Recombinant Human Uncharacterized protein C17orf109 (C17orf109)

What is the predicted structure and cellular localization of C17orf109?

C17orf109 belongs to the category of uncharacterized open reading frame (ORF) proteins from chromosome 17. While limited direct experimental data exists, computational prediction tools suggest potential structural motifs and cellular localization. Researchers should employ a combined approach of:

• Structure prediction using AlphaFold2 and Phyre2 for secondary structure identification

• Subcellular localization prediction using TargetP, MitoProt, and PSORT

• Homology modeling against characterized proteins

• Transmembrane domain prediction using TMHMM or similar algorithms

Validation of these predictions requires experimental verification through techniques such as immunofluorescence microscopy with specific antibodies and colocalization studies with organelle markers. Similar approaches with other uncharacterized proteins have revealed important functional insights, as demonstrated with C17orf80, which was confirmed to associate with mitochondrial nucleoids .

How should researchers design expression systems for recombinant C17orf109?

When expressing recombinant C17orf109, consider these methodological approaches:

• Vector Selection: The C17orf109 ORF vector commercially available contains the gene between AflII and EcoRV restriction sites . For optimal expression, researchers should:

  • Verify the absence of internal AflII and EcoRV sites in the insert

  • Consider PCR amplification with preferred restriction sites if internal sites exist

  • Evaluate expression vectors with appropriate promoters for mammalian, bacterial, or insect cell systems

• Tagging Strategies:

  • N-terminal vs. C-terminal tags (His, FLAG, Myc) based on predicted protein topology

  • Consider dual tagging systems for purification and detection

  • Evaluate tag interference with protein folding and function

• Expression Conditions:

  • Temperature optimization (typically 16-37°C)

  • Induction parameters (IPTG concentration, induction time)

  • Cell lysis conditions (detergent selection for membrane proteins)

The choice of expression system should be guided by the predicted properties of C17orf109 and experimental goals.

What techniques are most effective for detecting endogenous C17orf109 expression?

Detection of endogenous uncharacterized proteins requires careful methodological consideration:

Researchers should validate antibody specificity through siRNA-mediated depletion, similar to approaches used for C17orf80 . For immunofluorescence studies, compare endogenous staining patterns with those of tagged recombinant protein to confirm specificity.

How can researchers determine potential interaction partners of C17orf109?

Identifying protein-protein interactions provides critical insights into function. For uncharacterized proteins like C17orf109, employ these methodological approaches:

• Proximity-Based Methods:

  • BioID or TurboID fusion proteins to identify proximal proteins

  • APEX2 labeling for temporal interaction dynamics

  • Validate interactions with reciprocal pulldowns

• Affinity Purification Coupled with Mass Spectrometry:

  • Optimize lysis conditions to preserve interactions

  • Include appropriate controls (tag-only, unrelated protein)

  • Apply statistical analysis to distinguish specific from non-specific interactions

• Yeast Two-Hybrid Screening:

  • Consider both N-terminal and C-terminal fusion constructs

  • Implement stringent selection conditions to reduce false positives

  • Validate hits with orthogonal methods

For uncharacterized proteins, proximity labeling has proven particularly valuable, as demonstrated with C17orf80, which was discovered near nucleoid components through this approach .

What are the recommended approaches for functional characterization of C17orf109?

Functional characterization requires multiple complementary approaches:

• Gene Perturbation Strategies:

  • CRISPR-Cas9 knockout generation

  • Inducible knockdown systems (shRNA, siRNA)

  • Rescue experiments with wild-type and mutant constructs

• Phenotypic Analysis:

  • Cell viability and proliferation assays

  • Morphological assessment through microscopy

  • Organelle-specific functional assays based on localization predictions

• Omics Integration:

  • Transcriptomics before and after perturbation

  • Proteomics to assess changes in interactome

  • Metabolomics if metabolic functions are suspected

Researchers should design comprehensive assays based on predictions and preliminary data. For example, if computational analysis suggests mitochondrial localization (similar to C17orf80), mitochondrial function assays would be appropriate .

