Recombinant Mesoplasma florum Glucose-6-phosphate isomerase (pgi)

What is the genomic context of glucose-6-phosphate isomerase in Mesoplasma florum?

Glucose-6-phosphate isomerase (pgi) in Mesoplasma florum is encoded by the gene Mfl254 within the bacterial genome of approximately 800 kb . The gene is part of the central carbon metabolism pathway, specifically involved in glycolysis and gluconeogenesis. Based on transcriptome and proteome analyses of M. florum, the pgi gene shows significant expression during exponential growth phase . The genomic organization reveals that pgi is expressed as part of the central metabolic gene cluster, which is consistent with its essential role in sugar metabolism. Comparative genomics studies across 13 M. florum strains have identified pgi as part of the core genome (546 homologous gene cluster families observed in all compared genomes), showing its evolutionary conservation and functional importance .

What is the basic biochemical function of M. florum glucose-6-phosphate isomerase?

Glucose-6-phosphate isomerase (GPI/PGI) in M. florum catalyzes the reversible isomerization between glucose-6-phosphate and fructose-6-phosphate, serving as a critical junction enzyme between glycolysis and gluconeogenesis pathways . This enzyme functions as part of the central carbon metabolism in M. florum, allowing the organism to process various carbon sources. Structural analysis has demonstrated that M. florum GPI shows high structural similarity to other bacterial phosphoglucose isomerases . Interestingly, the enzyme also exhibits promiscuity by catalyzing the conversion of mannose-6-phosphate to fructose-6-phosphate, thereby enabling the organism to metabolize mannose as an alternative carbon source . This dual functionality highlights the metabolic versatility of M. florum despite its minimalist genome.

How does M. florum pgi expression compare to other metabolic enzymes in the organism?

Proteomic analysis indicates that central carbon metabolism proteins account for approximately 7.5% of the total M. florum protein diversity . Given that pgi (Mfl254) is a key enzyme in central carbon metabolism, it is likely expressed at moderate to high levels relative to the proteome as a whole, although not among the most abundantly expressed proteins like the HPr PTS phosphocarrier protein .

What are the optimal conditions for heterologous expression of M. florum pgi in E. coli?

For optimal heterologous expression of M. florum pgi in E. coli, researchers should consider the following methodological approach:

• Expression vector selection: Use broad-host-range vectors like pBBR1-MCS5 which has been successfully used for M. florum genes . Alternatively, standard T7-based expression vectors (pET series) can be employed with appropriate codon optimization.

• Codon optimization: M. florum has a low GC content genome, so codon optimization for E. coli expression is recommended to enhance protein yield.

• Expression conditions: Based on protocols for other recombinant GPI enzymes:

  • Host strain: BL21(DE3) or its derivatives

  • Induction: 0.1-0.5 mM IPTG at OD₆₀₀ of 0.6-0.8

  • Temperature: Lower post-induction temperature (16-25°C) often improves solubility

  • Duration: 16-18 hours for maximum yield

• Fusion tags: A His-tag fusion similar to that described for human GPI (containing 578 amino acids with a 20 amino acid His-Tag at N-terminus) has been shown to facilitate purification without affecting enzyme activity.

• Growth medium: Rich media like 2YT or TB rather than LB is recommended for higher biomass and protein yield.

The stability and activity of the recombinant protein can be enhanced by including 10% glycerol, 1mM DTT, and 20mM Tris-HCl buffer (pH 8.0) in the purification and storage buffers, as demonstrated for other GPI enzymes .

What purification strategy yields the highest purity and activity for recombinant M. florum pgi?

A multi-step purification strategy for recombinant M. florum pgi should include:

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

• Initial capture: Immobilized metal affinity chromatography (IMAC) using Ni-NTA or similar matrix for His-tagged protein.

• Intermediate purification: Ion exchange chromatography (preferably Q Sepharose) to separate based on charge differences.

• Polishing step: Size exclusion chromatography to remove aggregates and ensure homogeneity.

