Recombinant Pisum sativum OBERON-like protein (PVIP), partial

What are the optimal conditions for extracting PVIP from pea tissues?

Extracting PVIP from pea tissues requires careful pH optimization. Research indicates that alkaline conditions (pH 9.0) significantly improve protein solubility and extraction efficiency of pea proteins. The recommended protocol involves dispersing pea flour in water (1:10 ratio), adjusting to pH 9.0 with 1 mol dm³ NaOH, stirring for 1 hour at room temperature, followed by centrifugation (4,000 × g for 10 min) to remove insoluble components. Sequential extractions improve yield, with the insoluble fraction re-extracted at pH 9.0 for 30 minutes before the second centrifugation. The supernatants should be combined before further processing .

How does the vicilin:legumin ratio affect PVIP extraction efficiency?

The vicilin:legumin ratio significantly impacts extraction efficiency of pea proteins. Research demonstrates a positive correlation between higher vicilin:legumin ratios and increased protein extractability. Genotypes with higher vicilin content (such as L1 with vicilin:legumin ratio of 1.49) show improved protein extraction compared to those with lower ratios (such as Maja with ratio of 1.06). When developing extraction protocols for PVIP, researchers should consider the composition of the specific pea genotype being used, as the ratio of vicilin + convicilin:legumin can range from 1.30 to 1.78 depending on genotype .

What purification strategy yields the highest purity for recombinant PVIP?

For optimal purification of recombinant PVIP, a combined approach of isoelectric precipitation followed by chromatographic techniques is recommended. Initial purification should begin with isoelectric precipitation at pH 4.5 (the approximate isoelectric point of many pea proteins), followed by centrifugation (4,000 × g for 15 min). The precipitate should be re-dissolved at pH 9.0, precipitated again at pH 4.5, and re-dissolved at pH 7.0 before lyophilization. For higher purity, subsequent chromatographic techniques such as ion-exchange chromatography followed by size-exclusion chromatography are advised. This approach can effectively separate PVIP from other pea proteins that may have similar physicochemical properties .

What analytical methods are most effective for confirming the identity of recombinant PVIP?

Confirming recombinant PVIP identity requires a multi-method approach. SDS-PAGE under both reducing and non-reducing conditions is essential for determining molecular weight and subunit composition. For PVIP, molecular weight determination should be complemented with Western blotting using antibodies specific to the OBERON-like protein. Mass spectrometry (MALDI-TOF MS or LC-MS/MS) provides definitive identification through peptide mass fingerprinting and amino acid sequencing. Circular dichroism spectroscopy helps verify proper folding by analyzing secondary structure elements. N-terminal sequencing can confirm the presence of expected amino acid sequences and verify proper processing of any signal peptides. These combined approaches provide comprehensive validation of recombinant PVIP identity .

How can researchers distinguish between native and recombinant PVIP in experimental systems?

Distinguishing between native and recombinant PVIP requires strategic approaches targeting structural and functional differences. Researchers should incorporate affinity tags (His, FLAG, or GST) in recombinant constructs, enabling specific detection via anti-tag antibodies in Western blots or through affinity chromatography. Mass spectrometry can identify sequence modifications specific to the recombinant version. If the recombinant PVIP includes altered glycosylation patterns, lectin blotting or glycan-specific staining can reveal these differences. Post-translational modification analysis using phospho-specific antibodies or specialized mass spectrometry can identify differences in modification patterns between native and recombinant forms, providing clear differentiation between the two protein sources .

What are the key structural differences between PVIP and other OBERON-like proteins from different plant species?

OBERON-like proteins across plant species share conserved domains but exhibit species-specific structural variations. While the search results don't provide specific information about PVIP structure, analysis of related plant proteins suggests that OBERON-like proteins typically contain DNA-binding domains and protein-protein interaction regions. The key structural differences likely involve amino acid composition in variable regions, post-translational modification patterns, and tertiary structure elements that confer species-specific functions. Based on pea protein research, PVIP may contain uniquely positioned cysteine residues that form disulfide bonds differently than in other species, potentially affecting protein stability and function. Comparative structural analysis using X-ray crystallography or cryo-electron microscopy would be necessary to fully characterize these differences .

How does pH affect the solubility and stability of recombinant PVIP?

The solubility and stability of recombinant PVIP varies significantly with pH. Research on pea proteins indicates minimal solubility near the isoelectric point (pH 4.5-5.0) and maximal solubility at alkaline pH values (8.0-9.0). At pH values below 3.0 or above 10.0, protein denaturation may occur, compromising stability. Storage stability studies should monitor the protein at various pH values (3.0, 5.0, 7.0, and 9.0) over extended periods (0, 7, 14, 28 days) at controlled temperatures (4°C and 25°C), with regular assessment of structural integrity through circular dichroism spectroscopy and functional assays. Because solubility directly impacts functional properties, emulsifying and foaming properties should be evaluated across the pH range to determine optimal conditions for different applications .