How should researchers approach evolutionary conservation analysis of C17orf109?

Evolutionary conservation analysis provides insights into functional importance:

• Homology Identification:

  • BLAST searches against multiple genome databases

  • PSI-BLAST for distant homology detection

  • HMM-based searches for remote homologs

• Conservation Analysis:

  • Multiple sequence alignment of orthologs using MUSCLE or CLUSTALΩ

  • Identification of conserved motifs using MEME

  • Calculation of conservation scores using ConSurf

• Structural Conservation:

  • Compare predicted structures of orthologs

  • Identify conserved surface patches potentially involved in interactions

  • Map conservation onto structural models

For uncharacterized proteins, conservation analysis is particularly valuable, as demonstrated with C17orf80, where conserved cysteine and histidine residues provided insights into potential functional domains .

How should researchers design experiments to determine if C17orf109 associates with specific subcellular structures?

Determining subcellular associations requires methodical experimental design:

• Colocalization Studies:

  • Immunofluorescence with known organelle markers

  • Live-cell imaging with fluorescent protein fusions

  • Super-resolution microscopy for detailed spatial relationships

  • Quantitative colocalization analysis using Manders' coefficients

• Biochemical Fractionation:

  • Differential centrifugation protocols

  • Density gradient separation

  • Western blot analysis of fractions with organelle markers

  • Protease protection assays for membrane topology

• Proximity Labeling:

  • BioID fusions targeted to specific organelles

  • APEX2-mediated biotinylation

  • Mass spectrometry analysis of labeled proteins

For membrane-associated proteins, antibody accessibility assays with selective membrane permeabilization (using digitonin and Triton X-100) can determine which side of the membrane the protein domains face, as demonstrated with C17orf80 .

What controls are essential when studying the effects of C17orf109 depletion?

Effective depletion studies require rigorous controls:

• Depletion Validation:

  • Confirmation at both mRNA (qRT-PCR) and protein (Western blot) levels

  • Multiple siRNA/shRNA sequences to rule out off-target effects

  • Time-course analysis to determine optimal depletion conditions

• Phenotypic Controls:

  • Empty vector controls for CRISPR experiments

  • Non-targeting siRNA controls

  • Rescue experiments with siRNA-resistant constructs

  • Wild-type and mutant rescue constructs to identify critical domains

• Dosage Considerations:

  • Partial vs. complete knockdown phenotypes

  • Inducible systems for temporal control

  • Clonal variation analysis in stable cell lines

The effects of depletion should be assessed across multiple cell types and under various stress conditions to uncover context-dependent functions.

How can researchers determine if C17orf109 is subject to post-translational modifications?

Post-translational modifications (PTMs) can significantly impact protein function:

• Global PTM Analysis:

  • Mass spectrometry-based phosphoproteomics

  • Enrichment strategies for specific modifications (phospho, ubiquitin, SUMO)

  • Site-specific mutational analysis

• Targeted PTM Detection:

  • Western blotting with modification-specific antibodies

  • Phos-tag SDS-PAGE for phosphorylation detection

  • Mobility shift assays with and without phosphatase treatment

• PTM Dynamics:

  • Time-course analysis following stimulation

  • Inhibitor studies to identify responsible enzymes

  • In vitro modification assays with purified components

For uncharacterized proteins, identifying PTMs can provide critical clues about regulatory mechanisms and integration into cellular signaling networks.

How should researchers interpret contradictory localization data for C17orf109?

