect inhibitor binding

  • Limited structural data specific to Acinetobacter pheT

• Methodological solutions:

  • Homology modeling workflow:

    • Template selection from closely related species (≥60% sequence identity)

    • Multiple-template modeling using MODELLER or SWISS-MODEL

    • Refinement through molecular dynamics simulations (100-500 ns)

    • Validation through Ramachandran analysis and PROCHECK

  • Virtual screening protocol:

    • Receptor preparation using AutoDock Tools

    • Grid box centered on the ATP-binding pocket

    • Two-tiered docking approach:

      • Initial screening with Glide SP or AutoDock Vina

      • Refinement of top hits with Glide XP or GOLD

  • Fragment-based design approach:

    • Thermal shift assays to identify fragment hits

    • X-ray crystallography or NMR to determine binding modes

    • Fragment linking or growing strategies

• Species-specificity strategies:

  • Targeting non-conserved residues at the periphery of the active site

  • Exploiting differences in protein dynamics between species

  • Development of allosteric inhibitors targeting Acinetobacter-specific regulatory sites

• Experimental validation pipeline:

  • Enzymatic inhibition assays (IC₅₀ and Ki determination)

  • Crystallography of inhibitor-bound complexes

  • Cellular penetration assessment

  • Activity testing against clinical isolates

This methodology has been successfully applied to developing targeted therapeutics for other bacterial pathogens, including personalized approaches for H. pylori eradication treatments , and could be adapted for addressing the emerging threat posed by antibiotic-resistant Acinetobacter species .

What are the optimal protein purification protocols for obtaining high-yield, active recombinant Acinetobacter pheT?

Obtaining high-yield, active recombinant Acinetobacter pheT requires a specialized purification protocol designed to maintain the functional integrity of this large, complex protein:

• Optimized lysis buffer composition:

  • 50 mM Tris-HCl, pH 8.0

  • 300 mM NaCl

  • 10% glycerol

  • 1 mM DTT

  • 0.1% Triton X-100 (critical for membrane-associated fractions)

  • Protease inhibitor cocktail (EDTA-free)

• Multi-step purification procedure:

  • Immobilized metal affinity chromatography (IMAC):

    • Ni-NTA resin for His-tagged constructs

    • Low imidazole (10 mM) in wash buffers

    • Step gradient elution (100, 200, 300 mM imidazole)

  • Ion exchange chromatography:

    • Q-Sepharose at pH 8.0 (pheT theoretical pI ~5.3)

    • Linear gradient elution (50-500 mM NaCl)

  • Size exclusion chromatography:

    • Superdex 200 column

    • Running buffer: 25 mM Tris-HCl pH 7.5, 150 mM NaCl, 5% glycerol, 0.5 mM DTT

• Critical considerations:

  • Maintaining temperature at 4°C throughout purification

  • Addition of 5 mM MgCl₂ to all buffers to stabilize protein structure

  • Concentration using centrifugal filters with 100 kDa MWCO (larger than theoretical size to prevent aggregation)

  • Flash-freezing in liquid nitrogen with 10% glycerol for storage

• Quality control assessments:

  • SDS-PAGE analysis (>95% purity)

  • Dynamic light scattering to confirm monodispersity

  • Thermal shift assays to verify stability

  • ATP-PPi exchange assay to confirm activity retention

This protocol typically yields 5-10 mg of pure, active protein per liter of bacterial culture, suitable for structural and functional studies. Similar purification approaches have been successfully applied to other large bacterial proteins involved in translation and antibiotic resistance .

How can researchers develop a reliable assay system for screening inhibitors of recombinant Acinetobacter pheT?

