Recombinant Human Uncharacterized protein C3orf17 (C3orf17)

What is Recombinant Human Uncharacterized protein C3orf17?

Recombinant Human Uncharacterized protein C3orf17 refers to the artificially produced form of a naturally occurring protein encoded by the C3orf17 gene located on chromosome 3 in humans. As an uncharacterized protein, its molecular function, biological processes, and cellular component associations remain largely unknown. The protein is classified as "uncharacterized" because it lacks comprehensive functional annotation in major protein databases. Recombinant versions are produced in expression systems (commonly E. coli, mammalian cells, or insect cells) to enable functional studies, structural analysis, and antibody production. Similar to other uncharacterized proteins described in the research literature, C3orf17 likely has specific biological functions that can be elucidated through systematic investigation using modern genomic and proteomic approaches.

What approaches can be used to predict the function of uncharacterized proteins like C3orf17?

Multiple complementary approaches can be employed to predict the function of uncharacterized proteins like C3orf17:

• Co-essentiality mapping: This approach measures gene essentiality across diverse cell lines and identifies functional relationships by correlating phenotypic profiles. As demonstrated in recent research, co-essentiality mapping can identify genes that function in the same pathway or complex, even when they lack obvious sequence similarity .

• Sequence-based prediction: Analysis of protein domains, motifs, and sequence homology can provide initial clues about function. Even distant homology to characterized proteins may suggest functional categories.

• Structural prediction: Using AlphaFold or similar tools to predict protein structure can provide insights into potential binding sites, catalytic domains, or structural similarities to known proteins.

• Co-expression analysis: Although shown to have complementary strengths to co-essentiality mapping, co-expression can identify genes that are transcriptionally co-regulated, potentially indicating functional relationships .

• Evolutionary conservation analysis: Examining conservation patterns across species can highlight functionally important regions of the protein.

The selection of prediction methods should be guided by available data. For instance, the research described in search result successfully used co-essential modules to predict the functions of 108 previously uncharacterized genes.

How can I determine the subcellular localization of C3orf17?

Determining the subcellular localization of an uncharacterized protein like C3orf17 is a crucial step in understanding its function. Several methodological approaches can be employed:

• Immunofluorescence microscopy: This technique allows visualization of the protein within cellular compartments using specific antibodies against C3orf17 or against tags fused to the recombinant protein. Co-staining with known organelle markers can confirm specific localizations, as demonstrated in the study of C17orf80, where this approach revealed mitochondrial localization .

• Subcellular fractionation and Western blotting: By separating cellular components (nucleus, mitochondria, cytosol, etc.) through differential centrifugation and detecting the protein in these fractions via Western blotting, you can biochemically confirm localization results.

• Fluorescent protein fusion: Creating C3orf17 fusions with fluorescent proteins (GFP, mCherry, etc.) enables live-cell imaging of the protein's localization and dynamics.

• Proximity labeling approaches: Methods like BioID or APEX can identify proteins in close proximity to C3orf17, providing contextual information about its localization and potential interacting partners. This approach was successfully used to initially identify C17orf80 as a protein proximal to mitochondrial nucleoid components .

• Computational prediction: Tools like DeepLoc, TargetP, and PSORT can predict subcellular localization based on sequence features, providing hypotheses for experimental validation.

For comprehensive characterization, it is recommended to combine at least two independent techniques to confirm localization findings.

What expression patterns does C3orf17 show across different tissues?

Understanding the expression pattern of C3orf17 across different tissues can provide valuable insights into its potential tissue-specific functions. To characterize these patterns:

• Public database mining: Analyze expression data from repositories such as GTEx, Human Protein Atlas, or Expression Atlas to determine baseline expression across tissues.

• qRT-PCR analysis: Perform quantitative RT-PCR across a panel of human tissues or cell lines to independently verify expression patterns.

• Western blot analysis: Examine protein levels across tissue samples using validated antibodies against C3orf17.

• Single-cell RNA sequencing data: Analyze scRNA-seq datasets to understand cell type-specific expression within tissues.

