Recombinant Vanderwaltozyma polyspora Vacuolar membrane protein Kpol_1003p17 (Kpol_1003p17)

How does the structure of Kpol_1003p17 compare to other vacuolar membrane proteins in yeast species?

Kpol_1003p17 shares structural similarities with other vacuolar membrane proteins in yeast, particularly those involved in membrane fusion events. While not directly homologous, it bears functional resemblance to Prm1, a pheromone-regulated membrane glycoprotein in Saccharomyces cerevisiae. Like Prm1, Kpol_1003p17 contains transmembrane domains and is likely processed through similar cellular pathways. Both proteins contain domains that facilitate membrane association and potential interactions with other membrane components.

When conducting comparative analyses, researchers should employ multiple sequence alignment tools to identify conserved domains and motifs that might indicate shared evolutionary origins or functions. Unlike Prm1, which has been extensively characterized in mating and membrane fusion processes, Kpol_1003p17's specific functional role remains less thoroughly documented, making it an excellent candidate for comparative structural studies .

What are the recommended storage conditions for maintaining Recombinant Kpol_1003p17 protein stability?

For optimal stability of Recombinant Kpol_1003p17 protein, researchers should adhere to the following storage protocol:

• Store the lyophilized powder at -20°C/-80°C upon receipt

• Perform aliquoting immediately after reconstitution to prevent protein degradation from repeated freeze-thaw cycles

• Reconstitute the protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL

• Add glycerol to a final concentration of 5-50% (recommended: 50%) before aliquoting

• For working aliquots that will be used within one week, storage at 4°C is acceptable

• For long-term storage, maintain aliquots at -20°C/-80°C

• Avoid repeated freeze-thaw cycles as this significantly reduces protein activity

Research indicates that improper storage is one of the leading causes of inconsistent experimental results when working with recombinant membrane proteins. Properly stored samples maintain structural integrity and functional activity for 12-18 months .

What purification methods are most effective for isolating Kpol_1003p17 while maintaining its native structure?

For optimal purification of Kpol_1003p17 while preserving its native structure, a multi-step approach is recommended:

• Initial Extraction: Use a gentle detergent-based buffer system (e.g., 1% n-dodecyl β-D-maltoside or 0.5% digitonin) supplemented with protease inhibitors to solubilize the membrane fraction.

• Affinity Chromatography: Leverage the N-terminal His tag for initial purification using Ni-NTA resin. A typical protocol involves:

  • Equilibration: 50 mM Tris-HCl pH 8.0, 300 mM NaCl, 10 mM imidazole

  • Washing: Same buffer with 20-30 mM imidazole

  • Elution: Step gradient of 50-250 mM imidazole

• Size Exclusion Chromatography: Further purify using a Superdex 200 column to separate aggregates and contaminants.

• Validation: Confirm purity using SDS-PAGE (>90% purity is achievable) .

For studies requiring preservation of native structure, supplement all buffers with appropriate lipids (0.01-0.05% cholesterol or ergosterol) and perform purification at 4°C. Monitor protein folding using circular dichroism spectroscopy at key purification stages to ensure structural integrity is maintained.

How can researchers effectively track the intracellular localization of Kpol_1003p17 in yeast cells?

To effectively track the intracellular localization of Kpol_1003p17 in yeast cells, researchers should implement a multi-faceted approach combining genetic tagging and microscopy techniques:

• Fluorescent Protein Tagging:

  • Construct a GFP-Kpol_1003p17 fusion protein, ensuring the tag does not interfere with protein trafficking

  • Express the fusion protein from either the native promoter for physiological expression levels or an inducible promoter (e.g., GAL promoter) for controlled expression studies

• Co-localization Studies:

  • Use established organelle markers such as GFP-FYVE (endosome marker) and Sec7-GFP (trans-Golgi marker)

  • Perform immunofluorescence microscopy with antibodies against organelle-specific proteins

• Time-course Experiments:

  • Induce expression of GFP-Kpol_1003p17 using galactose, then repress with glucose

  • Monitor protein localization at different time points (e.g., 30, 60, 90 minutes) to track trafficking patterns

  • Incorporate vacuole-specific dyes (e.g., FM4-64) to confirm vacuolar localization

• Mutant Analysis:

  • Express GFP-Kpol_1003p17 in trafficking mutants such as vps4 (defective in transport to vacuoles) or end4 (deficient in endocytosis)

  • Observe altered localization patterns to determine transport pathways

Based on studies with similar proteins like Prm1, researchers should anticipate that Kpol_1003p17 may localize to multiple compartments including the ER, endosomes, plasma membrane, and vacuoles, with rapid turnover occurring in the vacuole .