What experimental approaches best characterize PVIP phosphorylation patterns and their functional significance?

Characterizing PVIP phosphorylation requires a comprehensive analytical workflow. Initial identification of phosphorylation sites should employ mass spectrometry-based phosphoproteomics, using both titanium dioxide (TiO₂) enrichment for phosphopeptides and liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Site-directed mutagenesis of identified phosphorylation sites (converting serine/threonine residues to alanine or aspartate to mimic non-phosphorylated or phosphorylated states) helps establish functional significance. Phosphorylation-specific antibodies can monitor phosphorylation status under different experimental conditions. Kinase inhibitor studies may identify responsible kinases, while in vitro kinase assays confirm direct interactions. Combining these approaches with functional assays will establish correlations between phosphorylation patterns and PVIP activity, revealing regulatory mechanisms governing protein function .

How can researchers effectively measure and compare the enzymatic activity of wild-type versus mutant PVIP variants?

Measuring enzymatic activity differences between wild-type and mutant PVIP variants requires establishing reliable, reproducible assays specific to PVIP's function. Though the exact enzymatic function of PVIP isn't specified in the search results, researchers should develop activity assays based on known OBERON-like protein functions. Key considerations include: (1) Substrate specificity determination using a range of potential substrates; (2) Enzyme kinetics analysis measuring Km, Vmax, and kcat values to quantify differences in catalytic efficiency; (3) pH and temperature optima determination for each variant; (4) Inhibition studies to identify regulatory mechanisms; and (5) Assessment of cofactor requirements. Statistical analysis should employ one-way ANOVA with post-hoc tests (e.g., Tukey's HSD) to establish significant differences in activity parameters. For comprehensive characterization, researchers should complement in vitro assays with in vivo functional studies using appropriate model systems .

What strategies can optimize expression of recombinant PVIP in bacterial systems?

Optimizing recombinant PVIP expression in bacterial systems requires systematic adjustment of multiple parameters. Codon optimization is crucial - researchers should adapt the PVIP coding sequence to match the codon usage bias of the expression host (commonly E. coli) to enhance translation efficiency. Selection of an appropriate expression vector with tightly regulated inducible promoters (such as T7 or tac) allows controlled expression. Expression conditions should be optimized by testing various induction parameters: IPTG concentrations (0.1-1.0 mM), induction temperatures (16°C, 25°C, 37°C), and induction durations (4-24 hours). Inclusion of solubility-enhancing fusion partners (SUMO, MBP, or TrxA) can improve proper folding. For proteins forming inclusion bodies, solubilization and refolding protocols using chaotropic agents followed by controlled dilution methods should be established. Co-expression with molecular chaperones (GroEL/GroES, DnaK/DnaJ) can significantly improve folding and solubility of complex plant proteins like PVIP .

How do genetic variations in PVIP correlate with protein function across different pea genotypes?

Genetic variations in PVIP across pea genotypes likely affect protein function through alterations in amino acid sequence and expression levels. While specific PVIP variations aren't detailed in the search results, research on pea proteins demonstrates significant genotypic influence on protein content, composition, and functionality. Quantitative trait loci (QTLs) have been identified on multiple chromosomes associated with protein quality traits. For PVIP research, genotyping by sequencing or whole-genome sequencing can identify single nucleotide polymorphisms (SNPs) and structural variants in the PVIP gene region. These genetic variations should be correlated with protein expression levels (via RT-qPCR and Western blotting) and functional assays to establish genotype-phenotype relationships. Multiple pea genotypes should be compared to construct haplotype maps relating genetic variation to functional outcomes, providing valuable insights for breeding programs focused on improving PVIP-related traits .

What yeast two-hybrid system modifications improve detection of PVIP interaction partners?

Enhancing yeast two-hybrid (Y2H) system sensitivity for PVIP interaction partner detection requires specialized modifications. To minimize false positives and negatives, researchers should use a split-ubiquitin Y2H system, which is particularly effective for plant proteins. PVIP should be tested as both bait and prey to capture interactions that might be directionally sensitive. Domain-based Y2H, where individual domains of PVIP are tested separately, can identify domain-specific interactions often missed in full-length protein screens. Stringency optimization through adjusting selection medium composition (varying 3-AT concentrations) balances sensitivity against background. For plant-specific interactions, incorporating plant-derived cofactors or using plant extract supplementation in the growth medium can recreate conditions necessary for biologically relevant interactions. All positive interactions should be validated through reciprocal Y2H tests and secondary methods such as co-immunoprecipitation or bimolecular fluorescence complementation (BiFC) .

