Recombinant Escherichia coli Uncharacterized protein ytcA (ytcA)

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Description

Introduction to Recombinant Escherichia coli Uncharacterized Protein ytcA (ytcA)

Recombinant E. coli uncharacterized protein ytcA (ytcA) is a transmembrane protein expressed in E. coli through heterologous production systems. Despite its classification as "uncharacterized," ytcA has been studied in the context of recombinant protein expression and structural analysis. Its biological function remains undefined, but its production parameters and biochemical properties have been documented in commercial and academic research.

Expression System and Purification

ytcA is typically expressed in E. coli using in vitro systems, such as the BL21(DE3) strain, under T7 RNA polymerase-driven promoters (e.g., pET vectors). The recombinant protein is purified via nickel affinity chromatography due to its N-terminal 10xHis-tag .

ParameterDetails
Expression HostE. coli BL21(DE3) or similar strains
TagN-terminal 10xHis-tag
Expression RegionResidues 27–91 (partial sequence)
SequencePartial sequence: CSLSPAIPVIGAYYPGWFFCAIASLILTLITRRIIQRTNINLAFVGIIYTALFALYAMLF WLAFF

Biochemical Properties

ytcA is classified as a transmembrane protein, though its exact topology and membrane interaction mechanisms are not fully resolved. Its production often requires optimized conditions to prevent aggregation and ensure solubility .

Genomic and Evolutionary Context

ytcA is encoded by the ytcA gene (locus c5088 in E. coli O6), part of a subset of genes annotated as "uncharacterized" due to insufficient experimental data. Similar E. coli proteins, such as YtfB, have been linked to cell division and adhesion , suggesting ytcA may play roles in cellular processes like membrane integrity or signal transduction.

Comparative Analysis with Related Proteins

ProteinDomainProposed FunctionSource
ytcATransmembraneMembrane-associated role
YtfBLysM-likeCell division, glycan binding

Limited Functional Data

No studies directly address ytcA’s biological role. Its uncharacterized status reflects gaps in experimental validation, a common issue with E. coli "y-genes" (unannotated genes) .

Technical Limitations

Recombinant production of ytcA faces challenges such as inclusion body formation and low solubility, necessitating optimized expression conditions (e.g., lower temperatures, chaperone co-expression) .

Product Specs

Form
Lyophilized powder
Note: While we prioritize shipping the format currently in stock, we are happy to accommodate special format requests. Please specify your preferred format during order placement, and we will prepare accordingly.
Lead Time
Delivery time may vary depending on the purchasing method and location. For precise delivery estimates, please contact your local distributors.
Note: All protein shipments are standardly packed with blue ice packs. If dry ice shipping is required, please inform us in advance, as additional charges will apply.
Notes
Repeated freezing and thawing is not recommended. Store working aliquots at 4°C for up to one week.
Reconstitution
We recommend centrifuging the vial briefly before opening to ensure the contents are at the bottom. Reconstitute the protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL. We recommend adding 5-50% glycerol (final concentration) and aliquoting for long-term storage at -20°C/-80°C. Our standard glycerol final concentration is 50%, serving as a reference point for your convenience.
Shelf Life
Shelf life is influenced by various factors, including storage conditions, buffer components, storage temperature, and the intrinsic stability of the protein.
Generally, liquid forms have a shelf life of 6 months at -20°C/-80°C. Lyophilized forms have a shelf life of 12 months at -20°C/-80°C.
Storage Condition
Upon receipt, store at -20°C/-80°C. Aliquoting is recommended for multiple uses. Avoid repeated freeze-thaw cycles.
Tag Info
Tag type is determined during the manufacturing process.
The tag type is determined during the production process. If you have a specific tag type preference, please inform us, and we will prioritize developing the specified tag.
Synonyms
ytcA; b4622; Uncharacterized protein YtcA
Buffer Before Lyophilization
Tris/PBS-based buffer, 6% Trehalose.
Datasheet
Please contact us to get it.
Expression Region
27-91
Protein Length
Full Length of Mature Protein
Species
Escherichia coli (strain K12)
Target Names
ytcA
Target Protein Sequence
CSLSPAIPVIGAYYPSWFFCAIASLILTLITRRVIQRANINLAFVGIIYTALFALYAMLF WLAFF
Uniprot No.

