ERP44 Antibody

What is ERP44 and what are its key functional domains?

ERP44 (Endoplasmic Reticulum Protein 44), also known as TXNDC4 or PDIA10, belongs to the thioredoxin family (TRX). It mediates thiol-dependent retention in the early secretory pathway and plays a crucial role in protein quality control.

The protein contains:

• Domain a (light violet): Contains C29, essential for substrate binding

• Domain b (blue) and b' (light brown): Structural domains

• C-tail (green): Regulates substrate access to the active site

• RDEL motif: C-terminal ER-retention sequence

The conserved C29 residue in domain a forms mixed disulfides with substrate proteins through its CRFS motif, essential for its chaperone function . ERP44 has a calculated molecular weight of 47 kDa and observed molecular weight of 44-47 kDa .

What types of ERP44 antibodies are currently available for research applications?

Various ERP44 antibodies are available for research:

For specific research questions, antibody selection should be based on the intended application, required species reactivity, and whether monoclonal specificity or polyclonal coverage is preferred .

How should I optimize Western blot protocols when using ERP44 antibodies?

For optimal Western blot results with ERP44 antibodies:

• Sample preparation: Use appropriate lysis buffer (e.g., radioimmune precipitation assay buffer containing 25 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% sodium deoxycholate, 1% Nonidet P-40, 0.1% Triton X-100 plus protease inhibitors)

• Gel selection: Use 4-20% gradient SDS-PAGE for optimal resolution of ERP44 (44-47 kDa)

• Dilution ranges:

  • For monoclonal antibodies: 1:5000-1:50000

  • For polyclonal antibodies: 0.04-0.4 μg/mL

• Reducing vs. non-reducing conditions:

  • Use non-reducing conditions to preserve disulfide bonds when studying ERP44-substrate interactions

  • Use reducing conditions to analyze total ERP44 levels

• Negative controls: Include C29A mutant samples or ERP44 knockdown controls to verify specificity

• Detection systems: Use appropriate secondary antibodies based on the host species of your primary antibody

What is the recommended procedure for immunoprecipitation using ERP44 antibodies?

For successful immunoprecipitation of ERP44 and associated proteins:

• Cell lysis:

  • Wash cells with PBS buffer (137 mM NaCl, 2.7 mM KCl, 4.3 mM Na₂HPO₄, 1.47 mM KH₂PO₄, pH 7.4)

  • Lyse in radioimmune precipitation assay buffer with protease inhibitors

• Pre-clearing:

  • Use 100 μg of cell lysate

  • Pre-clear with protein G beads to reduce non-specific binding

• Immunoprecipitation:

  • Incubate pre-cleared lysate with ERP44 antibody (recommended: antibodies specifically designed for IP such as the Mouse Monoclonal Anti-TXNDC4 in antibody pair sets)

  • Add protein G beads and incubate overnight at 4°C with shaking

• Washing and elution:

  • Wash beads with radioimmune precipitation assay buffer three times

  • Elute complexes with non-reducing SDS-PAGE sample buffer if studying substrate interactions

• Analysis:

  • Analyze by Western blotting using a different ERP44 antibody (e.g., rabbit polyclonal) or antibodies against suspected interaction partners

For co-immunoprecipitation studies, consider using tagged versions of ERP44 (e.g., PDI-myc) if background issues occur with your antibody .

What are the best practices for immunohistochemistry with ERP44 antibodies?

For optimal immunohistochemical detection of ERP44:

• Antigen retrieval:

  • Primary method: TE buffer pH 9.0

  • Alternative method: citrate buffer pH 6.0

• Dilution ranges:

  • Typical range: 1:500-1:2000 for monoclonal antibodies

  • For polyclonal antibodies: 1:50-1:200

• Positive control tissues:

  • Human breast cancer tissue

  • Human liver cancer tissue

• Negative controls:

  • Omit primary antibody

  • Use isotype-matched control antibody

• Detection systems:

  • DAB (3,3′-diaminobenzidine) for brightfield microscopy

  • Fluorescent-labeled secondary antibodies for immunofluorescence

• Counterstaining:

  • Hematoxylin for nuclear visualization in brightfield

  • DAPI for nuclear visualization in fluorescence

Always validate staining patterns with known subcellular localization (primarily ER and early secretory pathway for ERP44) .

How does pH affect ERP44 function and substrate binding properties?