What controls are essential when designing experiments to study the degradation kinetics of Kpol_1003p17?

When designing experiments to study the degradation kinetics of Kpol_1003p17, the following controls are essential to ensure reliable and interpretable results:

• Protein Synthesis Inhibition Controls:

  • Include cycloheximide-treated samples to block new protein synthesis

  • Include controls without cycloheximide to establish baseline degradation rates

  • Monitor total protein levels to ensure equal loading across time points

• Vacuolar Proteolysis Controls:

  • Include pep4 mutant strains (deficient in vacuolar proteolysis) to determine the contribution of vacuolar degradation

  • Compare degradation kinetics between wild-type and pep4 mutant cells to quantify the proportion of protein degraded via vacuolar pathways

• Endocytosis Pathway Controls:

  • Include end4 mutant strains (deficient in endocytosis) to determine if the protein cycles through the plasma membrane

  • Use vps4 mutant strains to examine the involvement of the endosomal sorting complex

• Ubiquitination Status Controls:

  • Include analysis of ubiquitination status at different time points

  • Consider using rsp5 ubiquitin-ligase mutants to determine if this pathway contributes to protein degradation

• Expression System Controls:

  • Compare degradation kinetics between native promoter and overexpression systems

  • Include untagged versions of the protein to ensure tags do not artificially alter degradation rates

• Time-point Selection:

  • Sample at appropriate intervals (e.g., 0, 15, 30, 60, 120 minutes) to accurately capture degradation kinetics

  • Continue sampling until signal reaches baseline to establish complete degradation profiles

Similar studies with the Prm1 protein demonstrated that most newly synthesized protein is rapidly degraded in vacuoles, with a small subpopulation remaining stable. These controls will help determine if Kpol_1003p17 follows similar degradation patterns .

How can blocking designs be implemented to optimize resources when investigating Kpol_1003p17 interactions with multiple membrane components?

When investigating Kpol_1003p17 interactions with multiple membrane components, implementing blocking designs can significantly optimize resources and improve experimental precision:

• Randomized Complete Block Design Implementation:

  • Group experimental units (e.g., yeast strains, protein batches) into homogeneous blocks

  • Within each block, randomly assign treatments (e.g., different membrane components)

  • Example blocking factors: protein preparation batches, yeast strain backgrounds, or experimental days

• Practical Application in Pull-down Assays:

  • Block structure: Prepare a single batch of purified Kpol_1003p17 protein (Block 1)

  • Treatments within block: Test interactions with various membrane components (phospholipids, proteins, sterols)

  • Analysis: Compare interaction strengths within the same protein preparation block to reduce variability

• Application in Microscopy Studies:

  • Block structure: Image all treatments on the same day with identical microscope settings

  • Randomization: Randomize the order of imaging within blocks

  • Quantification: Normalize fluorescence intensity measurements within each imaging session

• Statistical Power Considerations:

  • Conduct power analysis to determine minimum required sample size

  • For typical membrane protein interaction studies, aim for n=4-6 biological replicates per treatment

  • Use statistical software to analyze results with block as a random effect

This blocking approach can reduce experimental variability by 30-40%, allowing researchers to detect significant interaction differences with fewer replicates, thus saving time and resources .

What experimental approaches can distinguish between direct and indirect interactions of Kpol_1003p17 with other vacuolar membrane proteins?

To distinguish between direct and indirect interactions of Kpol_1003p17 with other vacuolar membrane proteins, researchers should employ a tiered experimental approach combining in vitro and in vivo methods:

• In Vitro Direct Binding Assays:

  • Surface Plasmon Resonance (SPR): Immobilize purified Kpol_1003p17 on a sensor chip and measure binding kinetics with candidate proteins. Direct interactions will show concentration-dependent binding curves with calculable affinity constants.

  • Microscale Thermophoresis (MST): Label Kpol_1003p17 with a fluorescent dye and measure thermophoretic mobility changes upon binding to unlabeled partners. This technique requires minimal protein amounts and works well with membrane proteins.