Target Background

Database Links

KEGG: eco:b4622

Protein Families
YtcA family
Subcellular Location
Cell membrane; Lipid-anchor. Membrane; Multi-pass membrane protein.

Q&A

What is the uncharacterized protein ytcA in Escherichia coli?

The ytcA protein in Escherichia coli is currently classified as an uncharacterized protein with unknown function. Similar to many other uncharacterized proteins in E. coli, ytcA represents one of the remaining proteins whose biological role, structure, and regulatory mechanisms have not been fully elucidated despite the extensive study of E. coli as a model organism. Bioinformatic analysis suggests ytcA may contain domains consistent with regulatory functions, but experimental validation is required to confirm its precise role in cellular processes.

To characterize uncharacterized proteins like ytcA, researchers typically follow a systematic approach including:

  • Bioinformatic analysis for structural prediction

  • Recombinant expression and purification

  • Structural determination

  • Functional assays

  • Integration of data into existing knowledge frameworks

This methodical approach allows researchers to move from sequence information to functional characterization, gradually building a comprehensive understanding of the protein's role .

What experimental techniques are currently used for initial characterization of proteins like ytcA?

Initial characterization of uncharacterized proteins like ytcA typically involves multiple complementary approaches:

Gene Expression Analysis:

  • RNA-seq to determine expression patterns under various conditions

  • RT-qPCR for validation of expression levels

  • Promoter-reporter fusion constructs to identify regulatory elements

Protein Production and Analysis:

  • Recombinant expression in E. coli BL21 or other expression systems

  • Protein purification using affinity tags (His-tag, GST-tag)

  • Western blotting for protein detection

  • Mass spectrometry for protein identification and post-translational modification analysis

Structural Characterization:

  • X-ray crystallography

  • NMR spectroscopy

  • Cryo-electron microscopy

These methods provide foundational data about protein expression, localization, and structure that guide subsequent functional studies .

How do researchers determine if ytcA functions as a transcription factor?

Determining whether an uncharacterized protein like ytcA functions as a transcription factor requires multiple lines of evidence:

Computational Prediction:

  • Analyze the protein sequence for DNA-binding domains such as helix-turn-helix (HTH) motifs

  • Compare with known transcription factor families using Hidden Markov Models

  • Predict the relative position of potential DNA-binding domains within the protein sequence

Experimental Validation:

  • Chromatin immunoprecipitation followed by sequencing (ChIP-seq) or ChIP-exo to identify genome-wide binding sites

  • Electrophoretic mobility shift assays (EMSA) to confirm direct DNA binding

  • Reporter gene assays to assess transcriptional regulation activity

  • RNA polymerase (RNAP) holoenzyme binding analysis to determine effects on transcription initiation

Based on approaches used for similar uncharacterized proteins, researchers would examine ytcA for structural homology to known transcription factor families such as LysR, AraC, GntR, CheY, TetR, LuxR, GalR/LacI, IclR, or DeoR .

What recombinant expression strategies are most effective for producing ytcA protein for functional studies?

Optimizing recombinant expression of ytcA protein requires careful consideration of expression systems, vectors, and conditions:

Expression System Selection:
E. coli BL21(DE3) is often the first choice for recombinant protein expression due to its deficiency in lon and ompT proteases, which helps prevent protein degradation. For membrane-associated or toxic proteins, alternative strains like C41(DE3) or C43(DE3) may be more appropriate.

Vector and Tag Selection:

  • pET vectors with T7 promoter systems allow for high-level, inducible expression

  • N-terminal or C-terminal His-tags facilitate purification while minimizing interference with protein function

  • Fusion partners like MBP or SUMO can enhance solubility of difficult-to-express proteins

Expression Conditions Table:

ParameterStandard ConditionOptimization Options
Temperature37°C18-30°C for improved folding
Induction OD₆₀₀0.6-0.80.4-1.0 depending on protein
IPTG Concentration1.0 mM0.1-0.5 mM for reduced aggregation
Post-induction Time4 hoursOvernight at lower temperatures
MediaLBTB, 2xYT, or minimal media
SupplementsNoneRare amino acids, chaperones

Troubleshooting Approaches:
If initial expression attempts yield poor results, systematically test different combinations of the above parameters. For particularly challenging proteins, consider cell-free expression systems or alternative hosts like Bacillus subtilis .

How should researchers design comparative analyses to investigate potential functions of ytcA?