How can researchers effectively use CRISPR-Cas9 to study PVIP function in pea plants?

Implementing CRISPR-Cas9 for PVIP functional studies in pea requires careful design and optimization. For guide RNA (gRNA) design, researchers should target conserved functional domains of PVIP, with multiple gRNAs (3-4) targeting different exons to maximize editing efficiency. Pea-specific codon-optimized Cas9 should be used, with expression driven by strong constitutive promoters (e.g., CaMV 35S) or tissue-specific promoters for targeted editing. Delivery options include Agrobacterium-mediated transformation of embryonic axes or particle bombardment of embryonic tissues, with regeneration through tissue culture. Screening for edited plants requires a combination of PCR-RFLP, T7E1 assay, and Sanger sequencing. Homology-directed repair templates can be included to introduce specific mutations or reporter genes. Phenotypic characterization should assess both molecular (transcriptomics, proteomics) and physiological parameters (growth, stress response) in multiple independent transgenic lines compared to wild-type controls. Off-target effects should be evaluated through whole-genome sequencing of selected lines .

What are the most informative in silico approaches for predicting PVIP function based on structural homology?

In silico prediction of PVIP function through structural homology analysis requires a multi-faceted computational approach. Researchers should begin with sequence-based methods including PSI-BLAST and HMMer to identify remote homologs, followed by multiple sequence alignment using MUSCLE or MAFFT to identify conserved functional motifs. For structural prediction, AlphaFold2 or RoseTTAFold can generate accurate 3D models of PVIP, which should then undergo refinement using molecular dynamics simulations to assess structural stability. Functional annotation should employ combined approaches: (1) structural alignment against proteins of known function using DALI or TM-align; (2) binding site prediction through COACH or 3DLigandSite; (3) molecular docking with potential substrates or interaction partners; and (4) evolutionary conservation mapping onto the structural model using ConSurf. Integration of these approaches with available experimental data and gene co-expression networks can significantly improve functional predictions. Researchers should validate in silico predictions through targeted experimental approaches such as site-directed mutagenesis of predicted functional residues .

What are the critical factors affecting reproducibility in PVIP functional assays?

Ensuring reproducibility in PVIP functional assays requires strict control of multiple experimental variables. First, protein quality is paramount—researchers must verify batch-to-batch consistency through analytical techniques including SDS-PAGE, mass spectrometry, and circular dichroism to confirm structural integrity. Storage conditions significantly impact stability; protocols should specify temperature (-80°C for long-term), buffer composition (pH, salt concentration, reducing agents), and avoid freeze-thaw cycles by preparing single-use aliquots. Assay standardization requires precise control of reaction parameters including temperature (±0.5°C), pH (±0.1 units), substrate concentration, and incubation time. Reference standards and positive controls should be included in each experimental run. Instrument calibration and maintenance schedules must be documented. Biological replicates (minimum n=3) and technical replicates (minimum n=3) are essential, with statistical power calculations guiding sample size determination. Detailed reporting of all methodological parameters, including reagent sources and lot numbers, enables proper replication by other researchers .

What specialized approaches are needed to study post-translational modifications of PVIP?

Comprehensive analysis of PVIP post-translational modifications (PTMs) requires integrated analytical workflows targeting specific modification types. For phosphorylation studies, researchers should employ titanium dioxide (TiO₂) or immobilized metal affinity chromatography (IMAC) enrichment followed by LC-MS/MS analysis with collision-induced dissociation (CID) and electron transfer dissociation (ETD) fragmentation to precisely locate phosphorylation sites. Glycosylation analysis requires sequential enzymatic deglycosylation (PNGase F, Endo H) followed by released glycan analysis using hydrophilic interaction liquid chromatography (HILIC) coupled with mass spectrometry. For comprehensive PTM profiling, top-down proteomics using high-resolution mass spectrometry (Orbitrap or FTICR-MS) preserves intact protein modifications. Site-specific incorporation of unnatural amino acids can be used to mimic PTMs for functional studies. Temporal dynamics of modifications should be tracked using pulse-chase labeling with subsequent mass spectrometry analysis. Integration of these approaches provides a comprehensive view of PVIP PTM landscape essential for understanding regulatory mechanisms .

How do specific amino acid substitutions affect PVIP stability and function?

Amino acid substitutions can significantly impact PVIP stability and function through multiple mechanisms. While specific PVIP data isn't available in the search results, research on pea proteins indicates that substitutions in conserved regions have greater functional consequences than those in variable regions. Critical considerations include charge alterations (substituting charged for neutral residues can disrupt salt bridges), hydrophobicity changes (affecting protein folding and solubility), and alterations to cysteine residues (disrupting disulfide bonds). Structure-guided mutagenesis should target conserved domains first, with alanine scanning providing baseline data on essential residues. Stability analysis should employ differential scanning calorimetry to determine melting temperature (Tm) changes and circular dichroism to monitor structural integrity. Functional assays specific to PVIP activity should be conducted in parallel with stability measurements to correlate structural changes with functional outcomes. Computational approaches like molecular dynamics simulations can predict substitution effects before experimental validation .