ERP44 activity is critically regulated by pH, making it a pH sensor in the early secretory pathway:

• pH-dependent substrate binding:

  • Lower pH (ER/ERGIC) promotes ERP44 binding to substrates through C29

  • Higher pH (Golgi) reduces ERP44's retentive activity

• Molecular mechanism:

  • In vitro experiments show increased MalPEG binding to C29 at lower pH

  • Mutations affecting the protonation state of C29 (S32A, R98Q, T369A) disrupt pH sensitivity

• Experimental evidence:

  • GPHR silencing (which basifies Golgi pH) decreases wild-type ERP44 binding to client proteins

  • Mutants lacking key pH-sensing residues bind clients in a pH-independent manner

• Functional consequence:

  • When Golgi pH increases, WT ERp44 shows reduced complex formation

  • pH insensitive mutants (S32A, R98Q, T369A) remain highly bound to clients regardless of pH changes

This pH sensitivity allows ERP44 to function as a sensor that selectively captures and retains clients in specific compartments along the secretory pathway, releasing them when appropriate conditions are met .

What is known about the interaction between ERP44 and adiponectin?

The interaction between ERP44 and adiponectin (APN) represents a critical quality control mechanism:

• Binding mechanism:

  • ERP44 forms mixed disulfides with adiponectin through its C29 residue

  • This interaction can be visualized using non-reducing SDS-PAGE

• Experimental approaches to study the interaction:

  • Co-immunoprecipitation: Using anti-adiponectin or anti-ERp44 antibodies

  • Far-Western blotting: Purified adiponectin separated by non-reducing SDS-PAGE, transferred to PVDF, then incubated with ERp44

  • Co-incubation experiments: Mixing purified adiponectin (1 μg) with ERp44 (10 μg) in different pH buffers

• pH dependence:

  • The ERp44-adiponectin interaction is stronger at pH 6.5 (ER/ERGIC-like) than at pH 8.0 (Golgi-like)

  • This pH dependence is reversible - adjusting pH from 6.5 to 8.0 releases adiponectin from ERp44

• Functional significance:

  • ERp44 retains incompletely assembled adiponectin in the early secretory pathway

  • Only properly assembled adiponectin oligomers are secreted

  • This quality control mechanism ensures that only functional adiponectin reaches circulation

• Structural analysis:

  • SEC-MALS (Size Exclusion Chromatography with Multi-Angle Light Scattering) and ESI-MS (Electrospray Ionization Mass Spectroscopy) can be used to analyze ERp44-adiponectin complexes

How does ERp44 participate in redox quality control in the early secretory pathway?

ERp44 functions as a key regulator in redox quality control through several mechanisms:

• Client protein retention:

  • Binds and retains substrates of thiol-mediated retention (TMR) including:

    • Unassembled IgM subunits

    • SUMF1

    • Adiponectin

    • Oxidases (Ero1α, Ero1β, Prx4)

• C-tail regulation mechanism:

  • The C-tail movements are crucial for simultaneously exposing:

    • The substrate binding site containing C29

    • The RDEL motif for KDEL-receptor binding

  • Formation of a disulfide between C29 and C369 in T369C mutant hinders both client access and RDEL exposure

• Compartment-specific interactions:

  • Different clients bind ERp44 in different compartments based on:

    • Affinity for ERp44 binding site

    • Environmental pH

    • Cofactor concentrations

  • This allows sequential localization of interactors along the secretory pathway

• Regulation by histidine residues:

  • Conserved histidines at the border between domain b' and C-tail regulate ERp44 function

  • Mutants lacking key histidines undergo O-glycosylation and are partially secreted

  • Client protein expression restores retention of these mutants, suggesting histidines regulate RDEL exposure in the absence of clients

• Physiological regulation:

  • ERp44 can undergo O-glycosylation under physiological conditions

  • Cyclic oscillation of O-glycosylated ERp44 occurs in stromal endometrial cells during the menstrual cycle

Why might I observe multiple bands when using ERP44 antibodies in Western blot?

