Recombinant UPF0337 protein M6_Spy1715 (M6_Spy1715)

What is UPF0337 protein M6_Spy1715 and what is its biological function in Streptococcus pyogenes?

UPF0337 protein M6_Spy1715 is a small protein found in Streptococcus pyogenes serotype M6 that belongs to the CsbD family of proteins, which are considered general stress-response proteins in bacteria. While the specific function of M6_Spy1715 is still being elucidated, research on homologous UPF0337 proteins in other bacteria provides valuable insights. For instance, UPF0337 protein SACOL1680 in Staphylococcus aureus has been identified as a marker for methicillin resistance .

The UPF0337 family of proteins appears to play important roles in bacterial stress response mechanisms. Studies suggest these proteins may contribute to antibiotic resistance by altering binding affinities to antibiotics and helping bacteria adapt to stressful environments encountered during infection, such as host immune responses and nutrient limitation. In S. pyogenes specifically, M6_Spy1715 likely contributes to this pathogen's ability to survive in hostile environments and may influence virulence through stress adaptation mechanisms.

What expression systems are most effective for producing recombinant UPF0337 protein M6_Spy1715?

Multiple expression systems have been employed for producing recombinant UPF0337 proteins, each with distinct advantages depending on research objectives. The following table summarizes the major expression systems used for UPF-family proteins:

Based on commercial offerings, recombinant UPF0337 protein M6_Spy1715 is available produced in baculovirus expression systems , suggesting this system provides an effective balance between proper protein folding and yield for this particular protein. For structural studies requiring high purity, E. coli systems may be preferable due to higher yields, while functional studies might benefit from insect or mammalian cell expression systems that better preserve native protein structure.

What structural characteristics define the UPF0337 family of proteins and how do they relate to function?

UPF0337 proteins share several key structural characteristics that influence their function in bacterial stress response. Based on studies of homologous proteins like UPF0337 protein SACOL1680 from S. aureus, we can identify the following defining features:

• Small size: UPF0337 proteins typically have a molecular mass around 6.5-6.6 kDa after N-terminal methionine removal, making them relatively small bacterial proteins .

• Distinctive binding pocket: Crystal structure and molecular docking simulations reveal these proteins contain a binding cavity that may interact with antibiotics and other molecules. In S. aureus, specific amino acid variations at positions 36-37 significantly alter this cavity's size and binding properties .

• Amino acid sequence variations: Critical amino acid differences between UPF0337 variants have been identified. For example, UPF0337 protein SA1452 in methicillin-susceptible S. aureus contains 36AT37, while UPF0337 protein SACOL1680 in methicillin-resistant S. aureus contains 36VI37 at the same positions .

• CsbD family characteristics: As members of the CsbD family, UPF0337 proteins likely share structural features common to stress-response proteins, enabling them to function in challenging environmental conditions .

The structure-function relationship is particularly evident in molecular docking simulations showing that after the two-amino-acid mutation from AT to VI at positions 36-37, the binding affinities for methicillin changed significantly (from -5.4 kcal/mol for SA1452 to -4.9 kcal/mol for SACOL1680). When the binding area was limited to the protein cavity, SA1452 maintained binding affinity (-5.3 kcal/mol) while SACOL1680 showed no docking result, potentially explaining its association with methicillin resistance .

What experimental approaches are most effective for studying UPF0337 protein function?

Research on UPF0337 proteins has employed several complementary experimental approaches that can be effectively adapted to study M6_Spy1715:

• Mass spectrometry techniques:

  • MALDI-TOF MS has been successfully used to identify UPF0337 protein variants and correlate their presence with specific bacterial phenotypes .

  • NanoLC-MS/MS analysis provides detailed protein identification and sequence determination after appropriate fractionation and digestion steps .

• Molecular biology methods:

  • Gene knockout studies to examine phenotypic changes in bacterial strains lacking UPF0337 proteins.

  • Site-directed mutagenesis to investigate the effects of specific amino acid substitutions.

• Structural biology approaches:

  • X-ray crystallography or NMR spectroscopy to determine three-dimensional structures.