• Cell line dependency patterns: As observed with other uncharacterized proteins, C3orf17 may show differential essentiality across cancer cell lines. For example, the study of TMEM189 revealed its particular essentiality in hematological cancer cell lines, which provided clues about its function .

When interpreting expression data, consider correlations with known pathways or processes, as co-expression patterns may suggest functional relationships. Tissue-specific expression may indicate specialized functions in particular organs or cell types.

How can co-essentiality mapping be applied to predict the function of C3orf17?

Co-essentiality mapping is a powerful approach for predicting the function of uncharacterized proteins like C3orf17. The methodology involves:

• Data acquisition and preprocessing: Analyze CRISPR-Cas9 screening data across diverse cell lines, such as the DepMap project, which contains gene dependency scores for hundreds of cancer cell lines.

• Statistical modeling: Apply generalized least squares (GLS) or similar statistical frameworks to calculate correlations between gene dependencies while accounting for confounding factors. As demonstrated in research on other uncharacterized proteins, GLS outperforms standard Pearson correlation in detecting genuine functional relationships .

• Network analysis and module detection: Identify co-essential modules – groups of genes with similar essentiality patterns across cell lines. In the study described in search result , researchers detected 93,575 significant co-essential gene pairs and organized them into functional modules using community detection algorithms.

• Functional enrichment analysis: For modules containing C3orf17, perform Gene Ontology enrichment analysis to predict the biological processes, molecular functions, or cellular components associated with the protein.

• Validation priority: Prioritize functional predictions based on module enrichment scores. In previous research, modules with at least 100-fold enrichment for specific GO terms provided reliable functional predictions for uncharacterized genes .

This approach has successfully assigned putative functions to 108 previously uncharacterized genes, including identifying TMEM189 as a key enzyme in plasmalogen synthesis and C15orf57 as having a role in clathrin-mediated endocytosis .

When applying this method to C3orf17, it's important to note that co-essentiality can detect functional relationships even for genes with modest effects on cell viability, as 70% of the 10% least essential genes still had detectable co-essential partners .

What bioinformatic tools are most effective for analyzing uncharacterized proteins like C3orf17?

Several bioinformatic tools can be particularly effective for analyzing uncharacterized proteins like C3orf17:

• Sequence analysis tools:

  • InterProScan for domain and motif identification

  • HHpred for remote homology detection

  • SignalP and TMHMM for signal peptide and transmembrane domain prediction

  • NetPhos for phosphorylation site prediction

• Structural prediction tools:

  • AlphaFold2 for accurate 3D structure prediction

  • RoseTTAFold for alternative structural models

  • FoldSeek for structural similarity searches

• Functional prediction tools:

  • GESECA or DepMap Portal for co-essentiality analysis

  • STRING for protein-protein interaction network analysis

  • DAVID or g:Profiler for functional annotation

  • NetGO for Gene Ontology prediction

• Evolutionary analysis tools:

  • MAFFT for multiple sequence alignment

  • ConSurf for evolutionary conservation mapping

  • PAML for positive selection analysis

• Integrative platforms:

  • Cytoscape for network visualization and analysis

  • Interactive co-essentiality webtools, similar to those described in search result

When analyzing C3orf17, it is advisable to employ multiple complementary tools and to critically evaluate predictions by considering their statistical confidence and biological plausibility. As demonstrated in the research on gene co-essentiality, integrating multiple sources of evidence can significantly improve functional predictions for uncharacterized proteins .

How should I design validation experiments to confirm predicted functions of C3orf17?

Designing robust validation experiments is crucial for confirming predicted functions of uncharacterized proteins like C3orf17. Based on successful approaches described in the research literature:

• Knockout/knockdown studies:

  • Generate CRISPR-Cas9 knockout or siRNA knockdown models

  • Perform phenotypic assays relevant to the predicted function

  • Compare essentiality patterns across different cell lines, as different genetic backgrounds may reveal function-specific dependencies

• Protein localization and interaction studies:

  • Conduct immunofluorescence microscopy with organelle-specific markers

  • Perform co-immunoprecipitation or proximity labeling (BioID/APEX) to identify interacting partners

  • Use fluorescence resonance energy transfer (FRET) to confirm direct interactions

  • These approaches were successfully used to characterize C17orf80 as a mitochondrial protein

• Biochemical assays:

  • Design assays specific to the predicted function (enzymatic activity, binding assays, etc.)