Multiple bands in ERP44 Western blots can occur for several biological and technical reasons:

• Client protein interactions:

  • Under non-reducing conditions, ERP44 forms disulfide-linked complexes with client proteins

  • Solution: Compare reducing vs. non-reducing conditions - true complexes will disappear under reducing conditions

• ERP44 dimerization:

  • ERP44 can form dimers through disulfide bonds

  • Experimental verification: Purified ERp44 can be separated into monomeric and dimeric forms using size exclusion chromatography (Superdex-200)

• Post-translational modifications:

  • O-glycosylation: ERp44 can undergo O-glycosylation under certain conditions

  • Solution: Treat samples with glycosidases to confirm glycosylation-related shifts

• pH-dependent conformational changes:

  • ERp44 conformation and complex formation is pH-dependent

  • Experimental approach: Compare samples from cells with normal vs. basified Golgi pH (e.g., through GPHR silencing)

• Antibody cross-reactivity:

  • Some antibodies may cross-react with related thioredoxin family proteins

  • Control: Include ERP44 knockdown samples or use multiple antibodies targeting different epitopes

• Degradation products:

  • Proteolytic fragments during sample preparation

  • Solution: Add sufficient protease inhibitors to lysis buffer and avoid repeated freeze-thaw cycles

How can I determine if my ERP44 antibody is detecting the active form of the protein?

To verify detection of functionally active ERP44:

• C29 accessibility assays:

  • In vitro: MalPEG binding assay - MalPEG binds to accessible C29, causing reduced gel mobility

  • Compare binding at different pH values (e.g., pH 6.5 vs. pH 8.0)

• Functional assays:

  • Co-immunoprecipitation with known client proteins (Ero1α, adiponectin, etc.)

  • Client protein retention assay: Co-express ERP44 and client proteins, measure client secretion

• Conformational mutant controls:

  • Include samples expressing ERP44 mutants with known conformational states:

    • C29A mutant: Impairs covalent client binding

    • Δβ16 mutant: Exposes binding site, increases client binding

    • T369C mutant: Locks C-tail, reduces client binding

• pH manipulation experiments:

  • GPHR silencing: Basifies Golgi pH, should reduce wild-type ERP44 client binding

  • Compare wild-type vs. pH-insensitive mutants (S32A, R98Q, T369A)

• Far-Western blotting:

  • Separate purified client proteins by non-reducing SDS-PAGE

  • Transfer to membrane and incubate with purified ERP44

  • Detect bound ERP44 with anti-ERP44 antibody

This multi-faceted approach will confirm whether your antibody detects functionally active ERP44 capable of engaging in its normal binding interactions.

How can I design experiments to study the pH-dependent cycling of ERP44 between cellular compartments?

To investigate ERP44's pH-dependent cycling:

• Fluorescent tagging approaches:

  • Generate fluorescently tagged ERP44 constructs (C-terminal tag to avoid interfering with N-terminal signal sequence)

  • Co-localize with compartment markers (Calnexin for ER, ERGIC-53 for ERGIC, GM130 for Golgi)

  • Live-cell imaging to track movement between compartments

• pH manipulation strategies:

  • GPHR silencing: Basifies Golgi pH

  • Monensin/nigericin treatment: Disrupts pH gradients

  • Compare localization of wild-type vs. pH-insensitive mutants (S32A, R98Q, T369A)

• Glycosylation analysis:

  • Monitor O-glycosylation status as indicator of Golgi transit

  • Experimental approach: Compare wild-type ERP44 vs. histidine mutants (which undergo increased O-glycosylation)

  • Client co-expression experiments to assess rescue of mutant retention

• Pulse-chase experiments:

  • Radiolabel proteins and track ERP44 modifications over time

  • Combine with compartment-specific enzymes (EndoH for ER, O-glycosylation for Golgi)

• Proximity labeling approaches:

  • APEX2 or BioID fusions to ERp44

  • Compartment-specific labeling under different pH conditions

  • Mass spectrometry identification of proximity partners in each compartment

• Mutant analysis matrix:
  Design a comprehensive set of experiments comparing:

  ERP44 Variant	pH Condition	Client Expression	Readout
  Wild-type	Normal	-	Localization/Glycosylation
  Wild-type	Basified (GPHR KD)	-	Localization/Glycosylation
  pH-insensitive mutants	Normal	-	Localization/Glycosylation
  pH-insensitive mutants	Basified (GPHR KD)	-	Localization/Glycosylation
  Histidine mutants	Normal	-	Localization/Glycosylation
  Histidine mutants	Normal	+	Localization/Glycosylation

What experimental approaches can determine which domains of ERP44 are critical for specific client interactions?