• Proximity-based In Vivo Assays:

  • Bimolecular Fluorescence Complementation (BiFC): Fuse Kpol_1003p17 and candidate protein with complementary fragments of a fluorescent protein. Direct interactions bring fragments together, restoring fluorescence.

  • Split-Ubiquitin Yeast Two-Hybrid: Particularly suited for membrane proteins, this assay can detect direct interactions at native cellular locations.

• Co-immunoprecipitation with Controls:

  • Stringency Series: Perform co-IP under increasing detergent or salt concentrations. Direct interactions typically withstand higher stringency conditions.

  • Domain Mapping: Create truncation variants of Kpol_1003p17 to identify specific interaction domains.

  • Cross-linking: Use chemical cross-linkers of various lengths to capture transient direct interactions.

• Reconstitution Experiments:

  • Liposome Reconstitution: Incorporate purified Kpol_1003p17 and candidate proteins into artificial liposomes. Direct interactions will occur in this minimal system lacking other cellular components.

  • Crosslinking Mass Spectrometry: Identify direct binding interfaces at amino acid resolution.

• Genetic Interaction Analysis:

  • Epistasis Analysis: Compare phenotypes of single and double mutants. Non-additive effects suggest proteins function in the same pathway but don't confirm direct interaction.

  • Suppressor Screens: Identify mutations in one protein that suppress effects of mutations in the other.

Drawing from approaches used to study Prm1, researchers should be aware that membrane protein interactions may be transient or stabilized by lipid environments, requiring careful experimental design and interpretation .

How can researchers effectively investigate the role of Kpol_1003p17 in membrane fusion events using genetic approaches?

To effectively investigate the role of Kpol_1003p17 in membrane fusion events using genetic approaches, researchers should implement a comprehensive strategy combining deletion analysis, domain mapping, and phenotypic characterization:

• Targeted Gene Deletion Analysis:

  • Generate complete Kpol_1003p17 knockout strains using CRISPR-Cas9 or homologous recombination

  • Create a complementation series with the wild-type gene under native and inducible promoters

  • Assess phenotypic outcomes including:

    • Vacuolar morphology (using FM4-64 staining)

    • Membrane fusion capacity (using in vivo and in vitro fusion assays)

    • Cell viability under stress conditions

• Domain-Specific Mutant Series:

  • Based on the amino acid sequence of Kpol_1003p17, identify key domains:

    • Transmembrane domains (predicted at residues 172-192)

    • Potential phosphorylation sites (serine/threonine-rich regions)

    • Conserved motifs shared with other fusion proteins

  • Generate specific mutations targeting these domains:

    • Alanine scanning mutagenesis of conserved residues

    • Domain deletion mutants

    • Point mutations at potential regulatory sites

• Genetic Interaction Mapping:

  • Perform synthetic genetic array (SGA) analysis with the Kpol_1003p17 mutant

  • Focus on interactions with known membrane fusion components:

    • SNARE proteins

    • Tethering factors

    • Lipid-modifying enzymes

  • Create double mutants with genes showing strongest interactions

• Suppressor Screen Approaches:

  • Identify genetic suppressors of Kpol_1003p17 deletion phenotypes

  • Use error-prone PCR to generate a library of random Kpol_1003p17 mutants

  • Screen for hypermorphic or neomorphic alleles with enhanced fusion activity

• Regulated Expression Systems:

  • Implement galactose-regulated expression systems similar to those used for Prm1

  • Monitor phenotypic consequences of protein depletion and re-introduction

  • Use time-course experiments to determine the temporal requirements for protein function

• Marker Protein Analysis:

  • Monitor trafficking of vacuolar marker proteins in wild-type versus mutant backgrounds

  • Track the dynamics of membrane proteins known to undergo endocytosis and vacuolar targeting

This experimental approach is informed by studies of Prm1, which demonstrated that membrane fusion proteins may function at specific contact sites despite broader cellular distribution. Detailed phenotypic analysis should focus on both steady-state conditions and dynamic responses to environmental changes that trigger membrane fusion events .

How can researchers prevent pseudo-replication when collecting data on Kpol_1003p17 localization patterns?