Comparative analyses are essential for investigating the function of uncharacterized proteins like ytcA. These analyses should be structured to compare multiple conditions or treatments systematically:

Experimental Design Considerations:

  • Select an appropriate experimental design based on the question being asked

  • For comparing different treatments, use a multi-element design

  • For examining developmental or temporal changes, consider multiple baseline designs

  • For dose-response relationships, implement changing criterion designs

Implementation Strategy:

  • Define the Experimental Question:

    • Is the goal to compare ytcA mutants with wild-type (comparative analysis)?

    • Are you examining different domains of ytcA (component analysis)?

    • Are you testing different expression levels or conditions (parametric analysis)?

  • Select Appropriate Controls:

    • Wild-type E. coli strains

    • Known mutants in related pathways

    • Empty vector controls for recombinant studies

  • Measure Multiple Outcomes:

    • Growth characteristics

    • Gene expression profiles

    • Protein-protein interactions

    • Cellular phenotypes

  • Data Analysis Framework:

    • Apply appropriate statistical tests based on data distribution

    • Consider multiple hypothesis correction for genome-wide studies

    • Integrate data from different experimental approaches

By carefully designing comparative analyses, researchers can systematically eliminate hypotheses and narrow down potential functions of ytcA2 .

What parametric analysis approaches help determine optimal conditions for ytcA functional studies?

Parametric analysis systematically varies experimental parameters to identify optimal conditions. For ytcA studies, this would involve:

Key Parameters to Vary:

  • Temperature and pH ranges

  • Substrate concentrations

  • Cofactor requirements

  • Binding partner concentrations

  • Expression levels

Parametric Analysis Framework:

  • Initial Screening:

    • Broad range testing of conditions using factorial design

    • Identification of significant factors affecting ytcA function

  • Optimization Phase:

    • Fine-tuning of identified significant parameters

    • Response surface methodology to identify optimal combinations

  • Validation:

    • Confirmation of optimal conditions in independent experiments

    • Assessment of reproducibility and robustness

Statistical Approach:

  • ANOVA to evaluate significance of different parameters

  • Regression analysis for continuous variables

  • Machine learning approaches for complex parameter interactions

Parametric analysis allows researchers to determine not just whether ytcA has a particular function, but the optimal conditions under which that function is expressed2.

How can researchers resolve contradictory data when characterizing ytcA function?

When facing contradictory results in ytcA characterization studies, researchers should employ a systematic approach to identify sources of discrepancy:

Common Sources of Contradiction:

  • Different experimental conditions (temperature, pH, strain backgrounds)

  • Varying expression levels affecting protein behavior

  • Post-translational modifications altering function

  • Indirect effects versus direct effects

  • Incomplete gene knockout compensated by redundant systems

Resolution Strategy:

  • Meta-analysis of Experimental Conditions:

    • Document all experimental variables across contradictory studies

    • Identify patterns in conditions that yield different results

    • Design controlled experiments to test specific variable effects

  • Independent Validation:

    • Replicate key experiments using multiple methods

    • Employ orthogonal techniques to confirm findings

    • Collaborate with independent laboratories

  • Reconciliation Framework:

    • Consider if contradictions reflect different aspects of a complex function

    • Develop unified models that accommodate apparently contradictory observations

    • Test integrative hypotheses with new experiments

Documentation Approach:
Maintain comprehensive records of contradictory findings in a structured format:

ObservationExperimental ConditionDetection MethodPotential Confounding FactorsReplication Status
Function ACondition XMethod 1Factor 1, Factor 2Replicated in Lab Y
Function BCondition YMethod 2Factor 3Not independently verified

This systematic approach helps researchers navigate contradictory findings while avoiding confirmation bias .

What bioinformatic approaches provide the most reliable predictions for ytcA function?