  • Use recombinant protein to test hypothesized activities in vitro

  • The validation of TMEM189 as plasmanylethanolamine desaturase demonstrates how biochemical assays can confirm predicted enzymatic functions

• Rescue experiments:

  • Complement knockout/knockdown with wild-type or mutant versions of C3orf17

  • This approach can confirm specificity and identify functionally important domains or residues

When designing these experiments, it's important to include appropriate controls and to consider the cellular context most relevant to the predicted function of C3orf17. The validation strategy should be tailored to the specific functional hypothesis derived from bioinformatic analyses.

What are the challenges in determining protein-protein interactions for uncharacterized proteins like C3orf17?

Determining protein-protein interactions (PPIs) for uncharacterized proteins like C3orf17 presents several specific challenges:

Methodological solutions:

• Proximity labeling approaches: BioID or APEX2 fusion proteins can identify proximity partners in living cells, as demonstrated for C17orf80

• Affinity purification-mass spectrometry (AP-MS): Using tagged versions of C3orf17 to pull down interaction complexes

• Yeast two-hybrid screening: For detecting binary interactions with a library of potential partners

• Cross-linking mass spectrometry: To capture transient interactions by chemical cross-linking

• Co-essentiality analysis: To identify functionally related proteins that may physically interact

For uncharacterized proteins, integrating multiple complementary approaches is particularly important to build confidence in the identified interactions.

What are the optimal conditions for expressing recombinant C3orf17?

Determining optimal conditions for recombinant expression of an uncharacterized protein like C3orf17 requires systematic optimization. Here's a methodological approach:

• Expression system selection:

  • Bacterial systems (E. coli): Start with BL21(DE3) or Rosetta strains for simple setup and high yield

  • Insect cells (Sf9, High Five): Better for proteins requiring eukaryotic post-translational modifications

  • Mammalian cells (HEK293, CHO): Ideal for human proteins needing complex folding or modifications

  • Cell-free systems: For proteins toxic to host cells

• Construct design considerations:

  • Test multiple fusion tags (His, GST, MBP, SUMO) to improve solubility and purification

  • Consider codon optimization for the expression host

  • Include protease cleavage sites for tag removal

  • Design constructs with and without predicted signal peptides or transmembrane domains

• Expression condition optimization:

  • Temperature: Test standard (37°C) and lower temperatures (16-30°C) to improve folding

  • Induction parameters: Vary inducer concentration (IPTG for bacteria) and induction time

  • Media formulation: Try rich (LB, TB) and defined media with supplements

  • Co-expression with chaperones: For proteins with folding challenges

• Purification strategy:

  • Design multi-step purification schemes (affinity, ion exchange, size exclusion)

  • Optimize buffer conditions (pH, salt, additives) to maintain stability

  • Test detergents for membrane-associated proteins

  • Consider on-column refolding for proteins in inclusion bodies

• Quality control assessments:

  • SDS-PAGE and Western blot to verify expression and purity

  • Mass spectrometry to confirm protein identity

  • Circular dichroism to assess secondary structure

  • Dynamic light scattering to evaluate aggregation state

For C3orf17 specifically, without prior knowledge of its properties, it's advisable to test expression in multiple systems in parallel, starting with a mammalian system like HEK293T cells that provides a native-like environment for human proteins.

How can CRISPR-Cas9 technology be utilized to study C3orf17 function?

CRISPR-Cas9 technology offers versatile approaches for studying the function of uncharacterized proteins like C3orf17:

• Knockout studies for loss-of-function analysis:

  • Design multiple sgRNAs targeting early exons of C3orf17

  • Create complete knockout cell lines using CRISPR-Cas9

  • Perform comprehensive phenotypic assays to identify functional defects

  • Compare results across multiple cell types to identify context-dependent functions

  • This approach has been fundamental in generating the co-essentiality data described in search result

• Knockin strategies for protein characterization:

  • Create endogenously tagged versions (FLAG, HA, GFP) for localization and interaction studies