Contradictory localization data is common for proteins with multiple isoforms or dynamic localization:

• Resolution Strategies:

  • Isoform-specific analysis using splice variant-specific antibodies or constructs

  • Cell cycle synchronization to detect temporal variations

  • Stress condition testing to identify conditional localization

  • Single-cell analysis to detect population heterogeneity

• Technical Considerations:

  • Fixation artifact assessment using multiple fixation methods

  • Live-cell imaging to avoid fixation artifacts

  • Validation with biochemical fractionation

  • Tag interference evaluation using differently tagged constructs

• Biological Interpretation:

  • Shuttling protein hypothesis testing

  • Multi-compartment function evaluation

  • Stress-induced relocalization assessment

When analyzing contradictory data, consider that proteins may have punctate distribution patterns that partially overlap with organelle markers, as observed with C17orf80 and mitochondrial nucleoids .

What bioinformatic approaches can predict functions of C17orf109?

Computational prediction can guide experimental design:

• Sequence-Based Prediction:

  • Conserved domain identification using InterPro, PFAM

  • Motif scanning for functional sites (ELM, ScanProsite)

  • Secondary structure prediction (PSIPRED, JPred)

  • Disorder prediction (PONDR, IUPred)

• Structure-Based Approaches:

  • AlphaFold2 predictions for tertiary structure

  • Structural alignment against PDB database

  • Active site prediction based on structural features

  • Protein-protein interaction surface prediction

• Network-Based Methods:

  • Guilt-by-association in protein-protein interaction networks

  • Co-expression analysis across tissues and conditions

  • Pathway enrichment of predicted interactors

  • Phylogenetic profiling for functional inference

For uncharacterized proteins, combining multiple computational approaches increases prediction confidence. C17orf80's predicted structural features, including homology to ATP synthase subunit f, provided initial functional hypotheses .

How can researchers determine if C17orf109 has tissue-specific expression or functions?

Tissue specificity analysis requires integrative approaches:

• Expression Profiling:

  • Analysis of public RNA-seq databases (GTEx, Human Protein Atlas)

  • Tissue microarray immunohistochemistry

  • qRT-PCR panel across tissue samples

  • Western blot analysis of tissue lysates

• Functional Assessment:

  • Cell type-specific knockout models

  • Tissue-specific conditional knockout animals

  • Organoid models for tissue-specific function

  • Patient-derived cells for disease relevance

• Regulatory Analysis:

  • Promoter characterization in different cell types

  • Enhancer identification through ATAC-seq

  • Transcription factor binding site prediction and validation

  • Epigenetic regulation assessment through ChIP-seq

Public databases like the Human Protein Atlas provide valuable starting points for tissue expression patterns, as noted for C17orf80, which shows ubiquitous expression with highest levels in testes during spermatogenesis .

What are the current limitations in C17orf109 research?

Current limitations in studying uncharacterized proteins like C17orf109 include:

• Antibody Specificity Challenges:

  • Limited commercial antibodies with validated specificity

  • Difficult validation due to unknown expression patterns

  • Background signals in immunofluorescence studies

• Functional Inference Obstacles:

  • Absence of obvious structural homologs

  • Limited conservation data for evolutionary inference

  • Potential redundancy masking knockout phenotypes

  • Context-dependent functions requiring specific conditions

• Technical Challenges:

  • Protein expression and purification difficulties

  • Limited structural information

  • Unknown post-translational modifications

  • Potential for dynamic or condition-specific interactions

Researchers should address these limitations through rigorous validation, multiple methodological approaches, and carefully designed controls, as demonstrated in similar studies of uncharacterized proteins like C17orf80 .

What future research directions should be prioritized for C17orf109?

Priority research directions include:

• Comprehensive Characterization:

  • Generation of knockout cell lines and animal models

  • High-resolution structural determination

  • Complete interactome mapping under various conditions

  • PTM profiling and functional significance

• Disease Relevance Investigation:

  • Association studies with human diseases

  • Examination in pathological samples

  • Potential as biomarker or therapeutic target

  • Genetic variation impact assessment

• Integrative Approaches:

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

  • Systems biology modeling

  • Evolutionary analysis across species

  • Structural biology combined with functional assays