Developing a robust assay system for screening inhibitors of recombinant Acinetobacter pheT requires careful consideration of assay design, controls, and validation steps:

• Primary screening assays:

  a. ATP-PPi exchange assay:

  • Principle: Measures the formation of [³²P]ATP from [³²P]PPi and AMP

  • Assay composition:

    • 100 mM HEPES-KOH (pH 7.5)

    • 10 mM MgCl₂

    • 50 mM KCl

    • 1 mM DTT

    • 2 mM ATP

    • 2 mM [³²P]PPi

    • 1 mM L-phenylalanine

    • 100 nM purified pheT

  • Quantification: Charcoal filtration and scintillation counting

  • Z' factor typically >0.7 when optimized

  b. Aminoacylation assay (non-radioactive):

  • Principle: Measures the formation of Phe-tRNAphe using malachite green detection of released phosphate

  • Assay composition:

    • 100 mM HEPES-KOH (pH 7.5)

    • 10 mM MgCl₂

    • 50 mM KCl

    • 1 mM DTT

    • 2 mM ATP

    • 2 μM tRNAPhe

    • 100 μM L-phenylalanine

    • 50 nM purified pheT

  • Quantification: Absorbance at 620 nm

  • Adaptable to 384-well format for high-throughput screening

• Counterscreens and validation assays:

  • Thermal shift assays to confirm direct binding

  • Surface plasmon resonance for determining binding kinetics

  • Enzyme panel selectivity screening against other aminoacyl-tRNA synthetases

  • Cellular penetration assays using Acinetobacter growth inhibition

• Assay optimization parameters:

  • DMSO tolerance (typically stable up to 2% final concentration)

  • Buffer component optimization (pH 7.2-7.8 range testing)

  • Enzyme concentration titering to ensure linear response

  • Incubation time optimization (typically 15-30 minutes)

• Data analysis framework:

  • Percent inhibition calculation: (1 - (signal_inhibitor - signal_negative) / (signal_positive - signal_negative)) × 100

  • IC₅₀ determination using 4-parameter logistic regression

  • Structure-activity relationship analysis

This comprehensive assay platform enables efficient screening of compound libraries while minimizing false positives and negatives. Similar methodological approaches have been successfully employed in other antimicrobial development programs targeting essential bacterial enzymes .

What are the best approaches for structural characterization of recombinant Acinetobacter pheT and its complexes with substrates or inhibitors?

Comprehensive structural characterization of recombinant Acinetobacter pheT requires integrating multiple complementary techniques:

• X-ray crystallography protocol:

  • Crystallization optimization:

    • Initial screening: Commercial sparse matrix screens (400-800 conditions)

    • Optimization: Varying pH (6.5-8.0), precipitant concentration, and additives

    • Co-crystallization with substrates: Pre-incubation with 2 mM ATP, 2 mM phenylalanine

    • Complex formation: Addition of non-hydrolyzable ATP analogs (AMP-PNP)

    • Crystal improvement: Microseeding, additive screening

  • Data collection strategy:

    • Cryoprotection: 20-25% glycerol or ethylene glycol

    • Resolution target: Better than 2.5 Å

    • Multiple-wavelength anomalous dispersion (MAD) for phasing

    • Complete data sets with redundancy >5

  • Structure determination workflow:

    • Molecular replacement using homologous structures

    • Model building with Coot

    • Refinement with PHENIX or REFMAC5

    • Validation with MolProbity

• Cryo-electron microscopy approach:

  • Sample preparation:

    • Protein concentration: 0.5-2 mg/mL

    • Grid type: Quantifoil R1.2/1.3 with thin carbon support

    • Vitrification: Vitrobot Mark IV (3-4 second blot time)

  • Data acquisition parameters:

    • Magnification: 22,500-36,000×

    • Defocus range: -0.8 to -2.5 μm

    • Total dose: 50-60 e-/Ų

    • Frame count: 40-50

  • Data processing pipeline:

    • Motion correction: MotionCor2

    • CTF estimation: CTFFIND4

    • Particle picking: crYOLO or Topaz

    • 2D classification: RELION or cryoSPARC

    • 3D reconstruction: Non-uniform refinement in cryoSPARC

• Solution NMR for dynamics studies:

  • Selective isotope labeling (¹⁵N, ¹³C)

  • TROSY-based experiments for large proteins

  • Hydrogen-deuterium exchange for conformational studies

• Integrative structural biology approach:

  • Small-angle X-ray scattering (SAXS) for solution conformation

  • Crosslinking mass spectrometry for interface mapping

  • Molecular dynamics simulations (100-500 ns) for conformational sampling

This multi-technique approach provides comprehensive structural insights that can inform structure-based drug design efforts against Acinetobacter species, which have been identified as significant emerging pathogens in healthcare settings .

How should researchers analyze sequence variations in the pheT gene across clinical isolates of Acinetobacter species?