Preventing pseudo-replication in Kpol_1003p17 localization studies requires careful experimental design and statistical analysis approaches:

• Proper Experimental Unit Identification:

  • Clearly define independent experimental units (e.g., individual yeast cultures from separate starter colonies)

  • Avoid treating multiple measurements from the same culture as independent replicates

  • For microscopy studies, distinguish between technical replicates (multiple fields of view from the same slide) and biological replicates (cells from independent cultures)

• Hierarchical Sampling Design:

  • Implement a nested sampling approach:

    • Level 1: Independent biological samples (separate transformants/cultures)

    • Level 2: Individual cells within each sample

    • Level 3: Subcellular regions or time points within cells

  • Maintain proper sample identification throughout the experimental workflow

• Statistical Analysis Approaches:

  • Use mixed-effects models that account for the hierarchical nature of the data:

    • Include random effects for biological replicates

    • Nest technical replicates within biological replicates

  • Calculate variance components to determine sources of variability

  • Avoid pooling data from different hierarchical levels

• Practical Implementation Example:

  • Instead of analyzing 300 cells from a single culture, analyze 60 cells from each of 5 independent cultures

  • For time-course experiments, prepare separate cultures for each time point rather than sampling the same culture repeatedly

  • For co-localization studies, perform independent transformations and protein preparations

• Documentation and Reporting:

  • Clearly report the number of biological replicates (n) versus the number of observations

  • Document all sources of potential non-independence in the experimental design

  • Provide raw data organization that reflects the hierarchical structure

This approach aligns with established experimental design principles that emphasize the importance of properly defining experimental units to prevent inflated statistical power and false positive results. When studying dynamic processes like protein trafficking through the vacuolar pathway, proper experimental design is particularly crucial as temporal correlations can introduce additional dependencies in the data .

What analytical approaches can resolve conflicting localization data for Kpol_1003p17 in different experimental systems?

When faced with conflicting localization data for Kpol_1003p17 across different experimental systems, researchers should implement a systematic analytical approach:

• Meta-analysis Framework:

  • Compile all localization data with detailed metadata (cell type, expression system, tag location, imaging method)

  • Create a standardized scoring system for localization patterns (primary location, secondary locations, signal intensity)

  • Weight studies based on methodological rigor and sample size

  • Identify patterns in conflicting results related to specific experimental variables

• Experimental Variables Assessment:

  • Expression Level Analysis:

    • Compare native expression versus overexpression systems

    • Quantify protein levels across systems using western blotting

    • Determine if localization patterns shift with expression level

  • Tag Interference Evaluation:

    • Compare N-terminal versus C-terminal tags

    • Test multiple tag types (GFP, HA, FLAG) for differential effects

    • Include untagged protein controls with antibody detection

• Time-resolved Analysis:

  • Implement pulse-chase experiments to track protein movement

  • Create time-based localization profiles for each experimental system

  • Determine if apparent conflicts reflect different points in a dynamic process

  • Example: Using a GAL-regulated GFP-Kpol_1003p17 construct:

• Conditional Factor Analysis:

  • Test the effects of:

    • Growth conditions (nutrient availability, stress)

    • Cell cycle stage

    • Genetic background (wild-type vs. trafficking mutants)

  • Create a decision tree to predict localization based on conditions

• Orthogonal Method Validation:

  • Complement microscopy with biochemical fractionation

  • Perform protease protection assays to determine membrane topology

  • Use proximity labeling techniques (BioID, APEX) to map protein neighborhoods

• Bayesian Integrative Analysis:

  • Develop a probabilistic model incorporating all data sources

  • Calculate confidence scores for each localization pattern

  • Generate consensus localization maps weighted by reliability

This approach draws from studies of proteins like Prm1, which exhibited complex localization patterns dependent on expression levels, cellular conditions, and temporal dynamics. The apparent conflicts often reflect biological reality - membrane proteins frequently exist in multiple cellular compartments simultaneously, with only a small functional pool at specific sites like polarized plasma membrane domains .

How can blocking designs be implemented to optimize resources when investigating Kpol_1003p17 interactions with multiple membrane components?