To map the domain-specific interactions between ERP44 and its clients:

• Domain deletion/mutation analysis:

  • Generate a panel of ERP44 constructs with:

    • Domain deletions (Δa, Δb, Δb', ΔC-tail)

    • Point mutations of key residues (C29A, histidine mutations, S32A, R98Q, T369A)

    • C-tail mutants (Δβ16, T369C, ΔRDEL)

  • Assess each variant's ability to bind different clients

• Far-Western blotting with domain fragments:

  • Express and purify individual ERP44 domains

  • Use these in far-Western assays against immobilized client proteins

• Co-immunoprecipitation matrix:

  • Systematic co-IP experiments with:

    • Different ERP44 domain mutants

    • Different client proteins (Ero1α, adiponectin, SUMF1, IgM)

  • Analyze under reducing and non-reducing conditions

• SEC-MALS interaction studies:

  • Analyze binding of purified ERP44 domains to client protein oligomers

  • Determine stoichiometry and affinity of interactions

  • Compare at different pH values (6.5, 7.5, 8.0)

• ESI-MS disulfide mapping:

  • Incubate ERP44 with client peptides

  • Analyze formation of disulfide bonds between specific residues

  • Compare wild-type to mutant forms

• Peptide competition assays:

  • Design peptides corresponding to different ERP44 domains

  • Test their ability to compete with full-length ERP44 for client binding

  • Use both in vitro (purified proteins) and in vivo (cell-based) approaches

• Client-specific binding matrix:
  A comprehensive binding analysis might look like:

  ERP44 Variant	Ero1α Binding	Adiponectin Binding	IgM Binding	SUMF1 Binding
  Wild-type	+++	+++	+++	+++
  C29A	+	-	-	-
  Δa domain	-	-	-	-
  Δb domain	++	+++	+	++
  Δb' domain	+	++	+++	+
  ΔC-tail	++++	++++	++++	++++
  Δβ16	++++	++++	++++	++++
  T369C	-	-	-	-

This systematic approach will reveal which domains are universally required for all clients versus those with client-specific functions .

How can I investigate the physiological significance of ERP44 in specific tissues or disease models?

To study ERP44's physiological roles in tissue-specific contexts:

• Tissue-specific expression analysis:

  • Immunohistochemistry with validated ERP44 antibodies across tissue panels

  • RT-qPCR for ERP44 mRNA levels across tissues

  • Correlation with client protein expression (e.g., adiponectin in adipose tissue)

• Physiological cycling models:

  • Endometrial stromal cell models for menstrual cycle studies

  • Analyze O-glycosylated ERP44 levels across the cycle

• Genetic manipulation approaches:

  • Tissue-specific conditional knockout models

  • CRISPR/Cas9 editing to introduce specific mutations (C29A, pH-insensitive mutants)

  • Phenotypic analysis focusing on secretory pathway stress

• Stress response studies:

  • ER stress induction (tunicamycin, thapsigargin)

  • Oxidative stress (H₂O₂ treatment)

  • Monitor ERP44 levels, localization, and client interactions under stress conditions

• Disease-relevant cell models:

  • Plasma cells for antibody secretion disorders (IgM retention by ERP44)

  • Adipocytes for metabolic disease (adiponectin quality control)

  • Correlation with ER stress markers (BiP, XBP1 splicing)

• Client misfolding models:

  • Express mutant client proteins prone to misfolding

  • Assess ERP44's role in retention vs. degradation

  • Compare wild-type vs. ERP44-depleted conditions

• Redox homeostasis assessment:

  • Measure cellular redox state with redox-sensitive GFP probes

  • Compare wild-type vs. ERP44-depleted conditions

  • Assess impact on oxidative protein folding efficiency

• Analysis framework for tissue studies:

  Tissue/Cell Type	ERP44 Expression	O-glycosylation Status	Primary Client(s)	Key Phenotype in Depletion
  Plasma cells	High	Low	IgM	Secretion of unassembled IgM
  Adipocytes	Moderate	Variable	Adiponectin	Altered adiponectin oligomerization
  Endometrial stromal cells	Cyclical	Cyclical	Unknown	Menstrual irregularities
  Liver	High	Low	Secretory proteins	ER stress, steatosis

This comprehensive approach will illuminate ERP44's roles across tissues and in disease-relevant contexts .

How should differences in ERP44 antibody detection be interpreted across experimental platforms?