• Quality control: SDS-PAGE analysis should confirm >95% purity as shown for other recombinant GPI preparations .

For optimal enzyme activity, the final buffer composition should include:

• 20 mM Tris-HCl (pH 8.0)

• 10% glycerol for stability

• 1 mM DTT to maintain reduced cysteines

• Optional: adding a carrier protein (0.1% HSA or BSA) for long-term storage

Long-term storage recommendations include aliquoting and freezing at -20°C to avoid multiple freeze-thaw cycles which can reduce enzyme activity .

What methods can be used to verify the enzymatic activity of purified M. florum pgi?

Several complementary methods can be used to verify the enzymatic activity of purified M. florum pgi:

• Spectrophotometric coupled assay: This is the standard method for measuring GPI activity.

  • Forward reaction (G6P → F6P): Couple with phosphofructokinase and aldolase, followed by triose phosphate isomerase and glycerol-3-phosphate dehydrogenase, measuring NADH oxidation at 340 nm.

  • Reverse reaction (F6P → G6P): Couple with glucose-6-phosphate dehydrogenase, measuring NADPH formation at 340 nm.

• Direct product measurement by HPLC or enzymatic assays: Quantify substrate consumption and product formation directly.

• Isothermal titration calorimetry (ITC): Provides thermodynamic parameters of substrate binding.

• Controls for activity validation:

  • Heat-inactivated enzyme (negative control)

  • Commercial GPI from other sources (positive control)

  • Testing activity with/without potential inhibitors like 6-phosphogluconate and erythrose-4-phosphate, which are known inhibitors of GPI enzymes

• Kinetic parameter determination: Determine the K_m and V_max values for both forward and reverse reactions. For reference, other bacterial GPI enzymes typically show K_m values of 0.04-1.0 mM for fructose-6-phosphate and glucose-6-phosphate respectively .

How does the structure of M. florum pgi compare to other bacterial phosphoglucose isomerases?

M. florum glucose-6-phosphate isomerase (Mfl254) shows interesting structural features compared to other bacterial PGIs:

This structural flexibility may be a key adaptation that allows M. florum to maintain metabolic versatility despite its reduced genome size.

What is the substrate specificity profile of M. florum pgi and how does it differ from other bacterial enzymes?

M. florum glucose-6-phosphate isomerase exhibits an interesting substrate specificity profile:

• Primary substrates: Like most PGIs, it catalyzes the reversible isomerization between glucose-6-phosphate and fructose-6-phosphate .

• Secondary substrate - mannose metabolism: A distinctive feature of M. florum pgi (Mfl254) is its ability to convert mannose-6-phosphate to fructose-6-phosphate, as supported by structural comparison with enzymes having this capability . This allows M. florum to utilize mannose as a carbon source despite its minimal genome.

• Metabolic context: The ability to process mannose is significant as mannose has been detected in the M. florum polysaccharide layer , suggesting a potential role in cell surface structure formation.

• Functional complementation: Unlike some bacteria where pgi knockout completely prevents growth on certain sugars, M. florum likely uses alternative pathways. For instance, the Entner-Doudoroff pathway has been reported as an alternative glucose catabolism pathway in gram-negative bacteria .

• Comparison with other bacterial PGIs: While typical bacterial PGIs like those from E. coli primarily function in glycolysis/gluconeogenesis, M. florum pgi appears to have evolved a broader substrate range, possibly as an adaptation to its minimal genome. This presents an interesting case of functional optimization in a genome-reduced organism.

For researchers studying substrate specificity, enzyme kinetic analysis should be conducted with various substrates (glucose-6-phosphate, fructose-6-phosphate, mannose-6-phosphate) to determine the comparative efficiency (k_cat/K_m) for each substrate.

How can researchers study the role of M. florum pgi in cellular metabolism using genetic approaches?

Researchers can employ several genetic approaches to study the role of M. florum pgi in cellular metabolism:

These approaches can be combined with modern synthetic biology tools being developed for M. florum to enable precise genetic manipulation and study of metabolic pathways.