  • Generate point mutations to test the importance of specific residues

  • Introduce reporter genes to monitor expression patterns

• CRISPR interference (CRISPRi) and activation (CRISPRa):

  • Use catalytically inactive Cas9 (dCas9) fused to repressors or activators

  • Provides tunable and reversible modulation of C3orf17 expression

  • Useful for studying dosage-sensitive functions and temporal dynamics

• Genome-wide CRISPR screens to identify genetic interactions:

  • Perform screens in C3orf17 knockout background to identify synthetic lethal interactions

  • Identify suppressors or enhancers of C3orf17 loss-of-function phenotypes

  • This approach can place C3orf17 in functional networks and pathways

• Pooled CRISPR screens across diverse cell lines:

  • Generate dependency profiles across cell lines with different genetic backgrounds

  • Identify cell types where C3orf17 is essential, similar to the approach that revealed TMEM189's importance in hematological cancer cell lines

When designing CRISPR-Cas9 experiments for C3orf17, it is important to validate editing efficiency and specificity through sequencing, assess off-target effects, and include appropriate controls (non-targeting sgRNAs, wild-type rescue constructs).

What mass spectrometry approaches are most suitable for studying C3orf17?

Mass spectrometry (MS) offers powerful approaches for characterizing uncharacterized proteins like C3orf17. Here are the most suitable MS methodologies:

• Protein identification and characterization:

  • Bottom-up proteomics: Digest recombinant C3orf17 with trypsin and analyze resulting peptides by LC-MS/MS for sequence verification

  • Top-down proteomics: Analyze intact protein to identify post-translational modifications and proteoforms

  • Hydrogen-deuterium exchange MS (HDX-MS): Probe protein structure and dynamics in solution

• Post-translational modification mapping:

  • Phosphoproteomics: Enrich for phosphopeptides using TiO2 or IMAC before MS analysis

  • Glycoproteomics: Use lectins or hydrazide chemistry to capture glycopeptides

  • Ubiquitinomics: Enrich for ubiquitinated peptides using specific antibodies

• Protein-protein interaction analysis:

  • Affinity purification-MS (AP-MS): Use tagged C3orf17 to pull down interaction partners

  • BioID or APEX proximity labeling coupled with MS: Identify proteins in proximity to C3orf17, similar to the approach used for C17orf80

  • Cross-linking MS (XL-MS): Map specific interaction interfaces using chemical cross-linkers

• Functional proteomics:

  • Thermal proteome profiling (TPP): Detect changes in protein thermal stability upon ligand binding

  • Activity-based protein profiling (ABPP): If C3orf17 has enzymatic activity

  • Stable isotope labeling (SILAC, TMT, iTRAQ): For quantitative comparison of proteomes with and without C3orf17

For an uncharacterized protein like C3orf17, a staged approach is recommended: begin with basic characterization of the purified protein, proceed to PTM mapping, then employ interaction proteomics to place the protein in a functional context. Quantitative approaches comparing wild-type and knockout cells can reveal pathways affected by C3orf17, providing functional insights.

How should I interpret contradictory data regarding C3orf17 function?

Interpreting contradictory data about an uncharacterized protein like C3orf17 requires a systematic approach:

• Evaluate methodological differences:

  • Different experimental approaches may reveal distinct aspects of protein function

  • For example, co-essentiality and co-expression analyses sometimes yield different results for the same genes, as observed in the comparative analysis described in search result

  • Techniques with different sensitivities or specificities may produce apparently contradictory results

• Consider biological context:

  • Cell type-specific functions may explain different phenotypes across systems

  • Cellular conditions (stress, growth phase, etc.) may activate different protein functions

  • Genetic background effects may modulate protein function

• Assess protein interaction networks:

  • Proteins may have multiple distinct functions in different complexes

  • Negative correlations between genes sometimes indicate regulatory relationships rather than contradictions, as seen with TP53/MDM2 and other examples in the co-essentiality study

• Integrate multiple data types:

  • Weigh evidence based on methodology robustness

  • Look for consensus across independent approaches

  • Use orthogonal validation when possible

When interpreting contradictory data for C3orf17, maintain an open hypothesis framework that allows for multiple or context-dependent functions. Document contradictions systematically and design targeted experiments to resolve them rather than prematurely discarding seemingly inconsistent results.