When investigating Kpol_1003p17 interactions with multiple membrane components, implementing effective blocking designs can significantly optimize resources and improve experimental power:

• Randomized Complete Block Design Implementation:

  • Group experimental units into homogeneous blocks based on factors like protein preparation batch, cell culture passage, or experimental day

  • Within each block, randomly assign all treatments (different membrane components)

  • This approach controls for batch-to-batch variation that could otherwise mask treatment effects

• Latin Square Design for Multiple Factors:

  • When testing Kpol_1003p17 interactions across multiple lipid compositions and pH conditions:

    • Arrange treatments in a square grid where each row and column contains each treatment exactly once

    • This balanced design efficiently controls for two blocking factors simultaneously

• Split-Plot Design for Resource Efficiency:

  • When some experimental factors are more difficult to change than others:

    • Apply hard-to-change factors (e.g., protein purification method) at the whole-plot level

    • Apply easy-to-change factors (e.g., buffer composition) at the split-plot level

  • This approach minimizes the number of difficult operations while maintaining statistical power

• Incomplete Block Design for Large-Scale Screening:

  • When testing too many membrane components to fit in a single experiment:

    • Create smaller blocks containing subsets of treatments

    • Ensure each pair of treatments appears together in at least one block

    • Use specialized statistical methods to analyze unbalanced data

• Quantitative Resource Optimization:

• Practical Implementation Example:

  • When testing Kpol_1003p17 interactions with 5 different lipid compositions:

    • Instead of 5 separate protein preparations (one per lipid condition) requiring 25 total assays (5 replicates each)

    • Prepare 5 protein batches, each tested against all 5 lipid conditions

    • This approach controls for batch effects while maintaining the same total number of assays

By implementing appropriate blocking designs, researchers can achieve the same statistical power with fewer resources or increase power without additional resource expenditure, making investigations of Kpol_1003p17 interactions more efficient and reliable .

What experimental considerations are essential when designing studies to investigate Kpol_1003p17 trafficking under various cellular stress conditions?

When designing studies to investigate Kpol_1003p17 trafficking under various cellular stress conditions, several critical experimental considerations must be addressed:

• Stress Condition Standardization:

  • Establish precise protocols for each stress condition:

    • Osmotic stress: Define exact concentrations (e.g., 0.4M NaCl, 1M sorbitol)

    • Oxidative stress: Standardize H₂O₂ concentration and exposure time

    • Nutrient deprivation: Define media composition and starvation duration

    • Temperature stress: Establish precise temperature shifts and durations

  • Include recovery phases to assess reversibility of trafficking changes

  • Monitor cellular viability to distinguish trafficking changes from cell death effects

• Temporal Resolution Planning:

  • Implement time-course designs with appropriate intervals:

    • Acute response: 0, 5, 15, 30, 60 minutes after stress induction

    • Adaptive response: 1, 2, 4, 8, 24 hours after stress induction

  • Use synchronous cultures when appropriate to control for cell cycle effects

  • Employ pulse-chase approaches to track specific protein cohorts

• Protein Expression System Selection:

  • Consider multiple expression approaches:

    • Native promoter: Physiological expression but potentially affected by stress

    • Constitutive promoter: Consistent expression independent of stress

    • Inducible promoter: Controlled timing separate from stress induction

  • Quantify expression levels under each condition to normalize trafficking data

  • Include protein synthesis and degradation controls (cycloheximide treatment)

• Imaging and Quantification Strategy:

  • Design quantitative image analysis workflows:

    • Define objective compartment markers and colocalization thresholds

    • Establish automated analysis pipelines to reduce bias

    • Include internal calibration standards for fluorescence intensity

  • Implement multi-channel approaches to simultaneously track:

    • Target protein (GFP-Kpol_1003p17)

    • Organelle markers (endosomes, vacuoles, Golgi)

    • Stress response indicators (e.g., Hsp104 for heat stress)

• Genetic Background Considerations:

  • Include key mutant backgrounds:

    • Trafficking pathway mutants (end4, vps4, pep4)

    • Stress response pathway mutants (hog1, ire1, hsf1)

  • Create double-mutant combinations to test pathway interactions

  • Include complementation controls to confirm specificity

• Controls for Confounding Variables:

  • Address potential confounders:

    • pH changes: Monitor and control intracellular pH during stress

    • Membrane integrity: Include membrane permeability assays

    • Global trafficking effects: Track control proteins with known pathways

    • Energy status: Monitor ATP levels that might affect trafficking

• Crossover Design Implementation:

  • To efficiently test multiple stress conditions:

    • Apply different stresses to the same culture in varying sequences

    • Include appropriate recovery periods between stresses

    • Analyze order effects to identify pathway interactions

This comprehensive approach addresses experimental design principles while incorporating specific considerations for membrane protein trafficking studies. By systematically controlling variables and including appropriate controls, researchers can distinguish Kpol_1003p17-specific trafficking responses from general cellular effects under stress conditions .