How can M. florum pgi be engineered for enhanced catalytic properties?

Engineering M. florum pgi for enhanced catalytic properties requires a rational design approach based on structural and functional knowledge:

• Site-directed mutagenesis targets:

  • Active site residues: Modify to alter substrate specificity or reaction rate

  • Subunit interface: Enhance dimer stability for improved thermostability

  • Surface residues: Introduce charged residues to increase solubility

• Directed evolution strategies:

  • Error-prone PCR to generate random mutations

  • DNA shuffling with homologous PGIs from thermophilic organisms

  • Selection systems based on growth complementation in pgi-deficient strains

• Computational design approaches:

  • Molecular dynamics simulations to identify flexible regions that could be stabilized

  • In silico docking studies to predict mutations that enhance substrate binding

  • Sequence entropy analysis to identify conservation patterns across homologs

• Domain swapping: Create chimeric enzymes by swapping domains between M. florum pgi and other PGIs with desirable properties (e.g., thermostability from hyperthermophilic archaeal PGIs that show activity up to 95°C ).

• Performance metrics to evaluate improvements:

  • Catalytic efficiency (k_cat/K_m)

  • Thermostability (T₅₀ - temperature at which 50% activity remains after incubation)

  • pH stability range

  • Resistance to inhibitors

  • Substrate range expansion

These engineering approaches could yield variants of M. florum pgi with enhanced properties for biotechnological applications or provide insights into structure-function relationships of this enzyme.

What role does M. florum pgi play in the organism's adaptation to different carbon sources?

M. florum pgi plays a central role in the organism's adaptation to different carbon sources, despite the bacterium's minimal genome:

Understanding this metabolic adaptability is particularly interesting in near-minimal genomes like M. florum, as it reveals core metabolic functions that have been maintained during genome reduction.

How can M. florum pgi be used in metabolic engineering of minimal cells for synthetic biology applications?

M. florum pgi offers several advantages for metabolic engineering in minimal cell synthetic biology applications:

• Metabolic flux control point: As a key enzyme at the junction of glycolysis, gluconeogenesis, and the pentose phosphate pathway, modulating pgi activity can redirect carbon flux toward desired products. This is particularly valuable in minimal cell platforms with simplified metabolic networks.

• Chassis optimization: In near-minimal cellular chassis development, optimizing pgi expression levels can enhance growth rates and carbon source utilization. M. florum has a doubling time of ~32 minutes , making it attractive for synthetic biology applications requiring rapid growth.

• Orthogonal metabolic pathways: Introducing modified versions of M. florum pgi with altered substrate specificity could enable novel carbon utilization pathways in synthetic minimal cells.

• Integration with existing synthetic biology tools: The developed genetic tools for M. florum include:

  • Replicable plasmids based on oriC regions with transformation frequencies of ~4.1 × 10⁻⁶ transformants per viable cell

  • Selection markers (tetracycline, puromycin, and spectinomycin/streptomycin resistance)

  • Multiple transformation methods (PEG-mediated, electroporation, conjugation)

• Genome-scale metabolic model integration: Engineering pgi must consider its broader metabolic context. M. florum has a genome-scale metabolic model (iJL208) containing 208 protein-coding genes, which can predict the impact of pgi modifications on cellular metabolism .

• Minimal genome design: Understanding the role of pgi in near-minimal genomes informs rational design of synthetic minimal genomes. The essential gene set in M. florum L1 is estimated at 290-332 genes , and metabolic enzymes like pgi are critical components to retain or optimize in minimal genome designs.

These approaches enable researchers to utilize M. florum pgi as both a metabolic engineering target and a component for building synthetic minimal cells with customized metabolic capabilities.

How does M. florum pgi differ from pgi enzymes in other Mollicutes species?