What statistical methods are most appropriate for analyzing C3orf17 experimental data?

Selecting appropriate statistical methods for analyzing C3orf17 experimental data depends on the specific experimental design and data types. Here are methodological recommendations:

• For gene essentiality and co-essentiality analysis:

  • Generalized least squares (GLS): This approach outperforms standard correlation methods for detecting co-essential relationships by accounting for confounding factors, as demonstrated in research on other uncharacterized proteins

  • Principal component analysis (PCA): For bias correction in correlation analyses

  • Permutation tests: To establish significance thresholds for co-essentiality networks

• For differential expression/proteomics:

  • Limma: For microarray and RNA-seq differential expression analysis

  • DESeq2 or EdgeR: For RNA-seq count data

  • Mixed-effects models: When handling repeated measures or nested experimental designs

  • Multiple testing correction: Use Benjamini-Hochberg procedure to control false discovery rate

• For phenotypic assays:

  • ANOVA or Kruskal-Wallis: For comparing multiple experimental conditions

  • Linear or generalized linear models: To account for covariates

  • Survival analysis: For time-to-event data (e.g., cellular senescence)

• For interaction studies:

  • Significance Analysis of INTeractome (SAINT): For scoring protein-protein interactions from AP-MS data

  • MiST (Mass spectrometry interaction STatistics): Alternative scoring for interaction proteomics

  • Network analysis metrics: Betweenness centrality, clustering coefficient for interpreting interaction networks

For uncharacterized proteins like C3orf17, it's particularly important to control for false discoveries while maintaining sensitivity to detect subtle effects. The statistical approach should be tailored to the specific hypothesis being tested and should include appropriate visualization methods to communicate findings effectively.

How can I integrate multi-omics data to better understand C3orf17 function?

Integrating multi-omics data provides a powerful approach to understand the function of uncharacterized proteins like C3orf17. Here's a methodological framework:

• Data collection and preprocessing:

  • Genomics: Analyze C3orf17 genetic variants, structural variations, and conservation patterns

  • Transcriptomics: Examine expression patterns and co-expression networks across tissues

  • Proteomics: Study protein abundance, post-translational modifications, and interactions

  • Metabolomics: Identify metabolic changes upon C3orf17 perturbation

  • Functional genomics: Analyze essentiality and genetic interaction profiles

• Single-omics analysis:

  • Perform comprehensive analysis within each data type

  • Identify significant features (differentially expressed genes, protein interactions, etc.)

  • Generate initial hypotheses based on individual data types

• Pairwise integration approaches:

  • Correlation analysis: Between transcript and protein levels

  • Co-essentiality and co-expression comparison: As demonstrated in search result , these can provide complementary information

  • Protein-metabolite association: To identify potential enzymatic functions

• Multi-layer integration strategies:

  • Network fusion methods: Integrate multiple networks to create a unified representation

  • Multi-omics factor analysis (MOFA): Identify factors explaining variation across data types

  • Joint dimension reduction: Methods like DIABLO or JIVE for coordinated dimensionality reduction

When applying this approach to C3orf17, pay particular attention to patterns that emerge consistently across multiple data types, as these are more likely to represent genuine biological functions rather than technical artifacts. The integration of co-essentiality data with other omics data types has proven particularly valuable for uncharacterized proteins, as demonstrated by the successful functional prediction and validation of proteins like TMEM189 and C15orf57 .

What are the most promising research areas for uncharacterized proteins like C3orf17?

