SAR1785 is a UPF0173 family metal-dependent hydrolase from Staphylococcus aureus strain MRSA252, a clinically important methicillin-resistant strain. The protein has 229 amino acids and belongs to a class of enzymes that catalyze hydrolysis reactions using metal cofactors . While the specific physiological function of SAR1785 remains under investigation, it is part of S. aureus's extensive repertoire of hydrolytic enzymes that may contribute to nutrient acquisition, bacterial survival, and host immune evasion .
Metal-dependent hydrolases in pathogenic bacteria often play crucial roles in virulence by degrading host molecules or modifying bacterial surface components. As S. aureus produces numerous secreted enzymes that function in immune evasion and tissue degradation, SAR1785 might participate in these processes, although its specific substrates and exact mechanistic role require further characterization .
According to product specifications, the shelf life of recombinant SAR1785 depends on several factors including storage state, buffer composition, temperature, and the inherent stability of the protein itself. Generally:
Liquid form: 6 months stability at -20°C/-80°C
Lyophilized form: 12 months stability at -20°C/-80°C
Working aliquots can be stored at 4°C for up to one week
Repeated freezing and thawing is not recommended as it can compromise protein integrity. For optimal results, the protein should be reconstituted in deionized sterile water to a concentration of 0.1-1.0 mg/mL with 5-50% glycerol (final concentration) added before aliquoting for long-term storage .
| Form | Storage Temperature | Shelf Life |
|---|---|---|
| Liquid | -20°C/-80°C | 6 months |
| Lyophilized | -20°C/-80°C | 12 months |
| Working aliquots | 4°C | Up to 1 week |
The commercially available recombinant SAR1785 is produced in yeast expression systems , which suggests this host is suitable for obtaining properly folded, active protein. For researchers planning to express this protein, several considerations should be addressed:
Expression host selection:
Yeast systems (Pichia pastoris or Saccharomyces cerevisiae) provide eukaryotic post-translational processing capability
E. coli systems offer high yield but may require optimization for proper folding
Insect cell systems may be considered for complex proteins requiring specific folding conditions
Codon optimization:
Research indicates that accessibility of translation initiation sites significantly impacts expression success. For recombinant protein production, modifying the first nine codons of mRNAs with synonymous substitutions can significantly improve expression levels. Tools like TIsigner can be employed to optimize codon usage for the target expression system .
Vector design:
Include an appropriate affinity tag (His-tag, GST, etc.) for purification
Consider inducible promoters for controlled expression
Include appropriate signal sequences if secretion is desired
Growth conditions:
Optimized growth temperature, induction timing, and media composition should be empirically determined, as stochastic simulation models show that higher translation initiation site accessibility leads to higher protein production but potentially slower cell growth .
To characterize the metal dependency of SAR1785, a systematic approach combining multiple techniques is recommended:
Metal chelation studies:
Treat the purified enzyme with chelating agents (EDTA, EGTA) and measure residual activity
Perform rescue experiments by adding back individual metal ions (Zn²⁺, Mn²⁺, Fe²⁺, Cu²⁺, etc.) to identify which restore activity
Atomic absorption spectroscopy or ICP-MS:
Quantitatively determine the metal content of the purified enzyme
Compare metal content in active versus inactive preparations
Site-directed mutagenesis:
Identify putative metal-binding residues based on sequence alignment with related hydrolases
Mutate these residues and assess the impact on metal binding and catalytic activity
Structural studies:
Enzymatic assays with different buffers:
Test activity in buffers containing different metal ions
Monitor enzyme kinetics as a function of metal ion concentration
A comparison table documenting enzyme activity with different metals can provide clear evidence of preferential metal cofactor requirements:
| Metal Ion | Relative Activity (%) | Km (μM) | kcat (s⁻¹) | kcat/Km (M⁻¹s⁻¹) |
|---|---|---|---|---|
| None (EDTA) | 0 | - | - | - |
| Zn²⁺ | ? | ? | ? | ? |
| Mn²⁺ | ? | ? | ? | ? |
| Fe²⁺ | ? | ? | ? | ? |
| Cu²⁺ | ? | ? | ? | ? |
| Co²⁺ | ? | ? | ? | ? |
| Ni²⁺ | ? | ? | ? | ? |
Without definitively knowing the natural substrate of SAR1785, researchers should employ a panel of assays to characterize its hydrolytic activity:
Generic hydrolase screening:
p-Nitrophenyl ester hydrolysis assays with various chain lengths
Fluorogenic substrate libraries to identify preferred substrates
Coupled enzyme assays that detect released products
Specific candidate substrate testing:
Based on knowledge of related hydrolases and S. aureus biology, test activity against:
Activity-based protein profiling:
Use chemical probes that react specifically with active hydrolases
Can identify active site residues and confirm hydrolase classification
In silico substrate prediction:
Structure-based modeling to predict substrate binding and catalysis
Molecular docking of candidate substrates
Differential scanning fluorimetry:
Measure thermal stability shifts upon binding of potential substrates or inhibitors
Can provide indirect evidence of substrate specificity
While the specific mechanism of SAR1785 has not been fully elucidated, insights can be drawn from related metal-dependent hydrolases:
Catalytic site architecture:
Metal-dependent hydrolases typically feature a metal ion coordinated by conserved residues (often histidine, aspartate, glutamate) that positions and activates a water molecule for nucleophilic attack on the substrate .