  • Computational modeling using tools like Phyre2 for homology modeling when experimental structures are unavailable .

• Molecular docking simulations:

  • Programs like PyRX with PyMol visualization have been used to study interactions between UPF0337 proteins and potential binding partners like antibiotics .

• Machine learning methods:

  • LightGBM algorithm combined with SHAP analysis has been used to identify UPF0337 proteins as key biomarkers in large datasets .

  • These approaches can reveal subtle patterns in protein distribution across bacterial populations.

For comprehensive functional characterization of UPF0337 protein M6_Spy1715, a combination of these approaches would be most effective, starting with recombinant protein production, followed by structural determination, and then functional assays under various stress conditions relevant to S. pyogenes pathogenesis.

How does UPF0337 protein M6_Spy1715 compare to similar proteins in other pathogenic bacteria?

UPF0337 proteins are found across multiple bacterial species, with important variations that reflect their specialized functions in different organisms. While specific comparative data for M6_Spy1715 is limited in the literature, meaningful comparisons can be drawn from studies of related UPF0337 proteins:

• Sequence and structural comparisons:

  • UPF0337 protein variants in S. aureus (SACOL1680 and SA1452) show critical amino acid differences at positions 36-37 (VI vs. AT) that correlate with methicillin resistance .

  • Similar sequence variations likely exist between UPF0337 proteins across different bacterial species, reflecting adaptations to specific environmental pressures.

• Mass differences:

  • UPF0337 protein SA1452 in methicillin-susceptible S. aureus has a mass of approximately 6,550.0 Da after N-terminal methionine removal.

  • UPF0337 protein SACOL1680 in methicillin-resistant S. aureus has a mass of approximately 6,593.2 Da after N-terminal methionine removal .

  • These mass differences reflect amino acid sequence variations that influence protein function.

• Functional differences:

  • While all UPF0337 proteins likely function in stress response as members of the CsbD family, their specific roles may vary between species.

  • In S. aureus, UPF0337 protein variants correlate with antibiotic resistance profiles, particularly methicillin resistance .

  • UPF0337 protein M6_Spy1715 in S. pyogenes may have evolved functions specific to this organism's unique ecological niche and pathogenesis mechanisms.

The study of these comparative differences provides valuable insights into how these proteins have evolved specialized functions while maintaining their core role in bacterial stress response. This evolutionary perspective can inform research on targeting these proteins for antimicrobial development or diagnostic applications.

How can machine learning approaches enhance research on UPF0337 protein function?

Machine learning (ML) approaches have revolutionized research on bacterial proteins, including UPF0337 proteins, by enabling rapid analysis of complex datasets and extraction of subtle patterns that might otherwise remain undetected. Several ML applications have proven particularly valuable:

• Biomarker identification:

  • A machine learning model using LightGBM successfully identified UPF0337 protein SACOL1680 (m/z 6,590-6,599) as a key marker for methicillin-resistant S. aureus in a dataset comprising 20,359 clinical isolates, with area under the receiver operating curve values of 0.78 to 0.88 .

  • Similar approaches could identify UPF0337 protein M6_Spy1715 as a marker for specific S. pyogenes phenotypes.

• Feature importance analysis:

  • SHAP (SHapley Additive exPlanations) method effectively interpreted ML results and identified UPF0337 proteins as important molecular features differentiating bacterial strains .

  • This approach can determine which protein characteristics are most critical for predicting bacterial behavior.

• Experimental design optimization:

  • Active Learning approaches like ALPERC can sequentially refine predictive models with minimal experimental data, making research more efficient .

  • Random Forest models combined with active learning have proven effective for materials science applications and could be adapted for protein research .

• Structure-function relationship prediction:

  • ML models can analyze sequence variations across UPF0337 proteins from different species or strains to predict how specific sequence changes might affect protein function.

  • This can guide targeted mutagenesis experiments for functional validation.