When comparing ERP44 data across different platforms:

• Western blot vs. immunohistochemistry discrepancies:

  • Western blot detects denatured protein; may access epitopes hidden in native conformation

  • IHC preserves spatial information but may lose some epitopes due to fixation

  • Solution: Use multiple antibodies targeting different epitopes across methods

• Molecular weight variations:

  • Expected range: 44-47 kDa

  • Variations may reflect:

    • Post-translational modifications (especially O-glycosylation)

    • Species-specific differences

    • Antibody-specific detection preferences

• Signal intensity differences between antibodies:

  • Monoclonal antibodies: Higher specificity but may miss isoforms/variants

  • Polyclonal antibodies: Broader detection but potential cross-reactivity

  • Consider epitope location relative to functional domains when interpreting results

• Cross-platform validation strategy:

  1. Start with Western blot to confirm molecular weight and specificity

  2. Validate with immunoprecipitation to confirm native protein recognition

  3. Confirm subcellular localization with IHC/IF

  4. Use functional assays (client binding) to confirm activity

• Interpretation matrix:

  Result Pattern	Likely Interpretation	Validation Approach
  Multiple bands in WB, single signal in IHC	Complexes or PTMs disrupted in IHC	Non-reducing WB, glycosidase treatment
  Single band in WB, heterogeneous signal in IHC	Microenvironment affects conformation	pH-dependent staining, co-staining with client proteins
  Strong signal with antibody A, weak with antibody B	Epitope accessibility differences	Use denaturing vs. native conditions
  Different MW across platforms	Platform-specific modifications	Immunoprecipitation followed by mass spectrometry

This systematic interpretation approach accounts for platform-specific factors affecting ERP44 detection .

How can I accurately quantify changes in ERP44-client protein interactions in response to experimental manipulations?

For rigorous quantification of ERP44-client interactions:

• Co-immunoprecipitation with quantification:

  • Perform co-IP using anti-ERP44 or anti-client antibodies

  • Quantify the ratio of pulled-down client to total immunoprecipitated ERP44

  • Compare across experimental conditions (pH changes, redox manipulation)

• Far-Western blotting with densitometry:

  • Quantify ERp44 binding to immobilized client proteins

  • Compare binding at different pH values or with different ERp44 mutants

  • Normalize to total protein loading

• In vitro binding assays with purified components:

  • SEC-MALS to determine binding stoichiometry and complex formation

  • ESI-MS to quantify disulfide bond formation between ERp44 and client peptides

  • Compare across different conditions (pH, redox state)

• Cellular retention assays:

  • Measure client protein secretion vs. intracellular retention

  • Calculate retention efficiency as ratio of intracellular/secreted client

  • Compare across ERP44 variants or experimental manipulations

• Compartment-specific interaction analysis:

  • Combine subcellular fractionation with co-IP

  • Quantify client-ERP44 interactions in ER vs. ERGIC vs. Golgi fractions

  • Correlate with compartment-specific pH or other properties

• Data visualization and statistical analysis:

  • Present interaction data as fold-change relative to control conditions

  • Include appropriate statistical tests (paired t-test for before/after manipulations)

  • Consider multivariate analysis for complex datasets with multiple clients/conditions

• Quantification framework:

  Measurement	Calculation Method	Normalization Approach	Statistical Analysis
  Co-IP efficiency	(Client/ERP44 ratio)	Normalize to total ERP44	Paired t-test
  Client retention	(Intracellular/secreted ratio)	Normalize to total client expression	ANOVA with post-hoc tests
  Complex formation	MW from SEC-MALS	Compare to theoretical MW	Non-linear regression
  Disulfide formation	% modified in ESI-MS	Compare to maximum possible	Chi-square test

This comprehensive approach provides robust quantification of interaction changes across experimental conditions .

What are the cutting-edge approaches for studying ERP44 interactions in living cells?