Proposed catalytic steps:
The general mechanism likely involves:
Substrate binding in proximity to the metal center
Polarization of a water molecule by the metal ion
Nucleophilic attack by the activated water on the substrate
Formation of a tetrahedral intermediate
Collapse of the intermediate and product release
Structural dynamics:
Similar to S-adenosylhomocysteine hydrolase, SAR1785 may undergo conformational changes during catalysis. For example, a comparison of human S-adenosylhomocysteine hydrolase with and without inhibitor suggested a 17-degree rigid body movement of the catalytic domain upon substrate binding .
Proton transfer pathways:
The mechanism likely involves proton transfer networks, with conserved residues acting as general bases or acids. For example, in the catalytic mechanism of tyrosine phenol-lyase, specific residues like Lys-257 act as the base abstracting protons, while others stabilize reaction intermediates .
A systematic mutagenesis approach can reveal critical residues involved in catalysis:
Selection of target residues:
Conserved residues identified through sequence alignment with related hydrolases
Predicted metal-binding residues (His, Asp, Glu)
Residues likely involved in substrate binding or catalysis
Residues forming the hydrophobic pocket if present
Types of mutations to consider:
Conservative mutations (e.g., Asp to Glu) to test spatial requirements
Non-conservative mutations (e.g., His to Ala) to remove functional groups
Introduction of bulky side chains to probe steric constraints
Charge reversal to test electrostatic contributions
Analysis of mutant proteins:
Determine expression levels and solubility
Assess structural integrity through circular dichroism or thermal shift assays
Measure metal binding capacity
Determine kinetic parameters (kcat, Km) for comparison with wild-type
Integration with structural data:
Map mutations onto structural models
Correlate functional effects with structural features
Example mutagenesis plan:
| Residue | Predicted Role | Mutations | Expected Outcome |
|---|---|---|---|
| His-X | Metal binding | H→A, H→N | Loss of metal binding, inactive enzyme |
| Asp-Y | Metal binding | D→A, D→N | Reduced metal affinity |
| Glu-Z | General base | E→A, E→Q | Severely reduced catalytic rate |
| Ser-W | Substrate binding | S→A | Increased Km, minimal effect on kcat |
| Arg-V | Substrate binding | R→A, R→K | Altered substrate specificity |
S. aureus employs numerous virulence factors including toxins, enzymes, and immune evasion proteins to establish infection and counter host defenses . While the specific role of SAR1785 in pathogenicity is not explicitly documented in the search results, several hypotheses can be formulated based on knowledge of hydrolases in bacterial pathogenesis:
Potential functions in pathogenesis:
Degradation of host antimicrobial peptides
Modification of bacterial cell surface to evade immune recognition
Processing of bacterial virulence factors
Nutrient acquisition during infection
Contribution to biofilm formation or regulation
Comparative analysis with known virulence factors:
S. aureus produces numerous exoenzymes that contribute to virulence, including proteases (aureolysin, V8 protease, staphopains), nucleases, lipases, and hyaluronidase . SAR1785 may have complementary or redundant functions with these enzymes.
Immune evasion mechanisms:
S. aureus has evolved sophisticated mechanisms to evade the host immune response, including inhibition of neutrophil chemotaxis, resistance to antimicrobial peptides, and complement evasion . Metal-dependent hydrolases could potentially contribute to these processes through enzymatic modification of host defense molecules.
Integration with regulatory networks:
Virulence factor expression in S. aureus is controlled by complex regulatory networks including the accessory gene regulator (agr) system and staphylococcal accessory regulator (sarA) . Understanding how SAR1785 expression is regulated within these networks could provide insights into its role during infection.
