Recombinant Saccharomyces cerevisiae Putative uncharacterized YKL165C-A (YKL165C-A)

What is YKL165C-A and what do we currently know about its structure?

YKL165C-A is a putative uncharacterized protein from Saccharomyces cerevisiae (baker's yeast). It consists of 77 amino acids with the following sequence: MQFPVFFFRCFSYGISSMPLKNKVVFNENMERKDTFYQLILKVLSALLLLSVRNSSGHTR HFVQSSEKIYRRSLFKQ . The protein is classified as "putative uncharacterized," indicating that its precise function has not been definitively established. Current structural data is limited, with no published crystal structure available. Researchers typically work with recombinant forms of the protein, often expressed in E. coli with affinity tags (such as His-tag) to facilitate purification and subsequent analysis .

How can I obtain recombinant YKL165C-A for my experiments?

Recombinant YKL165C-A can be obtained through several approaches:

• Commercial sources: Pre-made recombinant protein is available from specialized suppliers who provide His-tagged full-length YKL165C-A (1-77 amino acids) expressed in E. coli systems .

• In-house expression: Researchers can generate their own recombinant YKL165C-A by:

  • Designing and synthesizing or acquiring the gene sequence

  • Cloning into an appropriate expression vector with desired fusion tags

  • Transforming into a suitable expression system (typically E. coli)

  • Inducing expression under optimized conditions

  • Purifying using affinity chromatography based on the fusion tag

For reproducing experimental results, it is important to document the exact source and preparation method of the protein, as variations in expression systems can affect post-translational modifications and protein folding.

What expression systems are recommended for producing recombinant YKL165C-A?

While E. coli is the most commonly used expression system for recombinant YKL165C-A , researchers should consider the following systems based on experimental requirements:

The choice of expression system should be guided by research objectives. For studying YKL165C-A function in its native context, expression in S. cerevisiae with minimal tags is often preferable despite potentially lower yields.

How should I design experiments to characterize the function of an uncharacterized protein like YKL165C-A?

Characterizing uncharacterized proteins like YKL165C-A requires a systematic experimental approach. Begin by defining clear research questions and variables related to potential functions . A recommended experimental workflow includes:

• Sequence analysis and prediction:

  • Conduct bioinformatic analysis for conserved domains

  • Perform phylogenetic comparisons with characterized proteins

  • Use structure prediction algorithms to inform functional hypotheses

• Localization studies:

  • Create fluorescent protein fusions to determine subcellular localization

  • Use fractionation techniques to confirm localization biochemically

  • Design between-subjects experiments comparing wild-type and tagged constructs to ensure tag doesn't disrupt function

• Interaction studies:

  • Implement yeast two-hybrid screens to identify potential binding partners

  • Confirm interactions using co-immunoprecipitation and pull-down assays

  • Create interaction networks to situate the protein in cellular pathways

• Loss-of-function studies:

  • Generate knockout strains using CRISPR or traditional deletion techniques

  • Perform comprehensive phenotypic characterization under various conditions

  • Implement rescue experiments with wild-type and mutant constructs

Each step should include appropriate controls and replications to ensure reliable results. Documenting negative results is equally important, as they can provide valuable insights into what the protein does not do 6.

What are the best methods for studying protein-protein interactions involving YKL165C-A?

When investigating protein-protein interactions involving YKL165C-A, multiple complementary approaches should be employed:

• In vivo techniques:

  • Yeast two-hybrid (Y2H) screening using YKL165C-A as bait against a yeast library

  • Proximity-dependent biotin identification (BioID) to identify neighboring proteins

  • Fluorescence resonance energy transfer (FRET) to confirm direct interactions in live cells

• In vitro techniques:

  • Pull-down assays using recombinant His-tagged YKL165C-A

  • Surface plasmon resonance (SPR) to determine binding kinetics

  • Isothermal titration calorimetry (ITC) for thermodynamic characterization

• Validation strategies:

  • Co-immunoprecipitation from native yeast cells

  • Reciprocal tagging of potential interactors

  • Competition assays with purified domains

Each method has distinct advantages and limitations. For example, Y2H may detect transient interactions but can generate false positives, while co-IP confirms interactions in native contexts but may miss weak or transient associations. Therefore, using multiple methods strengthens confidence in identified interactions. Document all experimental conditions thoroughly, including buffer compositions, temperature, and protein concentrations, as these can significantly affect interaction detection6.

