Recombinant Uncharacterized protein ZK1098.9 (ZK1098.9)

What is the current annotation status of ZK1098.9 protein in C. elegans?

ZK1098.9 is currently classified as an uncharacterized protein in C. elegans, meaning its function has not been fully determined through experimental validation. Like many uncharacterized proteins, it has been identified through genomic sequencing but lacks functional and structural characterization. The annotation of uncharacterized proteins is an ongoing process that evolves as new genomic information becomes available .

What computational approaches can be used for initial functional prediction of ZK1098.9?

For initial functional prediction of ZK1098.9, researchers should employ a multi-tool bioinformatic approach that includes:

• Physicochemical parameter prediction

• Domain and motif searches

• Pattern recognition analysis

• Subcellular localization prediction

This approach typically achieves approximately 83% accuracy in parameter prediction based on receiver operating characteristics (ROC) analysis . When analyzing ZK1098.9, a combination of these computational methods should be used rather than relying on a single prediction tool.

How can I express recombinant ZK1098.9 protein for laboratory studies?

Expression of recombinant ZK1098.9 can be accomplished using several expression systems, with E. coli being the most common for initial studies. The methodology involves:

• Gene synthesis or PCR amplification of the ZK1098.9 coding sequence

• Cloning into an appropriate expression vector with a purification tag

• Transformation into expression host cells (bacterial, yeast, baculovirus, or mammalian)

• Induction of protein expression under optimized conditions

• Purification using affinity chromatography based on the fusion tag

The choice of expression system should be determined by the experimental requirements, with bacterial systems offering simplicity and higher yields, while eukaryotic systems may provide better post-translational modifications .

What experimental research design is most appropriate for characterizing the function of ZK1098.9?

Characterizing ZK1098.9 requires a structured experimental research design framework that employs two sets of variables:

• Constant variables: Wild-type C. elegans strains and standardized growth conditions

• Experimental variables: ZK1098.9 knockout/knockdown strains, overexpression strains, or point mutations

This design is appropriate when:

• Time is an important factor in establishing cause-effect relationships

• There is potentially invariable behavior between cause and effect

• The researcher aims to understand the importance of the cause-effect relationship

The experimental design should include proper controls, replication, and randomization to ensure statistical validity and reliability of results.

How should I design protein interaction studies to identify potential binding partners of ZK1098.9?

To identify potential binding partners of ZK1098.9, a comprehensive protein interaction experimental design should include:

• Yeast two-hybrid screening: Primary method to identify potential protein-protein interactions

• Co-immunoprecipitation: Verification of interactions in physiologically relevant conditions

• Bimolecular fluorescence complementation: In vivo visualization of protein interactions

• Protein microarrays: High-throughput screening of multiple potential interactions

Statistical analysis of interaction data should include:

These methods should be used complementarily, as each has distinct advantages and limitations for detecting different types of protein interactions .

What controls are essential when performing recombination repair assays with ZK1098.9?

When investigating potential roles of ZK1098.9 in recombination repair, essential controls include:

• Positive controls: Known recombination repair proteins (e.g., homologs of Rhp51 or Swi5)

• Negative controls: Unrelated proteins with similar size/structure to ZK1098.9

• Genetic background controls: Isogenic strains without the modification of interest

• Cross-species controls: Testing functional complementation with homologs from related species

The experimental design should incorporate analysis of protein interactions with known recombination factors, as protein interactions with both Swi5 and Rhp51 (Rad51 homolog) are often mediated by common domains in recombination repair proteins .

How can I perform functional annotation of ZK1098.9 using contemporary bioinformatics approaches?

Functional annotation of ZK1098.9 requires a comprehensive bioinformatics pipeline that combines multiple prediction tools:

• Sequence-based annotation:

  • Homology searches against characterized proteins

  • Identification of conserved domains and motifs

  • Analysis of sequence patterns and signatures

• Structure-based annotation:

  • Homology-based structure prediction using Swiss-PDB and Phyre2 servers

  • Fold recognition and threading approaches

  • Molecular dynamics simulations to identify functional regions

• Interaction-based annotation:

  • String analysis to predict protein-protein interactions

  • Pathway enrichment analysis

  • Gene ontology term mapping

The efficacy of these databases for different parameter prediction typically ranges around 83.6% based on receiver operating characteristics analyses .

What approaches can resolve contradictory data between computational predictions and experimental results for ZK1098.9?

Resolving contradictions between computational predictions and experimental results for ZK1098.9 requires a systematic approach:

• Re-evaluation of computational models:

  • Assess the confidence scores of predictions

  • Compare results across multiple prediction algorithms

  • Consider evolutionary conservation patterns across species

• Experimental validation refinement:

  • Increase biological and technical replicates

  • Use orthogonal experimental approaches

  • Control for post-translational modifications

• Integrated analysis:

  • Employ Bayesian integration of computational and experimental data

  • Weight evidence based on methodological reliability

  • Consider functional context and biological networks

The resolution approach should be documented in a decision matrix:

This structured approach helps prioritize further investigations based on confidence levels in existing data .

How can CRISPR-Cas9 techniques be optimized for studying ZK1098.9 function in C. elegans?