Comparative analysis of M. florum pgi with those from other Mollicutes reveals several interesting distinctions:

• Evolutionary context: M. florum belongs to a group of Mollicutes with near-minimal genomes (~800 kb). Comparative genomics across 13 M. florum strains identified a core set of 546 homologous gene cluster families, likely including pgi as part of the essential metabolic machinery .

• Mycoplasma comparison: While M. florum pgi (Mfl254) functions in both glycolysis and gluconeogenesis, some Mycoplasma species have lost the ability to utilize certain carbon sources due to reductive genome evolution. For example, Mycoplasma mycoides and Mycoplasma capricolum, which are phylogenetically related to M. florum, have different metabolic capabilities despite similar genome sizes.

• Functional optimization: M. florum has retained the ability to metabolize multiple carbon sources including glucose and mannose , suggesting that its pgi enzyme has maintained or evolved broader substrate specificity compared to more specialized Mollicutes.

• Genomic context: In M. florum, pgi is part of a metabolic gene cluster arrangement that may differ from other Mollicutes. The organization of transcription units and promoter structures identified through transcriptome analysis provides insights into how pgi expression is regulated within the simplified genetic context of Mollicutes.

• Horizontal gene transfer: Some Mollicutes may have acquired their pgi genes through horizontal gene transfer, similar to what has been observed for Methanococcus jannaschii, whose PGI was likely obtained from bacteria, possibly from the hyperthermophile Thermotoga maritima .

This comparative perspective helps understand how metabolic enzymes like pgi have evolved within the context of genome minimization and specialization across the Mollicutes class.

What insights does M. florum pgi provide about enzyme evolution in minimal genomes?

M. florum pgi offers valuable insights into enzyme evolution within minimal genomes:

• Functional retention during genome reduction: Despite having a highly reduced genome (~800 kb), M. florum has retained pgi as part of its core metabolic machinery, suggesting that the isomerization of glucose-6-phosphate to fructose-6-phosphate represents an irreplaceable metabolic function.

• Moonlighting functions: While canonical glucose-6-phosphate isomerases can exhibit moonlighting functions (neurotrophic factor, autocrine motility factor, etc.) in complex organisms , M. florum pgi may represent a more specialized version focused on primary metabolic functions, exemplifying evolutionary streamlining in minimal genomes.

• Substrate promiscuity: The ability of M. florum pgi (Mfl254) to process mannose-6-phosphate in addition to glucose-6-phosphate suggests that substrate promiscuity might be a beneficial feature in minimal genomes, allowing one enzyme to perform multiple functions.

• Structural minimalism: Computational structure prediction has shown that M. florum pgi maintains structural similarity with other bacterial phosphoglucose isomerases despite potential sequence divergence, indicating that structural conservation is prioritized in minimal genomes.

• Metabolic context optimization: In M. florum, central carbon metabolism represents a significant portion of the proteome (7.5%) , suggesting that enzymes like pgi have been selectively maintained and potentially optimized during genome reduction to ensure efficient energy production.

These insights contribute to our understanding of how essential enzymes evolve and adapt in organisms undergoing genome minimization, providing valuable knowledge for synthetic biology efforts aimed at creating minimal cells.

How can comparative studies of M. florum pgi contribute to our understanding of minimal metabolic networks?

Comparative studies of M. florum pgi can significantly advance our understanding of minimal metabolic networks in several ways:

• Essential metabolic functions: By examining the role of pgi in M. florum compared to other minimal organisms, researchers can identify truly indispensable metabolic functions that must be retained even in highly reduced genomes. Transposon mutagenesis studies in M. florum have identified 290-332 putatively essential genes , providing context for understanding pgi's importance.

• Metabolic network topology: Understanding how pgi connects different metabolic pathways in M. florum reveals principles of minimal network design. The enzyme creates a critical junction between glycolysis, gluconeogenesis, and other pathways like pentose phosphate metabolism.

• Alternative pathway compensation: Studies of carbon metabolism in M. florum suggest that while the glucose-6-phosphate isomerase reaction can be bypassed using alternative pathways like the Entner-Doudoroff pathway for glucose metabolism, the reverse reaction in fructose utilization appears more essential . This differential essentiality provides insights into minimal pathway requirements.