When applying ML approaches to UPF0337 protein research, it's important to select appropriate experimental designs. A simulation study comparing 12 experimental designs found that MAXPRO_dis design, based on a space-filling criterion, performed best across multiple test functions and noise settings , suggesting this approach may be optimal for generating training data for ML models of UPF0337 protein function.

What mass spectrometry methods are most effective for characterizing UPF0337 proteins?

Mass spectrometry has been instrumental in characterizing UPF0337 proteins, with several complementary methods providing comprehensive analysis:

• MALDI-TOF MS (Matrix-Assisted Laser Desorption/Ionization-Time of Flight Mass Spectrometry):

  • This method was crucial in identifying UPF0337 protein variants in S. aureus and correlating them with methicillin resistance .

  • It enabled detection of specific mass peaks associated with UPF0337 protein variants: m/z 6,550.0 for SA1452 in methicillin-susceptible S. aureus and m/z 6,593.2 for SACOL1680 in methicillin-resistant S. aureus .

  • When combined with machine learning, MALDI-TOF MS data allowed development of a predictive model for rapid MRSA identification with high accuracy (AUC 0.78-0.88) .

• NanoLC-MS/MS (Nano Liquid Chromatography-Tandem Mass Spectrometry):

  • This method provided detailed protein identification and sequence determination after appropriate sample preparation.

  • It enabled identification of specific amino acid differences between UPF0337 protein variants that influence their function .

• Sample preparation strategies:

  • C4 column fractionation effectively isolated UPF0337 proteins prior to mass spectrometry analysis .

  • Multi-enzyme digestion using both trypsin (cleaves at K and R sites) and Glu-C (cleaves at E sites) provided comprehensive sequence coverage .

The pseudogel visualization method developed to present mass spectrometry data proved particularly effective for visualizing differences between UPF0337 protein variants across multiple samples . This approach could be valuable for comparing UPF0337 protein M6_Spy1715 across different S. pyogenes strains or under various experimental conditions.

For characterizing UPF0337 protein M6_Spy1715 specifically, researchers should consider a multi-stage approach: initial protein detection with MALDI-TOF MS, followed by fractionation, multi-enzyme digestion, and detailed sequence analysis with nanoLC-MS/MS to identify any potential post-translational modifications or strain-specific variations.

How can optimal experimental design improve research on UPF0337 proteins?

Optimal experimental design (OED) approaches can dramatically improve research efficiency and outcomes when studying UPF0337 proteins through systematic planning that maximizes information gain while minimizing experimental effort:

• Model-based optimal experimental design:

  • OED can define the optimal set of experiments that would either identify the best model or improve the identifiability of unknown parameters in protein research .

  • For UPF0337 proteins, this approach can help design experiments that efficiently elucidate structure-function relationships.

• Active Learning approaches:

  • The ALPERC algorithm has demonstrated effectiveness in sequentially refining predictive models with minimal experimental data .

  • This approach works by:
    a) Starting with a small set of experiments
    b) Building an initial model (e.g., using Random Forest)
    c) Using the model to identify the most informative next experiments
    d) Iteratively improving the model with new data

• Design selection for machine learning:

  • A comprehensive simulation study comparing 12 experimental designs found that the MAXPRO_dis design performed best across multiple test functions and noise settings .

  • This table summarizes key findings on design performance:

  Design Type	Performance Ranking	Best For
  MAXPRO_dis (space-filling)	1	Most scenarios, especially with nonlinear models
  I_opt (optimal design)	2	Scenarios with moderate noise
  D_opt (optimal design)	3	Simple model structures
  BBD (Box-Behnken)	Better than CCD	Settings with large noise
  Replicated designs	Only advantageous with large noise	Heteroscedastic noise cases

• Mutual information and submodularity:

  • Advanced OED approaches based on mutual information and submodularity principles can further optimize experimental design for protein research .

By applying these OED approaches, researchers can more efficiently characterize UPF0337 protein M6_Spy1715, potentially accelerating discoveries about its structure, function, and role in S. pyogenes biology while minimizing resource expenditure.

What molecular docking approaches are recommended for studying UPF0337 protein interactions?