Current advanced approaches include:

• Proximity labeling technologies:

  • BioID or TurboID fusions to ERP44: Identify proteins in close proximity through biotinylation

  • APEX2-ERP44 fusions: Electron microscopy-compatible labeling of interaction environment

  • Split-BioID: Study client-specific interaction environments by fusing complementary fragments to ERP44 and clients

• Live-cell imaging technologies:

  • FRET sensors to monitor ERP44-client interactions in real-time

  • Split-GFP complementation assays between ERP44 and clients

  • pH-sensitive fluorescent tags to monitor ERP44 trafficking through compartments with different pH

• Nanobody-based approaches:

  • Develop conformation-specific nanobodies to distinguish active vs. inactive ERP44

  • Use nanobodies for super-resolution microscopy of ERP44 distribution

  • Employ intrabodies to track or manipulate ERP44 function in living cells

• Genome-wide interaction screening:

  • CRISPR screens for factors affecting ERP44-client interactions

  • Synthetic genetic interaction screens to identify pathways connected to ERP44 function

  • Secretome analysis under ERP44 perturbation conditions

• Advanced mass spectrometry approaches:

  • Crosslinking mass spectrometry (XL-MS) to map interaction interfaces

  • Redox proteomics to identify ERP44-dependent disulfide formation globally

  • Glycoproteomics to track ERP44 O-glycosylation under different conditions

• Microfluidic and single-cell approaches:

  • Single-cell analysis of ERP44 function in heterogeneous populations

  • Microfluidic pulse-chase to track protein trafficking with high temporal resolution

  • Droplet-based assays for ERP44-client interactions

These emerging technologies provide unprecedented spatiotemporal resolution and systems-level insights into ERP44 function in living cells .

What disease models are revealing new roles for ERP44 in pathophysiology?

Emerging disease connections for ERP44 include:

• Metabolic disorders:

  • Adiponectin quality control: ERP44 regulates adiponectin assembly and secretion

  • Potential role in insulin resistance through adiponectin regulation

  • Experimental approaches: Diet-induced obesity models, correlation of ERP44 levels with metabolic parameters

• Immunological disorders:

  • IgM assembly regulation: ERP44 controls IgM polymerization and secretion

  • Implications for antibody-mediated diseases

  • Experimental models: B cell-specific ERP44 manipulation, analysis in autoimmune disease models

• Reproductive biology:

  • Endometrial cycling: O-glycosylated ERP44 shows cyclic oscillation in endometrial stromal cells

  • Potential roles in endometrial pathologies

  • Research approach: Analysis of ERP44 in endometriosis and other reproductive disorders

• Cancer biology:

  • ERP44 in cancer cell secretory pathways: Potential role in extracellular matrix modification

  • Detection in breast and liver cancer tissues by immunohistochemistry

  • Research approach: Correlation of ERP44 levels with cancer progression and therapy response

• Neurodegenerative diseases:

  • ER stress and protein misfolding are hallmarks of neurodegeneration

  • ERP44's role in protein quality control suggests potential involvement

  • Research approach: Analysis in models of Alzheimer's, Parkinson's, and ALS

• Redox homeostasis disorders:

  • ERP44 regulates oxidative protein folding through interaction with Ero1

  • Implications for diseases involving redox imbalance

  • Research approach: Analysis in models of oxidative stress-related pathologies

These emerging connections highlight ERP44's diverse roles across physiological systems and disease processes .

How are systems biology approaches advancing our understanding of ERP44's role in the protein quality control network?

Systems biology is revealing ERP44's broader network functions:

• Interactome mapping approaches:

  • Comprehensive protein-protein interaction studies place ERP44 in the quality control network

  • Integration with other thioredoxin family members (PDIs) and ER chaperones

  • Network visualization tools reveal ERP44's position at the interface of distinct quality control modules

• Multi-omics integration:

  • Combining proteomics, transcriptomics, and metabolomics data under ERP44 perturbation

  • Correlation with ER stress response networks

  • Pathway enrichment analysis to identify biological processes most affected by ERP44 dysfunction

• Mathematical modeling of secretory pathway dynamics:

  • Kinetic models of protein folding, trafficking, and quality control incorporating ERP44

  • pH-dependent transport models between ER, ERGIC, and Golgi

  • Prediction of system-level consequences of ERP44 perturbation

• Evolutionary systems biology:

  • Comparative analysis of ERP44 across species reveals conserved network motifs

  • Co-evolution analysis with client proteins and quality control machinery

  • Identification of species-specific adaptations in the ERP44 system

• Network perturbation analysis:

  • Systematic genetic or chemical perturbation of nodes in the ERP44 network

  • Identification of synthetic lethal interactions and compensatory mechanisms

  • Prediction of therapeutic targets related to ERP44 function

• Multi-scale modeling:

  • Integration of molecular dynamics simulations of ERP44-client interactions

  • Cellular-level models of secretory pathway function

  • Tissue-level models of physiological consequences of ERP44 dysfunction