Predicting the function of uncharacterized proteins like ytcA requires integrating multiple bioinformatic approaches:

Sequence-Based Methods:

  • Homology searching using PSI-BLAST or HHpred

  • Motif identification using PROSITE, PFAM, or InterPro

  • Disorder prediction to identify flexible regions

  • Subcellular localization prediction

Structure-Based Methods:

  • Homology modeling using tools like SWISS-MODEL or Phyre2

  • Ab initio structure prediction using AlphaFold or RoseTTAFold

  • Structure-based function prediction via structural alignment

  • Active site prediction and analysis

Network-Based Methods:

  • Gene neighborhood analysis

  • Protein-protein interaction prediction

  • Gene expression correlation networks

  • Phylogenetic profiling

Reliability Assessment:
Evaluate predictions using confidence scores and consensus approaches. The most reliable predictions typically:

  • Are supported by multiple independent methods

  • Show high confidence scores across different algorithms

  • Have consistent results across evolutionary relatives

  • Make biological sense in the context of existing knowledge

Implementation Table:

Prediction ApproachRecommended ToolsStrengthsLimitations
Sequence HomologyHHpred, HMMERDetects distant relationshipsMay miss novel functions
Structural PredictionAlphaFold, I-TASSERProvides mechanistic insightsDepends on model quality
Genomic ContextSTRING, GeContIdentifies functional associationsLimited by annotation quality
Machine LearningDeepFRI, COFACTORIntegrates diverse featuresRequires large training datasets

The most reliable approach combines multiple methods and critically evaluates the consistency of predictions across these methods .

How should researchers integrate transcriptomic data to understand ytcA's role in gene regulation?

If ytcA functions in gene regulation, integrating transcriptomic data requires a comprehensive analytical framework:

Data Generation Approach:

  • RNA-seq of wild-type vs. ytcA knockout/overexpression strains

  • Time-course analysis after ytcA induction

  • Condition-specific transcriptomics (stress responses, nutrient limitations)

  • ChIP-seq or ChIP-exo to identify potential binding sites

Analysis Pipeline:

  • Quality Control and Preprocessing:

    • Adapter trimming and quality filtering

    • Read alignment to reference genome

    • Count normalization (TPM, RPKM, or CPM)

  • Differential Expression Analysis:

    • Apply appropriate statistical methods (DESeq2, edgeR, limma)

    • Control for false discovery rate in multiple testing

    • Validate key findings with RT-qPCR

  • Functional Enrichment:

    • Gene Ontology (GO) enrichment analysis

    • Pathway analysis (KEGG, Reactome)

    • Motif enrichment in affected genes

  • Network Analysis:

    • Co-expression network construction

    • Identification of regulatory modules

    • Integration with protein-protein interaction data

Integration Framework:

  • Correlate transcriptomic changes with ChIP-seq binding data

  • Develop regulatory network models

  • Test model predictions with targeted experiments

By systematically analyzing transcriptomic data, researchers can identify direct and indirect effects of ytcA on gene expression and place the protein within the context of E. coli's transcriptional regulatory networks .

How can systems biology approaches enhance understanding of ytcA's role in cellular networks?

Systems biology offers powerful frameworks for understanding how ytcA functions within the broader context of cellular networks:

Multi-omics Integration:

  • Combine transcriptomics, proteomics, and metabolomics data

  • Correlate ytcA expression/activity with global cellular changes

  • Identify emergent properties not visible at single-omics level

Network Modeling Approaches:

  • Construct gene regulatory networks including ytcA

  • Develop protein-protein interaction networks

  • Create metabolic models incorporating ytcA's potential effects

Dynamic Analysis:

  • Time-course studies to capture system evolution

  • Perturbation response analysis

  • Identification of feedback and feedforward loops

Computational Framework:

Systems Biology ApproachApplication to ytcA ResearchExpected Insights
Flux Balance AnalysisModel metabolic impact of ytcAPredict growth phenotypes
Bayesian Network AnalysisInfer causal relationshipsIdentify regulatory hierarchy
Agent-Based ModelingSimulate cell population effectsUnderstand emergent behaviors
Constraint-Based ModelingPredict system behavior under constraintsIdentify essential interactions

Experimental Validation of Models:

  • Design targeted experiments to test model predictions

  • Iteratively refine models based on new data

  • Use model predictions to guide engineering applications

Systems biology approaches are particularly valuable for uncharacterized proteins like ytcA because they can reveal functional roles that may not be apparent from reductionist approaches alone .

What evolutionary approaches can provide insights into ytcA function across bacterial species?