To investigate the contribution of SAR1785 to S. aureus pathogenesis, several animal model approaches can be employed:
Gene knockout studies:
Generate SAR1785 deletion mutants in relevant S. aureus strains
Compare virulence of wild-type and mutant strains in infection models
Perform complementation studies to confirm phenotypes are due to SAR1785 deletion
Infection models:
Host response analysis:
Assess inflammatory markers and cytokine profiles
Evaluate neutrophil recruitment and function
Measure bacterial burden in tissues
Histopathological examination of infected tissues
In vivo expression studies:
Use reporter constructs to monitor SAR1785 expression during infection
Identify conditions that induce or repress expression
Combination with other virulence factor mutations:
Create double or triple mutants to assess functional redundancy
Evaluate cumulative effects on virulence
A comprehensive comparative analysis would include:
Sequence alignment and phylogenetic analysis:
Identify conserved domains and catalytic residues
Determine evolutionary relationships within the UPF0173 family
Compare SAR1785 from MRSA252 with orthologs from other S. aureus strains and related species
Structural comparison:
If crystal structures are available, compare folding patterns, active site architecture, and substrate binding pockets
In the absence of SAR1785 crystal structure, homology modeling based on related structures can provide insights
Analysis of potential conformational changes during catalysis similar to those observed in S-adenosylhomocysteine hydrolase
Functional comparison:
Compare substrate specificity profiles
Analyze metal preferences and catalytic parameters
Evaluate expression patterns and regulation
Evolutionary considerations:
Assess conservation across bacterial species
Identify potential horizontal gene transfer events
Evaluate selective pressures on different domains
Protein crystallization for structural determination presents several challenges:
Common challenges and solutions:
| Challenge | Strategy |
|---|---|
| Protein heterogeneity | Optimize purification to ensure homogeneity; consider removal of flexible regions |
| Limited solubility | Screen buffer conditions; consider fusion tags to enhance solubility |
| Conformational flexibility | Use ligands or inhibitors to stabilize specific conformations |
| Post-translational modifications | Express in systems that provide consistent modifications or use enzymatic treatment |
| Crystal packing issues | Engineer surface residues to promote crystal contacts |
Specific considerations for metal-dependent hydrolases:
Metal binding can induce conformational changes, affecting crystallization
Try crystallization with and without bound metals
Consider co-crystallization with substrate analogs or inhibitors to capture mechanistically relevant states
Alternative approaches:
Cryo-electron microscopy (cryo-EM) for structure determination without crystals
NMR spectroscopy for solution structure and dynamics studies
Small-angle X-ray scattering (SAXS) for low-resolution envelope determination
Case study from related proteins:
The structure of S-adenosylhomocysteine hydrolase was solved using a combination of crystallographic direct methods and multiwavelength anomalous diffraction data . Similar approaches could be applicable to SAR1785.
Identifying the natural substrates of SAR1785 requires integrative approaches:
Comparative genomics:
Analyze gene neighborhood and operon structure
Identify co-evolving genes that may encode substrates or pathway components
Compare presence/absence patterns across bacterial strains with different phenotypes
Transcriptomic correlation:
Identify genes co-expressed with SAR1785 under various conditions
Analyze expression patterns during infection or stress conditions
Perform RNA-seq on wild-type versus SAR1785 knockout strains
Metabolomics approaches:
Compare metabolite profiles between wild-type and SAR1785 mutant strains
Look for accumulated precursors or depleted products
Use labeled substrates to trace metabolic pathways
Proteomic strategies:
Activity-based protein profiling with hydrolase-specific probes
Affinity purification using catalytically inactive SAR1785 to capture substrates
Differential proteomics comparing wild-type and mutant strains
Structural prediction and docking:
In silico screening of potential substrates based on binding pocket analysis
Molecular docking simulations to predict binding affinities
Virtual screening of metabolite libraries
Evaluating SAR1785 as a therapeutic target requires consideration of several factors:
Target validation criteria:
Essentiality: Determine if SAR1785 is essential for S. aureus growth or virulence
Conservation: Assess conservation across clinical isolates to ensure broad-spectrum activity
Uniqueness: Evaluate structural or functional differences from human homologs to minimize off-target effects
Accessibility: Consider cellular location and accessibility to inhibitors
Inhibitor development approaches:
Structure-based design if crystal structure is available
High-throughput screening of compound libraries
Fragment-based drug discovery
Natural product screening
Potential advantages as a target:
If metal-dependent, metal chelation could be exploited for inhibition
Enzyme active sites often provide well-defined binding pockets for inhibitors
If involved in virulence rather than growth, inhibitors might reduce selective pressure for resistance
Challenges and considerations:
Designing an effective high-throughput screening campaign requires careful consideration of assay design and compound selection:
Assay development:
Primary assay: Develop a robust enzymatic assay with appropriate signal-to-noise ratio
Counter-screen: Include assays to identify false positives (e.g., compounds that interfere with detection method)
Secondary assays: Confirm hits with orthogonal assay formats
Tertiary assays: Test activity in cellular contexts (bacterial growth, infection models)
Compound library selection:
Diversity-oriented libraries for broad chemical space exploration
Focused libraries targeting metal-dependent hydrolases
Natural product libraries that may include evolved inhibitors
Fragment libraries for identifying starting points for optimization
Screening strategy:
Consider quantitative high-throughput screening (qHTS) with dose-response curves
Implement automated liquid handling and data analysis
Include appropriate controls for assay quality assessment (Z' factor)
Use clustering and machine learning for hit prioritization
Hit validation and optimization pipeline:
Confirm structure and purity of hits
Determine mechanism of inhibition
Assess specificity against related enzymes
Evaluate physicochemical properties and optimize for drug-like characteristics
Test activity against multiple S. aureus strains