How can I design controlled experiments to study the effect of YKL165C-A knockouts in yeast?

Designing controlled experiments to study YKL165C-A knockouts requires careful consideration of variables and appropriate controls . A systematic approach would include:

• Strain development:

  • Generate YKL165C-A deletion strain using homologous recombination or CRISPR-Cas9

  • Create complemented strains reintroducing wild-type YKL165C-A

  • Develop site-directed mutants to test specific functional hypotheses

  • Ensure genetic background is consistent across all strains

• Experimental design considerations:

  • Use between-subjects design comparing wild-type, knockout, and complemented strains

  • Control extraneous variables such as media composition, temperature, and growth phase

  • Include technical replicates (minimum 3) and biological replicates (minimum 3)

  • Implement blind analysis when possible to reduce experimenter bias 6

• Phenotypic characterization matrix:

• Validation approaches:

  • Rescue experiments reintroducing YKL165C-A under native or inducible promoters

  • Cross-complementation with homologs from related species

  • Dose-response relationships with controlled expression levels

When reporting results, clearly distinguish between direct phenotypic effects and potential secondary effects due to compensatory mechanisms or strain adaptation .

What advanced proteomic approaches can help elucidate YKL165C-A function in the cellular context?

Elucidating YKL165C-A function requires sophisticated proteomic approaches that can provide insights into its dynamic behavior and interactome:

• Proximity-based labeling techniques:

  • BioID fusion to YKL165C-A to identify proximal proteins through biotinylation

  • APEX2 labeling for temporal resolution of interaction dynamics

  • Split-BioID for conditional proximity mapping

• Quantitative interaction proteomics:

  • SILAC (Stable Isotope Labeling with Amino acids in Cell culture) combined with IP-MS

  • TMT (Tandem Mass Tag) labeling for multiplexed comparison across conditions

  • Label-free quantification for native protein complexes

• Structural proteomics:

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

  • Cross-linking mass spectrometry (XL-MS) to map protein interfaces

  • Native mass spectrometry to analyze intact complexes

• Global proteome impact assessment:

  • Comparative proteomics between wild-type and YKL165C-A deletion strains

  • Phosphoproteomics to detect signaling pathway alterations

  • Thermal proteome profiling to identify proteins affected by YKL165C-A deletion

For each approach, establish rigorous statistical frameworks for data analysis, including appropriate normalization methods, significance thresholds, and multiple testing corrections. Cross-validate findings using orthogonal techniques and integrate datasets to build comprehensive functional models. Consider temporal dynamics by sampling at multiple timepoints, particularly following perturbations that might engage YKL165C-A-dependent pathways.

How can I resolve contradictory data about YKL165C-A localization or function?

Resolving contradictory data about YKL165C-A requires a systematic approach to identify sources of variability and reconcile disparate findings:

• Methodological reconciliation:

  • Compare experimental conditions in detail (strain backgrounds, growth media, temperature)

  • Evaluate tag positions and types (N-terminal vs. C-terminal, size of tags)

  • Assess detection methods (direct fluorescence vs. immunofluorescence, antibody specificity)

  • Consider temporal aspects (cell cycle stage, growth phase, induction conditions)

• Technical validation strategies:

  • Implement orthogonal techniques to confirm findings (e.g., both microscopy and biochemical fractionation for localization)

  • Vary tag orientation and type to rule out tag interference

  • Use complementary antibodies targeting different epitopes

  • Perform rescue experiments with untagged constructs

• Biological context analysis:

  • Test for condition-specific behaviors (stress conditions, nutrient availability)

  • Investigate cell-to-cell variability through single-cell approaches

  • Examine potential moonlighting functions in different cellular compartments

  • Consider post-translational modifications that might affect localization or function

• Collaborative resolution approaches:

  • Arrange direct comparison experiments between laboratories reporting contradictory results

  • Exchange key reagents (strains, antibodies, constructs) to rule out reagent variability

  • Standardize protocols and establish minimal reporting requirements

What computational approaches can predict YKL165C-A function and guide experimental design?