Evolutionary analysis provides valuable context for understanding ytcA function:

Phylogenetic Analysis Framework:

  • Construct phylogenetic trees of ytcA homologs

  • Map sequence conservation patterns

  • Identify co-evolution with interaction partners

Comparative Genomics Approaches:

  • Analyze gene neighborhood conservation

  • Identify synteny patterns across species

  • Examine correlation between ytcA presence and specific phenotypes

Evolutionary Rate Analysis:

  • Calculate dN/dS ratios to assess selection pressure

  • Identify rapidly evolving regions versus conserved domains

  • Infer functional constraints from evolutionary patterns

Implementation Strategy:

  • Homolog Identification:

    • BLAST searches against bacterial genomes

    • Profile HMM searches for distant homologs

    • Classification of orthologs versus paralogs

  • Sequence Conservation Analysis:

    • Multiple sequence alignment of homologs

    • Identification of conserved residues and motifs

    • Mapping conservation onto predicted structures

  • Functional Inference:

    • Correlation of ytcA presence with ecological niches

    • Association with specific metabolic capabilities

    • Identification of co-evolving gene clusters

By examining ytcA within its evolutionary context, researchers can gain insights into its fundamental function and how it may have been adapted for species-specific roles .

What are the most promising novel methodologies for characterizing proteins like ytcA?

Emerging technologies offer new approaches for characterizing uncharacterized proteins:

Advanced Structural Biology Methods:

  • Cryo-electron tomography for in situ structural analysis

  • Hydrogen-deuterium exchange mass spectrometry for dynamics

  • Single-particle cryo-EM for challenging proteins

  • Integrative structural biology combining multiple data types

Functional Genomics Innovations:

  • CRISPR interference for precise transcriptional control

  • CRISPRi screens with pooled libraries for phenotyping

  • Ribosome profiling for translational analysis

  • Transposon sequencing for fitness contribution assessment

High-Resolution Interaction Mapping:

  • Proximity labeling (BioID, APEX) for in vivo interactomes

  • Cross-linking mass spectrometry for interaction interfaces

  • Single-molecule tracking for dynamic interactions

  • Protein complementation assays for conditional interactions

Emerging Methods Table:

Novel MethodologyApplication to ytcA ResearchTechnical Considerations
CRISPR Base EditingPrecise mutagenesis without DSBsPAM site availability
In-cell NMRStructural dynamics in native environmentSignal-to-noise challenges
Nanobody DevelopmentSpecific detection and perturbationRequires purified antigen
Deep Mutational ScanningComprehensive functional mappingHigh-throughput phenotyping needed

Implementation Strategy:

  • Evaluate which novel methodologies address specific knowledge gaps

  • Establish collaborations with specialized laboratories if needed

  • Develop pilot studies to assess feasibility

  • Integrate novel data with conventional approaches

These cutting-edge methods can provide unprecedented insights into ytcA function, particularly when conventional approaches have yielded limited information .

What are the key challenges remaining in ytcA characterization?

Despite advances in protein characterization methodologies, several challenges persist in fully understanding proteins like ytcA:

Technical Challenges:

  • Obtaining sufficient quantities of correctly folded protein

  • Identifying appropriate assay conditions for functional studies

  • Distinguishing direct from indirect effects in cellular studies

  • Resolving contradictory findings from different experimental approaches

Biological Complexities:

  • Potential condition-specific or transient functions

  • Redundancy and compensation within biological systems

  • Moonlighting functions across different cellular contexts

  • Post-translational modifications affecting activity

Knowledge Integration:

  • Connecting molecular function to cellular phenotypes

  • Placing ytcA within broader regulatory networks

  • Translating in vitro findings to in vivo relevance

  • Reconciling computational predictions with experimental data

Addressing these challenges requires integrated research strategies that combine multiple experimental approaches with computational analyses and careful data interpretation .

How might characterization of ytcA contribute to broader understanding of bacterial physiology?

The characterization of uncharacterized proteins like ytcA has implications beyond the specific protein:

Fundamental Knowledge Expansion:

  • Closing gaps in our understanding of core bacterial processes

  • Discovering novel regulatory mechanisms

  • Identifying new functional protein domains

  • Uncovering unexpected cellular functions

Systems-Level Insights:

  • Completing regulatory network models

  • Understanding cellular adaptation mechanisms

  • Elucidating coordination between metabolic and regulatory systems

  • Identifying new cellular stress responses

Translational Potential:

  • Discovering novel antimicrobial targets

  • Developing biotechnological applications

  • Enhancing metabolic engineering capabilities

  • Improving protein function prediction algorithms

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