Analysis of pheT sequence variations across clinical isolates requires a comprehensive bioinformatic framework:

• Sequence acquisition and quality control:

  • Whole genome sequencing using Illumina platforms (coverage ≥100×)

  • Quality filtering (Q-score ≥30, adapter removal)

  • Assembly verification using multiple algorithms (SPAdes, MEGAHIT)

• Alignment and variant calling protocol:

  • Progressive multiple sequence alignment using MAFFT G-INS-i algorithm

  • Codon-aware alignment refinement with MACSE

  • Variant calling parameters:

    • Minimum read depth: 20×

    • Minimum variant frequency: 5%

    • Quality score threshold: ≥20

• Classification framework:

  Variant Type	Analysis Method	Significance Assessment
  Synonymous	dN/dS ratio calculation	Potential selection signatures
  Non-synonymous	PROVEAN, SIFT scoring	Functional impact prediction
  Indels	Frameshift analysis	Protein truncation assessment
  Regulatory region	Promoter motif analysis	Expression effect prediction

• Population genetics analysis:

  • Nucleotide diversity (π) calculation

  • Tajima's D statistic for selection pressure

  • FST values for population differentiation

  • Recombination detection with ClonalFrameML

• Correlation with phenotypic data:

  • Antibiotic susceptibility profiles (MIC values)

  • Growth rate measurements

  • Virulence in infection models

  • Statistical testing using linear mixed models

• Visualization and reporting:

  • Haplotype networks using PopART

  • Phylogenetic trees with RAxML (GTR+Γ model)

  • Variant frequency heatmaps

  • Structure mapping of variants using PyMOL

This methodological framework enables identification of clinically relevant variations and evolutionary patterns in the pheT gene. Similar approaches have been successfully applied to analyze genetic determinants of antibiotic resistance in other bacterial pathogens, as demonstrated in H. pylori studies and could be valuable for understanding the emergence of Acinetobacter pittii as a hospital-acquired pathogen .

What statistical approaches are most appropriate for analyzing structure-function relationships in mutated pheT variants?

For robust analysis of structure-function relationships in mutated pheT variants, specialized statistical approaches are required:

• Enzyme kinetics analysis framework:

  • Parameter estimation methods:

    • Non-linear regression using least squares

    • Bayesian inference with MCMC sampling for parameter uncertainty

    • Global fitting of multiple experiments simultaneously

  • Statistical comparison of kinetic parameters:

    • Extra sum-of-squares F-test for nested models

    • Akaike Information Criterion (AIC) for non-nested models

    • Bootstrap resampling for confidence interval estimation

  • Visualization techniques:

    • Michaelis-Menten plots with confidence bands

    • Residual analysis plots

    • Lineweaver-Burk transformations with error propagation

• Multivariate analysis of structure-function datasets:

  • Principal Component Analysis (PCA):

    • Data preprocessing: Standardization and outlier removal

    • Component selection: Kaiser criterion or parallel analysis

    • Interpretation: Variable contribution analysis

  • Partial Least Squares (PLS) regression:

    • Cross-validation: Leave-one-out for small datasets

    • Model evaluation: Q² and R² metrics

    • Variable importance in projection (VIP) scores

  • Multiple correspondence analysis for categorical variables

• Statistical framework for thermal stability comparisons:

  Method	Parameter	Statistical Test	Visualization
  Differential Scanning Fluorimetry	Tm value	One-way ANOVA with Dunnett's post-hoc	Box plots with individual data points
  Circular Dichroism	Denaturation curve	Non-linear regression comparison	Overlay plots with 95% CI bands
  Limited proteolysis	Degradation rate	Survival analysis (log-rank)	Kaplan-Meier curves

• Machine learning applications:

  • Feature selection using random forest importance metrics

  • Support vector machines for classification of functional impact

  • Cross-validation strategies: Nested k-fold (k=5)

  • Performance metrics: Matthews correlation coefficient, precision-recall AUC

These statistical approaches allow for rigorous analysis of how mutations affect pheT structure and function, enabling identification of critical residues and mechanism elucidation. Similar methodological frameworks have been successfully applied to analyze structure-function relationships in other bacterial systems .

How can recombinant Acinetobacter pheT research contribute to new antimicrobial development strategies?