Molecular docking has provided valuable insights into UPF0337 protein interactions, particularly regarding antibiotic binding. For studying UPF0337 protein M6_Spy1715 interactions, the following approaches have proven effective:

• Structural modeling prerequisites:

  • Homology modeling using Phyre2 has successfully generated protein structures with specific amino acid variations for UPF0337 proteins .

  • For novel UPF0337 proteins like M6_Spy1715, alternative approaches such as AlphaFold or RosettaFold could provide accurate structural models.

• Docking software selection:

  • PyRX version 0.8 with PyMol for visualization was effectively used for docking simulations of UPF0337 proteins with methicillin .

  • The study successfully quantified binding affinity differences between protein variants:

    • 36AT37-methicillin (MSSA): -5.4 kcal/mol

    • 36VI37-methicillin (MRSA): -4.9 kcal/mol

  • When limiting the binding area to the protein cavity:

    • 36AT37-methicillin (MSSA): -5.3 kcal/mol

    • 36VI37-methicillin (MRSA): No docking result

• Refined binding site analysis:

  • Defining specific binding regions based on structural knowledge improved docking relevance for UPF0337 proteins .

  • This approach revealed that amino acid substitutions in positions 36-37 created functional differences by altering cavity size and binding properties.

• Binding partner selection:

  • For UPF0337 protein M6_Spy1715, potential binding partners to investigate include:

    • Antibiotics commonly used against S. pyogenes

    • Host immune system components

    • Other bacterial proteins involved in stress response pathways

• Validation approaches:

  • Computational predictions should be validated with experimental approaches such as:

    • Site-directed mutagenesis of predicted binding residues

    • Binding assays with purified recombinant proteins

    • Isothermal titration calorimetry to measure binding affinities

The molecular docking approach used for S. aureus UPF0337 proteins, which identified critical structural differences affecting antibiotic binding, provides an excellent model for similar studies with UPF0337 protein M6_Spy1715 from S. pyogenes.

How can recombinant UPF0337 proteins contribute to antimicrobial resistance research?

Recombinant UPF0337 proteins provide powerful tools for antimicrobial resistance research, offering insights into resistance mechanisms and potential therapeutic targets:

• Structure-function studies:

  • Recombinant UPF0337 proteins with specific mutations can be produced to investigate how structural variations affect antibiotic binding.

  • The approach used with S. aureus UPF0337 proteins, where molecular docking simulations showed different binding affinities to methicillin, can be extended using recombinant proteins for experimental validation .

• Biomarker development:

  • UPF0337 protein SACOL1680 served as a biomarker for methicillin-resistant S. aureus, enabling rapid identification through mass spectrometry .

  • Similarly, UPF0337 protein M6_Spy1715 variants could potentially serve as biomarkers for specific resistance patterns in S. pyogenes.

  • The machine learning model developed for MRSA prediction achieved area under the receiver operating curve values of 0.78 to 0.88 across 20,359 clinical isolates , suggesting similar approaches could be effective for other pathogens.

• High-throughput screening platforms:

  • Recombinant UPF0337 proteins can be used in screening assays to identify compounds that bind to or inhibit these proteins.

  • If UPF0337 proteins directly contribute to antibiotic resistance, such compounds could potentially serve as adjuvants to restore antibiotic sensitivity.

• Diagnostic development:

  • The correlation between specific UPF0337 protein variants and antibiotic resistance patterns could be exploited for diagnostic purposes.

  • Recombinant proteins can serve as standards for assay development and validation.

• Comparative resistance studies:

  • By comparing UPF0337 proteins across multiple bacterial species, including S. pyogenes, researchers can identify conserved mechanisms of resistance that might be broadly targeted.

The discovery that UPF0337 protein variants in S. aureus correlate with methicillin resistance, with specific amino acid changes affecting antibiotic binding, provides a valuable model for investigating similar phenomena in other pathogens, potentially contributing to novel strategies for combating antimicrobial resistance.

What are the challenges in determining the three-dimensional structure of UPF0337 proteins?