How can researchers integrate findings about Kpol_1003p17 with broader knowledge of vacuolar membrane proteins across yeast species?

Researchers can effectively integrate findings about Kpol_1003p17 with broader knowledge of vacuolar membrane proteins through a multi-dimensional comparative approach:

• Phylogenetic Framework Development:

  • Construct comprehensive phylogenetic trees of vacuolar membrane proteins across yeast species

  • Map functional domains and motifs across evolutionary distance

  • Identify orthologous proteins in model yeasts (S. cerevisiae, S. pombe) for comparative studies

  • Use this framework to predict functional conservation or divergence

• Systems Biology Integration:

  • Develop protein-protein interaction networks centered on Kpol_1003p17

  • Compare with interaction networks of related proteins like Prm1

  • Identify conserved and species-specific interaction partners

  • Map Kpol_1003p17 function within broader cellular pathways

• Functional Domain Comparison:

  • Create domain-function maps based on mutagenesis studies

  • Compare trafficking signals, localization patterns, and degradation kinetics

  • Identify conserved mechanisms versus species-specific adaptations

  • Develop predictive models for protein behavior based on sequence features

• Cross-species Complementation Studies:

  • Express Kpol_1003p17 in other yeast species with mutations in related vacuolar proteins

  • Assess functional rescue capabilities to determine functional equivalence

  • Use domain swapping to identify critical regions for species-specific functions

  • Create chimeric proteins to test domain portability

• Regulatory Network Comparison:

  • Compare transcriptional and post-translational regulation across species

  • Identify conserved stress response patterns affecting vacuolar protein trafficking

  • Map species-specific regulatory adaptations to ecological niches

  • Develop predictive models for protein regulation under various conditions

Through this integrated approach, researchers can position Kpol_1003p17 within the broader context of vacuolar membrane protein evolution and function. This comparative framework allows findings from well-studied systems to inform research on less characterized proteins, while highlighting unique aspects of Kpol_1003p17 that may represent specialized adaptations in Vanderwaltozyma polyspora .

What are the most promising future research directions for understanding the functional significance of Kpol_1003p17 in cellular processes?

The most promising future research directions for understanding the functional significance of Kpol_1003p17 in cellular processes include:

• Structure-Function Relationship Elucidation:

  • Determine the high-resolution structure using cryo-electron microscopy or X-ray crystallography

  • Identify functional domains through systematic mutagenesis

  • Map lipid-interaction domains and membrane topology

  • Develop in silico models to predict protein-protein and protein-lipid interactions

• Physiological Function Investigation:

  • Create conditional knockout systems to study function under various conditions

  • Identify cellular processes affected by Kpol_1003p17 deletion or overexpression

  • Explore potential roles in:

    • Vacuolar fusion and fission dynamics

    • Stress response mechanisms

    • Nutrient sensing and transport

    • Cell cycle regulation

• Interactome Mapping:

  • Implement proximity labeling approaches (BioID, APEX) to identify neighboring proteins

  • Perform quantitative proteomics under various conditions to detect dynamic interactions

  • Create detailed protein complex maps using native mass spectrometry

  • Validate key interactions through targeted biochemical approaches

• Cellular Trafficking Dynamics:

  • Implement advanced live-cell imaging with super-resolution microscopy

  • Track protein movement using photoactivatable fluorescent proteins

  • Quantify protein turnover rates using fluorescence recovery after photobleaching (FRAP)

  • Develop mathematical models of protein trafficking and degradation kinetics

• Comparative Systems Biology:

  • Compare function with homologous proteins in other yeast species

  • Integrate Kpol_1003p17 into broader cellular network models

  • Identify conserved pathways versus species-specific adaptations

  • Explore evolutionary trajectories of vacuolar membrane proteins

• Potential Biotechnological Applications:

  • Investigate potential uses in:

    • Protein expression optimization in yeast systems

    • Membrane protein stabilization for structural studies

    • Stress tolerance engineering in industrial yeasts

    • Biosensor development for cellular stress

• Methodological Innovations:

  • Develop improved techniques for:

    • Membrane protein purification while maintaining native interactions

    • In situ structural analysis of membrane protein complexes

    • Quantitative assessment of protein-lipid interactions

    • Tracking protein dynamics at single-molecule resolution