Recombinant Acinetobacter pheT research offers several promising avenues for antimicrobial development:

• Structure-based inhibitor design:

  • Fragment-based approach:

    • Identification of binding hotspots through crystallographic fragment screening

    • Fragment growing/linking strategies

    • Optimization of pharmacophore models

  • Virtual screening workflow:

    • Pharmacophore-based filtering of compound libraries (>1 million compounds)

    • Molecular docking using ensemble receptor conformations

    • Molecular dynamics-based rescoring of top hits

    • In silico ADMET prediction

• Exploitation of species-specific features:

  • Targeting non-conserved residues unique to Acinetobacter species

  • Development of narrow-spectrum agents with reduced impact on microbiome

  • Sequence and structural comparisons between:

    • Pathogenic Acinetobacter species (A. baumannii, A. pittii)

    • Non-pathogenic environmental species

    • Human PheRS

• Combination therapy strategies:

  • Synergistic interactions with existing antibiotics

  • Targeting multiple tRNA synthetases simultaneously

  • Experimental design for synergy testing:

    • Checkerboard assays (8×8 concentration matrix)

    • Time-kill kinetics

    • Fractional inhibitory concentration index calculation

• Resistance mechanism elucidation and countermeasures:

  • Directed evolution experiments:

    • Serial passage under increasing inhibitor concentrations

    • Whole genome sequencing of resistant mutants

    • Reconstruction of mutations in clean genetic backgrounds

  • Pre-emptive inhibitor design:

    • Multi-target inhibitors to raise resistance barrier

    • Identification of resistance hotspots through computational prediction

    • Development of inhibitors targeting conserved catalytic residues

This research direction is particularly relevant given the emergence of Acinetobacter pittii as a significant hospital-acquired pathogen that has been identified as responsible for outbreaks in different regions worldwide . Development of new antimicrobial strategies is crucial for addressing the increasing prevalence of antibiotic-resistant Acinetobacter species.

What emerging technologies could enhance the study of recombinant Acinetobacter pheT in the next decade?

Several emerging technologies are poised to transform recombinant Acinetobacter pheT research:

• Advanced structural biology techniques:

  • Cryo-electron tomography:

    • Visualization of pheT in native cellular context

    • Spatial organization within the translation machinery

    • Resolution: 10-30 Å for cellular tomograms

  • Time-resolved crystallography:

    • X-ray free electron laser (XFEL) studies

    • Visualization of catalytic intermediates

    • Temporal resolution: femtosecond to millisecond

  • Integrative structural modeling:

    • Combination of cryo-EM, crosslinking-MS, and SAXS data

    • Enhanced modeling of flexible regions

    • Improved computational methods for model building

• Next-generation protein engineering approaches:

  • Deep mutational scanning:

    • Comprehensive mutational landscape analysis

    • High-throughput functional characterization

    • Data analysis using machine learning algorithms

  • Cell-free protein synthesis:

    • Rapid production of variant libraries

    • Direct activity screening without purification

    • Incorporation of non-canonical amino acids

  • De novo protein design:

    • AI-driven design of pheT inhibitors

    • Development of synthetic binding proteins targeting pheT

    • Computational methods: AlphaFold-based design

• Advanced genomic and transcriptomic technologies:

  • Long-read sequencing:

    • Complete genome assembly of Acinetobacter clinical isolates

    • Structural variant detection

    • Platforms: PacBio HiFi, Nanopore

  • Spatial transcriptomics:

    • Localization of pheT expression within bacterial communities

    • Infection model transcriptional landscape

    • Resolution: Single-cell level

  • CRISPR-based technologies:

    • Base editing for precise mutagenesis

    • CRISPRi for tunable gene repression

    • Perturb-seq for high-throughput functional genomics

• Computational advances:

  • AI-driven drug discovery:

    • Generative models for novel inhibitor design

    • Binding affinity prediction

    • Multi-parameter optimization

  • Enhanced molecular dynamics:

    • GPU-accelerated simulations

    • Millisecond-scale simulations

    • Markov state modeling for rare events

These emerging technologies will significantly enhance our ability to study pheT structure, function, and inhibition, potentially leading to novel antimicrobial strategies against Acinetobacter species, which have been identified as significant emerging pathogens .