Determining the three-dimensional structure of UPF0337 proteins presents several challenges that researchers must address through careful experimental design and complementary approaches:

• Small protein size limitations:

  • UPF0337 proteins are relatively small (approximately 6.5-6.6 kDa after N-terminal methionine removal) .

  • Small proteins present challenges for X-ray crystallography due to difficulties in forming stable crystals with sufficient diffraction quality.

  • For NMR spectroscopy, their small size may actually be advantageous, but challenges in producing sufficient quantities of isotopically labeled protein remain.

• Expression and purification optimization:

  • Selecting the optimal expression system is critical. While E. coli systems offer high yield, proper folding may require eukaryotic expression systems like baculovirus .

  • Purification strategies must be carefully optimized to maintain native structure while achieving high purity.

  • The following table summarizes key considerations for different expression systems:

  Expression System	Yield	Folding Quality	Post-translational Modifications	Cost
  E. coli	High	Variable	Limited	Low
  Yeast	Moderate	Good	Moderate	Moderate
  Baculovirus	Moderate	Very good	Good	High
  Mammalian	Low	Excellent	Excellent	Very high

• Limited homology challenges:

  • If UPF0337 proteins have limited homology to proteins with known structures, homology modeling approaches (as used in search result with Phyre2) may have limitations in accuracy.

  • For UPF0337 protein M6_Spy1715, establishing reliable structural models may require experimental validation of computational predictions.

• Potential membrane associations:

  • If UPF0337 proteins associate with membranes or other cellular components, this could complicate their structural determination in isolation.

  • Membrane mimetics or co-crystallization with binding partners may be necessary for capturing physiologically relevant structures.

• Functional state capture:

  • Capturing UPF0337 proteins in their various functional states may require specific conditions or ligands.

  • For instance, the structural differences between SA1452 and SACOL1680 in S. aureus were critical for understanding their differential antibiotic binding properties .

To overcome these challenges, researchers should consider employing multiple complementary structural biology approaches and validating structural models through functional assays.

How can CRISPR-Cas9 technology advance functional studies of UPF0337 proteins?

CRISPR-Cas9 technology offers powerful approaches for investigating UPF0337 protein function through precise genetic manipulation. While not explicitly discussed in the search results for UPF0337 protein M6_Spy1715, this technology can be adapted for functional studies in several ways:

• Gene knockout and phenotypic analysis:

  • CRISPR-Cas9 can create precise knockouts of genes encoding UPF0337 proteins in S. pyogenes and other bacteria.

  • Phenotypic analysis of knockout strains can reveal:

    • Antibiotic susceptibility changes

    • Stress response alterations

    • Virulence modifications

    • Growth characteristics under various conditions

• Site-directed mutagenesis for structure-function studies:

  • Based on findings from S. aureus UPF0337 proteins where amino acid differences at positions 36-37 (AT vs. VI) affected methicillin binding , similar targeted mutations could be introduced in UPF0337 protein M6_Spy1715.

  • This approach can validate computational predictions and establish the functional significance of specific residues.

• Transcriptional regulation studies:

  • CRISPR interference (CRISPRi) systems can repress gene expression without altering the gene sequence.

  • CRISPR activation (CRISPRa) systems can enhance expression levels.

  • These approaches can help investigate the effects of altered UPF0337 protein expression levels on bacterial phenotypes.

• Genome-wide interaction screens:

  • CRISPR-Cas9 genome-wide screens can identify genes that interact genetically with UPF0337 protein genes.

  • This can reveal functional relationships and pathways involving these proteins.

• Tagged protein studies:

  • CRISPR-Cas9 can introduce tags at the endogenous locus of UPF0337 protein genes.

  • This enables studies of protein localization, interactions, and dynamics under native expression conditions.

Implementation challenges include optimizing transformation efficiency in S. pyogenes, ensuring editing specificity, and developing appropriate phenotypic assays. Despite these challenges, CRISPR-Cas9 technology offers unprecedented precision for investigating UPF0337 protein function in its native context.

How can UPF0337 proteins be leveraged for diagnostic applications in clinical microbiology?