Computational approaches provide valuable insights for predicting YKL165C-A function and strategically guiding experimental design:

• Sequence-based predictions:

  • Profile Hidden Markov Models to identify remote homology

  • Conservation analysis across yeast species to identify functional constraints

  • Coevolution analysis to predict interaction interfaces

  • Disorder prediction to identify flexible regions potentially involved in interactions

• Structural modeling:

  • Ab initio modeling for domains lacking homology to known structures

  • Molecular dynamics simulations to predict conformational flexibility

  • Binding site prediction to identify potential ligand pockets

  • Protein-protein docking with predicted interaction partners

• Systems-level analyses:

  • Gene neighborhood analysis and synteny across species

  • Coexpression network analysis to identify functionally related genes

  • Genetic interaction profiles comparison with genes of known function

  • Metabolic modeling to predict involvement in specific pathways

• Machine learning applications:

  • Functional prediction using ensemble methods integrating multiple features

  • Transfer learning from characterized proteins to YKL165C-A

  • Deep learning on structural features to predict binding sites

A strategic implementation would involve:

• Generating multiple computational hypotheses

• Ranking predictions by confidence scores

• Designing targeted experiments to test specific computational predictions

• Iteratively refining models based on experimental feedback

What are the most common technical challenges when working with YKL165C-A and how can they be addressed?

Researchers working with YKL165C-A frequently encounter several technical challenges that can be addressed through methodological refinements:

• Protein solubility and stability issues:

  • Challenge: Recombinant YKL165C-A may form aggregates or inclusion bodies during expression

  • Solutions:

    • Optimize expression temperature (typically lowering to 16-18°C)

    • Test multiple solubility tags (MBP, SUMO, Thioredoxin)

    • Incorporate stabilizing additives in buffers (glycerol, arginine, low concentrations of detergents)

    • Consider native purification from S. cerevisiae despite lower yields

• Antibody specificity problems:

  • Challenge: Generating specific antibodies against small proteins like YKL165C-A (77 amino acids)

  • Solutions:

    • Use epitope mapping to identify unique regions

    • Validate antibodies using knockout strains as negative controls

    • Consider alternative detection methods (e.g., epitope tagging)

    • Implement sandwich ELISA approaches for improved specificity

• Expression level detection:

  • Challenge: Low endogenous expression levels making detection difficult

  • Solutions:

    • Employ sensitive detection methods (nested PCR, digital PCR for mRNA)

    • Use signal amplification methods for protein detection

    • Consider concentration steps before analysis

    • Optimize induction conditions for recombinant expression

• Functional redundancy masking phenotypes:

  • Challenge: Lack of clear phenotypes in single gene deletions due to compensatory mechanisms

  • Solutions:

    • Create combinatorial knockouts with related genes

    • Test phenotypes under diverse stress conditions

    • Use sensitized genetic backgrounds

    • Implement acute depletion systems (e.g., degron tags) to bypass compensation

For each challenge, systematic optimization should be documented thoroughly to establish reproducible protocols. Collaborative approaches, sharing both successful and failed strategies across research groups, can accelerate technical problem-solving in this field.

How should I optimize experimental conditions when studying YKL165C-A protein-protein interactions?