Optimizing CRISPR-Cas9 for ZK1098.9 functional studies requires specific considerations for C. elegans experimental design:

• Guide RNA design:

  • Select target sites with minimal off-target effects

  • Consider chromatin accessibility at the ZK1098.9 locus

  • Design multiple gRNAs targeting different exons

• Delivery optimization:

  • Microinjection into the gonad with optimized concentrations

  • Co-CRISPR strategy using visible phenotypic markers

  • Temperature optimization for Cas9 activity

• Validation strategy:

  • PCR and sequencing verification of edits

  • Protein expression analysis

  • Functional assays based on predicted protein roles

• Phenotypic analysis pipeline:

  • Developmental timing assessment

  • Brood size quantification

  • Lifespan analysis

  • Stress response assays

This methodology allows for precise genetic manipulation to study ZK1098.9 function while controlling for potential confounding factors in the experimental design .

What are the recommended approaches for determining if ZK1098.9 has enzymatic activity?

To determine potential enzymatic activity of ZK1098.9, a systematic approach should include:

• Computational prediction:

  • Analyze for catalytic motifs and active site residues

  • Compare with characterized enzyme families

  • Perform structural analysis of potential active sites

• In vitro enzymatic assays:

  • Design substrate screening based on predicted function

  • Optimize assay conditions (pH, temperature, cofactors)

  • Measure kinetic parameters (Km, Vmax, kcat)

• Mutational analysis:

  • Generate point mutations of predicted catalytic residues

  • Assess activity changes using structure-function relationships

  • Perform complementation studies in mutant strains

The experimental approach should be guided by the fact that approximately 37% of uncharacterized proteins in similar organisms are ultimately identified as enzymes .

How can I assess whether ZK1098.9 is involved in DNA recombination repair pathways?

To assess ZK1098.9's potential involvement in DNA recombination repair pathways:

• Genetic epistasis analysis:

  • Create double mutants with known recombination genes

  • Assess synthetic lethality or rescue phenotypes

  • Compare with established pathways (e.g., Swi5-Sfr1-Rhp51)

• DNA damage sensitivity assays:

  • Expose ZK1098.9 mutants to various DNA damaging agents

  • Quantify survival rates compared to wild type

  • Analyze dose-response relationships

• Recombination frequency measurement:

  • Employ reporter systems to measure homologous recombination rates

  • Assess both spontaneous and induced recombination events

  • Compare with recombination rates in established repair mutants

• Protein complex identification:

  • Perform co-immunoprecipitation with known repair factors

  • Identify interaction domains using truncation mutants

  • Assess whether interactions are DNA damage-dependent

This approach is based on established methodologies used for characterizing proteins involved in Rhp51 (Rad51sp)-dependent recombination repair pathways .

What techniques can determine if ZK1098.9 functions as part of a protein complex?

To determine whether ZK1098.9 functions as part of a protein complex:

• Size exclusion chromatography:

  • Analyze native molecular weight compared to monomeric prediction

  • Identify co-eluting proteins via mass spectrometry

  • Compare elution profiles under different cellular conditions

• Blue native PAGE:

  • Preserve protein complexes during electrophoresis

  • Western blot detection of ZK1098.9 in complex bands

  • Excise and identify complex components via mass spectrometry

• Cross-linking mass spectrometry:

  • Use chemical cross-linkers to stabilize transient interactions

  • Identify cross-linked peptides via tandem mass spectrometry

  • Map interaction interfaces within complexes

• Fluorescence techniques:

  • FRET analysis to detect protein proximity in vivo

  • Fluorescence correlation spectroscopy to measure complex dynamics

  • Single-molecule tracking to assess complex formation

These methodologies are particularly relevant given that uncharacterized proteins often function within larger complexes, as demonstrated by studies of recombination repair proteins that form distinct functional complexes with different partner proteins .

How should RNA sequencing data be analyzed to understand ZK1098.9 knockout effects?

Analysis of RNA-seq data from ZK1098.9 knockout experiments should follow this methodological framework:

• Quality control and preprocessing:

  • Assess read quality (FastQC)

  • Trim adapters and low-quality bases

  • Filter ribosomal RNA contamination

• Alignment and quantification:

  • Map reads to C. elegans reference genome

  • Quantify gene expression (FPKM/TPM)

  • Perform transcript-level analysis

• Differential expression analysis:

  • Compare knockout vs. wild-type samples

  • Apply appropriate statistical methods (DESeq2, edgeR)

  • Control for false discovery rate (FDR ≤ 0.05)

• Functional interpretation:

  • Perform Gene Ontology enrichment analysis

  • Conduct pathway analysis (KEGG, Reactome)

  • Analyze protein interaction networks

• Validation strategy:

  • Select genes for qRT-PCR validation

  • Correlate RNA-seq and qRT-PCR results

  • Assess protein-level changes for key findings

This analytical pipeline helps identify biological processes affected by ZK1098.9 knockout, providing insights into its potential functions and regulatory roles .

What statistical approaches are most appropriate for analyzing ZK1098.9 phenotypic data across development?

For analyzing phenotypic data related to ZK1098.9 across C. elegans development:

• Longitudinal data analysis:

  • Apply mixed-effects models for repeated measurements

  • Use time-series analysis for developmental progression

  • Employ growth curve modeling techniques

• Multivariate analysis:

  • Principal component analysis to identify major sources of variation

  • Canonical correlation analysis for multiple phenotype relationships

  • MANOVA for comparing multiple dependent variables

• Non-parametric approaches:

  • Kaplan-Meier analysis for developmental timing events

  • Cox proportional hazards models for time-to-event data

  • Permutation tests for phenotypic distributions

• Visualization techniques:

  • Developmental trajectory plots

  • Heat maps of phenotypic severity across stages

  • Multidimensional scaling of phenotypic relationships

Statistical power analysis should be conducted a priori to determine appropriate sample sizes, with typical experiments requiring 30-50 animals per condition for 80% power at α=0.05 to detect a 20% difference in developmental phenotypes .