Several research areas show particular promise for advancing our understanding of uncharacterized proteins like C3orf17:

• Expanded co-essentiality mapping:

  • Extending CRISPR screening to more diverse cell types and conditions

  • Incorporating environmental or drug perturbations to reveal context-dependent functions

  • Developing improved statistical methods for detecting subtle functional relationships

  • This approach has already demonstrated success in characterizing over 100 previously uncharacterized genes

• Structural biology integration:

  • Leveraging AlphaFold2 and similar tools to predict protein structures

  • Using structure-based functional inference to generate testable hypotheses

  • Applying cryo-EM to characterize protein complexes containing uncharacterized proteins

• Single-cell functional genomics:

  • Applying CRISPR perturbations at single-cell resolution

  • Integrating with single-cell transcriptomics and proteomics

  • Identifying cell type-specific functions that may be masked in bulk analyses

• Comparative approaches across species:

  • Studying orthologs in model organisms with genetic tractability

  • Leveraging evolutionary signatures to identify functional constraints

  • Using cross-species complementation to validate functional conservation

• Development of targeted protein degradation tools:

  • Applying PROTAC or dTAG approaches for acute protein depletion

  • Studying temporal aspects of protein function not accessible through genetic knockout

For C3orf17 specifically, prioritization among these approaches should be guided by available preliminary data. The successful characterization of other uncharacterized proteins, such as TMEM189 as plasmanylethanolamine desaturase and C15orf57 as a regulator of clathrin-mediated endocytosis , demonstrates how integrated approaches can yield breakthrough discoveries about proteins of previously unknown function.

How might understanding C3orf17 function contribute to disease research?

Understanding the function of uncharacterized proteins like C3orf17 can significantly impact disease research in several ways:

• Identification of novel disease mechanisms:

  • Uncharacterized proteins may represent missing links in disease pathways

  • Similar to how TMEM189 was identified as a key enzyme in plasmalogen synthesis , C3orf17 might have enzymatic activity relevant to disease-associated metabolic pathways

  • Characterization could reveal unexpected connections between biological processes and disease

• Discovery of new therapeutic targets:

  • Novel protein functions provide opportunities for targeted drug development

  • If C3orf17 is involved in a disease-relevant pathway, it could represent an untapped target

  • Differential essentiality across cancer types, as observed for some uncharacterized proteins , may indicate potential for cancer-specific vulnerabilities

• Interpretation of genetic variants:

  • Many variants of unknown significance (VUS) occur in uncharacterized genes

  • Functional characterization would enable better interpretation of C3orf17 variants found in patients

  • Could explain currently unresolved genotype-phenotype relationships

• Biomarker development:

  • Knowledge of function could position C3orf17 as a diagnostic or prognostic biomarker

  • Expression patterns or post-translational modifications might correlate with disease states

  • Could improve stratification of patients for clinical trials or treatment selection

To specifically investigate C3orf17's potential disease relevance, researchers should analyze genetic association studies for links between C3orf17 variants and disease, examine differential expression patterns in disease databases, and investigate co-essentiality relationships with known disease genes.

What emerging technologies could accelerate functional characterization of C3orf17?

Several emerging technologies hold promise for accelerating the functional characterization of uncharacterized proteins like C3orf17:

• Advanced CRISPR technologies:

  • Base editing and prime editing: For precise introduction of specific mutations without double-strand breaks

  • CRISPR atlases: Systematic perturbation of non-coding regulatory elements affecting C3orf17

  • Perturb-seq/CROP-seq: Combining single-cell transcriptomics with CRISPR perturbation to capture molecular phenotypes at high resolution

  • CRISPR screens with single-cell readouts: To capture complex phenotypes beyond viability

• Spatial multi-omics:

  • Spatial transcriptomics: Map C3orf17 expression patterns with spatial context

  • Spatial proteomics: Visualize protein localization and interactions in situ

  • Multiplexed ion beam imaging (MIBI): For high-parameter protein imaging

  • These approaches provide crucial contextual information about protein function

• Protein structure and interaction technologies:

  • AlphaFold-Multimer and RoseTTAFold-Complex: For modeling protein complexes

  • Hydrogen-deuterium exchange mass spectrometry (HDX-MS): For studying protein dynamics and interactions

  • Cryo-electron tomography: For visualizing proteins in their cellular context

  • Cross-linking mass spectrometry: For mapping interaction interfaces in complex protein assemblies

By integrating these emerging technologies, researchers can accelerate the functional characterization of C3orf17 and other uncharacterized proteins, potentially uncovering novel biological insights and therapeutic opportunities. The most effective approach will likely combine multiple technologies tailored to the specific properties of C3orf17 as they are gradually revealed.