• Substrate promiscuity as an adaptation: The ability of M. florum pgi to process mannose-6-phosphate suggests that enzyme promiscuity may be a common adaptation in minimal metabolic networks, allowing fewer enzymes to process more substrates.

• Metabolic model validation: Comparing experimental data on pgi function with predictions from genome-scale metabolic models like iJL208 for M. florum can improve our understanding of minimal metabolism modeling:

These comparative studies contribute to both fundamental understanding of minimal metabolic networks and applied knowledge for designing synthetic minimal cells with customized metabolic capabilities.

What are common challenges in expressing and purifying active recombinant M. florum pgi?

Researchers working with recombinant M. florum pgi may encounter several challenges during expression and purification:

• Codon usage bias: M. florum has a low GC content (~27%) compared to common expression hosts like E. coli (~50%). This discrepancy can lead to:

  • Translational pausing

  • Premature termination

  • Low yield of full-length protein
    Solution: Codon optimization for the expression host or use of strains enriched in rare tRNAs (like Rosetta or CodonPlus).

• Protein solubility issues: Recombinant proteins often form inclusion bodies in heterologous hosts.
  Solutions:

  • Lower induction temperature (16-20°C)

  • Use solubility-enhancing fusion tags (SUMO, MBP, TrxA)

  • Co-express with chaperones (GroEL/ES, DnaK/J)

  • Optimize induction conditions (lower IPTG concentration, 0.1-0.3 mM)

• Activity loss during purification: Many enzymes lose activity during purification steps.
  Solutions:

  • Include stabilizing agents (10% glycerol, 1mM DTT)

  • Add protease inhibitors during initial lysis

  • Minimize purification steps and processing time

  • Consider protein engineering to enhance stability

• Oligomeric state issues: If M. florum pgi functions as a dimer (like other bacterial GPIs), purification conditions might disrupt oligomerization.
  Solution: Optimize buffer conditions (salt concentration, pH) to maintain native oligomeric state.

• Enzyme assay interference: Common contaminants can interfere with activity assays.
  Solution: Include appropriate controls and consider multiple activity measurement methods.

For long-term storage, adding a carrier protein (0.1% HSA or BSA) and avoiding multiple freeze-thaw cycles is recommended, as demonstrated for other GPI enzymes .

How can researchers address challenges in studying M. florum pgi in vivo?

Researchers studying M. florum pgi function in vivo face several challenges that can be addressed through specialized approaches:

• Genetic manipulation limitations:

  • Challenge: Limited genetic tools for M. florum compared to model organisms.

  • Solutions:

    • Utilize recently developed transformation methods (PEG-mediated, electroporation, or conjugation from E. coli)

    • Apply selection markers validated for M. florum (tetracycline, puromycin, spectinomycin/streptomycin)

    • Use oriC-based plasmids that have shown transformation frequencies of ~4.1 × 10⁻⁶ transformants per viable cell

• Essential gene modification:

  • Challenge: If pgi is essential, knockout studies will not be viable.

  • Solutions:

    • Use conditional expression systems

    • Apply CRISPR interference (CRISPRi) for partial knockdown

    • Implement transposon mutagenesis with deep sequencing to identify viable insertion sites

• Growth and cultivation challenges:

  • Challenge: M. florum has specific growth requirements.

  • Solutions:

    • Optimal growth temperature is 34°C with doubling time of ~32 minutes

    • Use rich media like ATCC 1161 medium for routine cultivation

    • For defined medium studies, M9 supplemented with appropriate carbon sources can be used

• Phenotype analysis:

  • Challenge: Connecting pgi function to observable phenotypes.

  • Solutions:

    • Monitor growth on different carbon sources (glucose vs. fructose)

    • Perform metabolomics analysis to track metabolite changes

    • Use isotope labeling to trace carbon flux through central metabolism

• Physiological context:

  • Challenge: Understanding pgi role within minimal cellular context.