UPF0337 proteins show considerable potential as biomarkers for diagnostic applications in clinical microbiology, particularly for rapid identification of antibiotic resistance phenotypes:

• Mass spectrometry-based diagnostics:

  • UPF0337 protein SACOL1680 (m/z 6,590-6,599) was identified as a key biomarker for methicillin-resistant S. aureus .

  • A machine learning model using MALDI-TOF MS data achieved rapid MRSA prediction with area under the receiver operating curve values of 0.78 to 0.88 across 20,359 clinical isolates from five different clinical sites .

  • This approach can be integrated into existing clinical workflows, as most microbiology laboratories already use MALDI-TOF MS for bacterial identification.

• Machine learning integration:

  • LightGBM algorithm effectively discriminated between MRSA and MSSA based on mass spectrometry data .

  • SHAP (SHapley Additive exPlanations) method improved interpretability by ranking the importance of molecular features .

  • Similar approaches could be developed for S. pyogenes, potentially using UPF0337 protein M6_Spy1715 as a biomarker for specific phenotypes.

• Rapid resistance profiling:

  • Traditional antimicrobial susceptibility testing methods require 18-48 hours, while mass spectrometry-based approaches can provide results in minutes after bacterial culture .

  • The identification of UPF0337 protein markers could enable rapid resistance profiling, improving patient care by guiding appropriate antibiotic therapy earlier.

• Multi-species applications:

  • The success with S. aureus suggests similar approaches could be developed for other pathogens, including S. pyogenes.

  • A comprehensive database of UPF0337 protein variants across multiple bacterial species could enable broad-spectrum resistance profiling.

• Point-of-care test development:

  • Knowledge of specific UPF0337 protein biomarkers could inform development of antibody-based or nucleic acid-based point-of-care tests.

  • Such tests could potentially identify resistance patterns directly from clinical samples without requiring bacterial culture.

The successful application of UPF0337 protein SACOL1680 as a biomarker for MRSA demonstrates the potential of this approach, which could be extended to UPF0337 protein M6_Spy1715 and other family members for comprehensive diagnostic applications in clinical microbiology.

What are the future research directions for UPF0337 protein studies?

The study of UPF0337 proteins represents a promising area for future research with several key directions that could significantly advance our understanding of bacterial stress response and antibiotic resistance:

• Comprehensive structural characterization:

  • Determine high-resolution structures of UPF0337 protein M6_Spy1715 and related proteins across multiple bacterial species.

  • Compare structural features to identify conserved elements and species-specific adaptations.

  • Investigate structural changes under different environmental conditions and in the presence of various binding partners.

• Systems biology integration:

  • Map the interaction networks of UPF0337 proteins within bacterial stress response pathways.

  • Apply optimal experimental design approaches like ALPERC to efficiently explore complex parameter spaces.

  • Develop predictive models of how UPF0337 proteins function within broader stress response systems.

• Antimicrobial resistance mechanisms:

  • Extend the findings from S. aureus UPF0337 proteins to investigate similar mechanisms in other pathogens.

  • Develop targeted inhibitors of UPF0337 proteins that could potentially serve as adjuvants to restore antibiotic sensitivity.

  • Investigate potential horizontal gene transfer of UPF0337 protein variants between bacterial species.

• Advanced computational approaches:

  • Apply deep learning methods to predict UPF0337 protein function from sequence data.

  • Use molecular dynamics simulations to investigate protein flexibility and binding dynamics.

  • Develop improved docking approaches for identifying novel binding partners.

• Clinical applications:

  • Validate UPF0337 protein biomarkers across diverse clinical settings and geographic regions.

  • Develop standardized mass spectrometry protocols for UPF0337 protein detection in clinical laboratories.

  • Integrate UPF0337 protein data into clinical decision support systems for antibiotic selection.

• Evolutionary studies:

  • Investigate the evolutionary history of UPF0337 proteins across bacterial species.

  • Identify selection pressures that have shaped UPF0337 protein diversity.

  • Predict future evolutionary trajectories under various antibiotic use scenarios.