Optimizing experimental conditions for studying YKL165C-A protein-protein interactions requires systematic parameter adjustment and validation:

• Buffer optimization strategy:

  • Test multiple buffer compositions systematically:

• Protein state considerations:

  • Compare native extraction vs. recombinant proteins

  • Test tag position effects (N-terminal vs. C-terminal)

  • Evaluate effect of post-translational modifications on interactions

  • Control protein concentration ratios in binding assays

• Interaction detection optimization:

  • Adjust incubation times (15 min to overnight) and temperatures (4°C to 30°C)

  • Optimize washing stringency in pull-down assays

  • Compare different detection methods (Western blot vs. mass spectrometry)

  • Implement crosslinking strategies for transient interactions

• Validation controls:

  • Include known interacting protein pairs as positive controls

  • Use unrelated proteins with similar biochemical properties as negative controls

  • Perform competition assays with unlabeled proteins

  • Test interaction dependency on specific domains through truncation constructs

For each interaction identified, establish a confidence score based on reproducibility across methods, detection under multiple conditions, and validation through orthogonal approaches. Record all optimization steps in detail to ensure reproducibility and to inform future interaction studies with YKL165C-A.

What are the emerging technologies that could advance our understanding of YKL165C-A function?

Several cutting-edge technologies are poised to significantly advance our understanding of YKL165C-A function:

• Advanced imaging technologies:

  • Super-resolution microscopy (PALM/STORM) for precise localization studies

  • Cryo-electron tomography to visualize YKL165C-A in its native cellular context

  • Live-cell single-molecule tracking to monitor dynamics and interactions

  • Correlative light and electron microscopy (CLEM) to bridge molecular and ultrastructural information

• Genome engineering advances:

  • CRISPR base editing for precise point mutations without DNA breaks

  • CRISPRi/CRISPRa for tunable expression modulation

  • Genome-wide interaction screens using CRISPR-based approaches

  • Site-specific recombination systems for controlled temporal studies

• Single-cell technologies:

  • Single-cell proteomics to examine YKL165C-A expression heterogeneity

  • Single-cell metabolomics to link YKL165C-A to metabolic phenotypes

  • Multi-omics integration at single-cell resolution

  • Microfluidic systems for temporally resolved single-cell studies

• Structural biology innovations:

  • Cryo-EM for structural determination of YKL165C-A complexes

  • Integrative structural biology combining multiple data types

  • AlphaFold2 and related AI approaches for structure prediction

  • Time-resolved structural methods to capture conformational changes

To effectively leverage these technologies, researchers should focus on collaborative approaches that combine technical expertise across disciplines. Consider establishing consortia or collaborative networks specifically focused on uncharacterized yeast proteins like YKL165C-A, pooling resources and standardizing protocols to accelerate discoveries.

How can systems biology approaches contribute to understanding YKL165C-A in the context of cellular networks?

Systems biology approaches provide powerful frameworks for understanding YKL165C-A within the broader context of cellular networks:

• Network integration strategies:

  • Construct multi-layered networks incorporating:

    • Protein-protein interaction data

    • Genetic interaction profiles

    • Transcriptional regulation networks

    • Metabolic pathway connections

  • Apply network analysis algorithms to identify:

    • Network motifs involving YKL165C-A

    • Centrality measures to assess functional importance

    • Modularity to identify functional clusters

• Perturbation-based approaches:

  • Implement systematic environmental perturbations:

    • Chemical genetic profiling under diverse conditions

    • Temporal response to stress conditions

    • Nutrient limitation studies

  • Analyze differential network states:

    • Condition-specific rewiring involving YKL165C-A

    • Dynamic changes in interaction partners

    • Compensatory network adaptations in YKL165C-A mutants

• Predictive modeling frameworks:

  • Develop kinetic models of pathways potentially involving YKL165C-A

  • Implement constraint-based models to predict metabolic impacts

  • Apply Bayesian networks to infer causal relationships

  • Utilize machine learning to predict emergent phenotypes

• Multi-omics data integration:

  • Correlate protein abundance, localization, modification, and interactions

  • Identify condition-specific activation of YKL165C-A functions

  • Map impacts of YKL165C-A perturbation across biological scales

  • Develop visualization tools for multi-dimensional data exploration

The systems biology approach is particularly valuable for uncharacterized proteins like YKL165C-A, as it can reveal functional context even when direct biochemical functions remain unclear. This holistic perspective can illuminate roles in cellular homeostasis, stress responses, or metabolic regulation that might be missed by reductionist approaches alone.