  • Solutions:

    • Integrate findings with genome-scale metabolic models like iJL208

    • Correlate with transcriptome and proteome data

    • Perform comparative studies with pgi mutants in other bacteria

These approaches can help overcome the challenges of studying enzyme function in a near-minimal organism with limited genetic tools.

What are the key considerations for designing experiments to study the role of M. florum pgi in metabolism?

When designing experiments to study the role of M. florum pgi in metabolism, researchers should consider these key factors:

• Experimental design hierarchy:

  • Start with in vitro biochemical characterization

  • Progress to heterologous expression studies

  • Advance to native host genetic manipulation

  • Culminate with systems-level analysis

• Controls and comparative systems:

  • Include wild-type M. florum as positive control

  • Use known pgi mutants from model organisms as reference points

  • Compare with other Mollicutes species to identify unique features

  • Consider heterologous complementation experiments in model organisms

• Metabolic context integration:

  • Design experiments that probe pgi's role at the junction of multiple pathways

  • Consider the impact on both glycolysis and gluconeogenesis

  • Examine connection to pentose phosphate pathway

  • Investigate relationship with mannose metabolism

• Analytical techniques selection:

  • Enzymatic assays for direct activity measurement

  • Metabolomics to profile pathway intermediates

  • ¹³C metabolic flux analysis to quantify carbon flow

  • Transcriptomics and proteomics for regulatory context

• Growth conditions matrix:

  • Test multiple carbon sources (glucose, fructose, mannose)

  • Vary metabolic states (exponential vs. stationary phase)

  • Consider stress conditions to reveal conditional phenotypes

  • Examine growth kinetics parameters:

• Technical considerations:

  • Growth measurement methods (OD, cell counting, viability assays)

  • Genetic manipulation efficiency (transformation frequencies ~4.1 × 10⁻⁶ transformants per viable cell)

  • Medium composition effects on phenotype expression

  • Appropriate statistical analysis for complex datasets

Careful consideration of these factors will ensure robust experimental design to elucidate the role of pgi in M. florum metabolism.

How might M. florum pgi contribute to the development of minimal synthetic cells?

M. florum pgi represents a valuable component for minimal synthetic cell development in several ways:

• Blueprint for essential metabolic functions: As part of the core metabolism in a near-minimal organism, M. florum pgi provides a model for essential carbon processing functions in synthetic minimal cells. Studies suggest that approximately 290-332 genes in M. florum L1 are potentially essential , forming a foundation for minimal genome design.

• Metabolic network optimization: The dual substrate specificity of M. florum pgi (processing both glucose-6-phosphate and mannose-6-phosphate) demonstrates how metabolic enzymes in minimal cells can be optimized for multifunctionality, reducing the total gene count needed.

• Synthetic cell energetics: Since pgi sits at a critical junction in central carbon metabolism, understanding its regulation and kinetics in M. florum informs energy metabolism design in synthetic cells. The enzyme's properties can be leveraged to:

  • Control flux between glycolysis and pentose phosphate pathway

  • Balance energy production and biosynthetic precursor generation

  • Enable utilization of diverse carbon sources

• Chassis compatibility: M. florum has emerged as a promising chassis for synthetic biology due to:

  • Small genome (~800 kb)

  • Fast growth rate (doubling time ~32 min)

  • Non-pathogenic nature

  • Growing toolkit for genetic manipulation

• Integration with existing synthetic biology efforts: M. florum research complements other minimal cell projects like JCVI-syn3.0, providing comparative insights into different minimal metabolic designs. The pgi enzyme represents one of the core metabolic functions that must be maintained even in highly reduced genomes.

• Rational design approach: Understanding the structural and functional properties of M. florum pgi enables rational engineering of this enzyme for novel synthetic cell applications, such as expanding substrate range or optimizing catalytic efficiency under different conditions.

These contributions position M. florum pgi as both a model component and an actual building block for the development of minimal synthetic cells with customized metabolic capabilities.