What methodological approaches best elucidate the relationship between PVIP quaternary structure and function?

Elucidating relationships between PVIP quaternary structure and function requires complementary biophysical and biochemical approaches. Size-exclusion chromatography coupled with multi-angle light scattering (SEC-MALS) provides accurate molecular weight determination of native complexes, while analytical ultracentrifugation can distinguish between different oligomeric states and determine association/dissociation constants. Chemical crosslinking followed by mass spectrometry (CXMS) identifies specific residues involved in subunit interactions. Native mass spectrometry preserves non-covalent interactions and identifies oligomeric distributions. Structure determination through X-ray crystallography or cryo-electron microscopy provides atomic-level details of quaternary arrangements. Functional studies should compare activity across different oligomeric states, isolated through techniques like gradient centrifugation or ion-exchange chromatography. Mutagenesis targeting interface residues can disrupt specific interactions, allowing correlation between quaternary structure disruption and functional consequences. Temperature and pH perturbation studies reveal stability of different oligomeric forms under varying conditions .

How can hydrogen-deuterium exchange mass spectrometry be applied to study PVIP conformational dynamics?

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) offers powerful insights into PVIP conformational dynamics when properly optimized. Experimental design should include careful pH control (typically pH 7.0) and temperature standardization (usually 25°C) during deuterium labeling. Time-course experiments with multiple deuterium exposure periods (10 sec to 24 hours) capture fast and slow exchanging regions. Quenching conditions (pH 2.5, 0°C) must immediately halt exchange. Optimized digestion using immobilized pepsin columns under quench conditions maximizes sequence coverage. Data analysis should employ specialized software (e.g., HDExaminer, DynamX) for peptide identification, deuterium uptake quantification, and statistical validation. For comparative studies, differential HDX-MS comparing PVIP under various conditions (ligand-bound vs. unbound, wild-type vs. mutant) reveals regions undergoing conformational changes. Integration with structural models allows mapping exchange rates onto three-dimensional structures, highlighting protected regions (likely buried or involved in hydrogen bonding) versus exposed flexible regions. This approach can identify allosteric effects, binding-induced conformational changes, and dynamic regions critical for PVIP function .

What approaches can identify genetic markers associated with PVIP expression for plant breeding programs?

Identifying genetic markers associated with PVIP expression for breeding programs requires multi-faceted genomic approaches. Quantitative trait loci (QTL) mapping using biparental populations can locate chromosomal regions influencing PVIP expression. Research on pea proteins has identified multiple QTLs associated with protein quality traits, including those on chromosomes 1, 2, 3, 4, and 5, with individual QTLs explaining 8-21% of phenotypic variation. Genome-wide association studies (GWAS) using diverse germplasm can pinpoint specific single nucleotide polymorphisms (SNPs) associated with PVIP expression. Expression QTL (eQTL) analysis, combining transcriptomics with genomic data, can identify regulatory variants specifically affecting PVIP transcript levels. Marker development should focus on codominant markers (SNPs, SSRs) amenable to high-throughput genotyping. Validation of marker-trait associations should occur across multiple environments and genetic backgrounds to ensure stability. Haplotype analysis can identify optimal allele combinations for marker-assisted selection, significantly accelerating breeding cycles compared to phenotypic selection alone .

How can researchers effectively design and implement RNA interference experiments to study PVIP function in vivo?

Designing effective RNA interference (RNAi) experiments for PVIP functional studies requires careful consideration of multiple factors. Target sequence selection is critical—researchers should design multiple siRNAs (3-4) targeting different exons of the PVIP transcript, avoiding regions with homology to non-target genes. Each siRNA should meet established design criteria: 19-23 nucleotides length, 30-60% GC content, and absence of internal repeats. For vector-based approaches, hairpin constructs with intron spacers driven by appropriate promoters (constitutive or inducible) enhance silencing efficiency. Delivery methods for pea plants include Agrobacterium-mediated transformation or virus-induced gene silencing (VIGS). Controls must include non-targeting siRNAs and wild-type comparisons. Validation of knockdown efficiency should employ RT-qPCR and Western blotting, with 70-90% reduction in expression considered successful. Phenotypic analysis should assess both molecular consequences (transcriptome/proteome alterations) and physiological impacts (growth parameters, stress responses). Complementation experiments with RNAi-resistant PVIP variants confirm phenotype specificity .