What emerging technologies could enhance our understanding of M. florum pgi structure and function?

Several emerging technologies promise to advance our understanding of M. florum pgi structure and function:

• Cryo-electron microscopy (Cryo-EM): This technique can reveal the detailed structure of M. florum pgi in near-native conditions without the need for crystallization, enabling visualization of:

  • Oligomeric state dynamics

  • Substrate binding conformational changes

  • Potential interaction with other metabolic enzymes

• Single-molecule enzymology: Advanced fluorescence techniques can track individual enzyme molecules:

  • FRET-based assays to monitor conformational changes during catalysis

  • Single-molecule tracking to observe intracellular localization and dynamics

  • Optical tweezers to measure mechanical properties of enzyme-substrate interactions

• Integrative structural biology approaches:

  • Combining X-ray crystallography, NMR, SAXS, and computational modeling

  • Hydrogen-deuterium exchange mass spectrometry (HDX-MS) to map dynamic regions

  • Cross-linking mass spectrometry to identify interaction interfaces

• Advanced genome editing tools:

  • CRISPR-based precise genome editing adapted for M. florum

  • Base editing technologies for introducing specific mutations

  • Multiplex genome engineering to study pgi in different genetic backgrounds

• Systems biology technologies:

  • Multi-omics integration (transcriptomics, proteomics, metabolomics, fluxomics)

  • Genome-scale metabolic modeling with machine learning enhancement

  • Single-cell omics to understand cell-to-cell variability in pgi expression and function

• Synthetic biology approaches:

  • Cell-free expression systems to rapidly test pgi variants

  • Minimal cell platforms to study pgi function in simplified genetic contexts

  • Biosensors that report on pgi activity or metabolic flux around this enzyme

• Computational advances:

  • Molecular dynamics simulations with enhanced sampling techniques

  • Quantum mechanics/molecular mechanics (QM/MM) to understand reaction mechanisms

  • Deep learning approaches for protein structure prediction and function annotation

These emerging technologies can be synergistically applied to develop a comprehensive understanding of M. florum pgi structure, function, and role in cellular metabolism.

What interdisciplinary approaches could lead to novel applications of M. florum pgi in biotechnology?

Interdisciplinary approaches combining multiple scientific fields could unlock novel applications of M. florum pgi in biotechnology:

• Synthetic biology + enzyme engineering:

  • Design pgi variants with expanded substrate specificity

  • Create metabolic valves based on engineered pgi allosteric regulation

  • Develop orthogonal metabolic modules using evolved pgi variants

• Metabolic engineering + computational biology:

  • Integrate M. florum pgi into designer minimal cells for specialized bioproduction

  • Use genome-scale models to predict optimal pgi expression levels for different biotransformations

  • Design metabolic pathways with optimized flux distribution around pgi

• Structural biology + nanotechnology:

  • Create pgi-based nanoreactors for controlled isomerization reactions

  • Develop immobilized enzyme systems with enhanced stability

  • Design protein scaffolds incorporating pgi for cascade reactions

• Systems biology + materials science:

  • Incorporate pgi into biomaterial production systems

  • Create self-assembling enzyme complexes with controlled spatial organization

  • Develop responsive biomaterials that adapt to metabolic signals

• Evolutionary biology + industrial biotechnology:

  • Apply directed evolution principles to develop pgi variants for industrial applications

  • Understand natural metabolic adaptations to inform biocatalyst design

  • Create robust pgi variants for harsh industrial conditions

• Medical biotechnology + synthetic genomics:

  • Explore the development of minimal cell therapeutics using M. florum chassis with optimized pgi

  • Design diagnostic biosensors based on pgi activity

  • Create cell-free therapeutic production systems incorporating pgi

• Biophysics + computational chemistry:

  • Develop detailed catalytic models to inform rational enzyme design

  • Study energy landscapes of pgi reaction to optimize catalytic efficiency

  • Investigate quantum effects in enzyme catalysis for biomimetic applications