Recombinant Desulfovibrio vulgaris UPF0234 protein DVU_1981 (DVU_1981)

What is the UPF0234 protein family and how is DVU_1981 classified?

DVU_1981 belongs to the UPF0234 family of proteins (UPF stands for "Uncharacterized Protein Family") found in Desulfovibrio vulgaris strain Hildenborough (ATCC 29579/DSM 644/NCIMB 8303) . The protein consists of 163 amino acids with a molecular mass of approximately 18.6 kDa .

While the specific function of this family remains incompletely characterized, sequence conservation across bacterial species suggests biological significance. When investigating this protein, researchers should note:

• Sequence conservation patterns may provide clues about functional domains

• The UPF0234 family appears across multiple bacterial species, suggesting an important role

• The protein's relatively small size (163 amino acids) makes it amenable to full recombinant expression

• The complete amino acid sequence is: MPSFDVVNKIELQELDNAVNNVKKEIETRYDFRNTTTEIDLHKGDLRITVVAADEMKMRALEEMLHAHCVRRKIDPRCLEFKEIEATSRGAVKREVQVKEGIAKDVAQKIVKAIKDSKLKVQGAIQDQQVRVTGKKIDDLQDVIALLREGDFGIPLQFVNMKN

How does DVU_1981 potentially relate to Desulfovibrio vulgaris metabolism?

While direct evidence linking DVU_1981 to specific metabolic pathways is limited, understanding the broader context of D. vulgaris metabolism provides important research directions:

D. vulgaris is characterized by its ability to respire sulfate linked to lactate oxidation, which is a key metabolic signature of the Desulfovibrio genus . The organism contains a nonacistronic transcriptional unit called the lactate utilization operon (luo) that encodes proteins involved in lactate metabolism .

Methodological approaches to investigate DVU_1981's metabolic role could include:

• Gene expression correlation analysis between DVU_1981 and known metabolic genes under various growth conditions

• Creation of knockout mutants to observe phenotypic effects on growth with different electron donors/acceptors

• Protein-protein interaction studies with components of the lactate utilization pathway

• Comparative genomics to identify potential functional associations based on gene neighborhood

When designing metabolism-related experiments, researchers should account for D. vulgaris being an obligate anaerobe with specialized energy conservation mechanisms.

What bioinformatic approaches can predict DVU_1981 structure and function?

Researchers can employ multiple complementary bioinformatic approaches to generate hypotheses about DVU_1981 function:

When applying these approaches to DVU_1981, special attention should be paid to:

• Potential metal-binding motifs, as many proteins in anaerobic bacteria coordinate metal ions

• Sequence conservation patterns within the UPF0234 family

• Possible interaction interfaces that might suggest binding partners

• Structural features that could indicate enzymatic or regulatory functions

What expression systems are optimal for recombinant DVU_1981 production?

Production of functional recombinant DVU_1981 requires careful consideration of expression systems to address challenges common to proteins from anaerobic organisms:

Expression System Comparison:

Optimization Protocol:

• Vector design considerations:

  • Include a cleavable affinity tag (His6, GST) for purification

  • Optimize codon usage for the expression host

  • Consider low-temperature inducible promoters for improved folding

• Expression conditions to test:

  • Temperature range (15°C, 25°C, 37°C)

  • Inducer concentration (0.1-1.0 mM IPTG)

  • Media formulations (LB, TB, auto-induction)

  • Duration of expression (4h vs. overnight)

Given DVU_1981's relatively small size (18.6 kDa) , a standard E. coli expression system with temperature optimization would be a reasonable starting point, with contingency plans for anaerobic expression if initial attempts yield poorly folded protein.

How should researchers design experiments to resolve contradictory functional data for DVU_1981?

When confronted with contradictory data in DVU_1981 functional studies, researchers should implement structured experimental designs that systematically address potential sources of variation.

Classification of Contradiction Types:

Following the framework described by researchers in data quality assessment , contradictions can be classified using parameters (α, β, θ), where:

• α represents the number of interdependent items

• β represents the number of contradictory dependencies

• θ represents the minimal number of required Boolean rules to assess contradictions

Systematic Approach to Resolving Contradictions:

• Experimental Design Implementation:

  • Use fractional factorial designs to efficiently explore multiple variables

  • Implement central composite designs when exploring optimal conditions

  • Consider split-plot designs when certain factors are difficult to randomize

• Three-Stage Experimental Strategy:

This three-stage approach, similar to that described for chemical reaction optimization , allows for iterative refinement and mid-experiment adjustments based on preliminary findings.

• Data Integration Framework:

  • Document all experimental conditions comprehensively

  • Implement consistent metadata standards across experiments

  • Track all deviations from protocols

  • Systematically catalog all contradictory observations

  • Evaluate methodological differences that could explain contradictions

This structured approach to experimental design and contradiction resolution enables researchers to systematically address discrepancies in functional characterization of DVU_1981.

What methodological approaches can detect protein-protein interactions involving DVU_1981?

Characterizing protein-protein interactions (PPIs) of DVU_1981 requires a systematic approach combining in silico prediction, in vitro validation, and in vivo confirmation.

In Silico PPI Prediction Methods:

• Sequence-based methods (conserved interfaces, binding motifs)

• Structure-based methods (docking simulations, interface prediction)

• Genomic context methods (gene neighborhood, gene fusion detection)

Experimental Validation Approaches:

Experimental Design Considerations:

• Include appropriate positive and negative controls

• Validate initial hits with orthogonal methods

• Consider buffer conditions that maintain physiological relevance

• Titrate protein concentrations to identify specific interactions

• Test interactions under anaerobic conditions if appropriate

Given the context of D. vulgaris metabolism, examining potential interactions with proteins in the lactate utilization pathway would be a logical starting point, as this represents a key metabolic process in this organism .

What purification strategies yield the highest purity and activity for recombinant DVU_1981?

Purifying recombinant DVU_1981 to high homogeneity while maintaining its native activity requires a tailored purification strategy:

Recommended Purification Workflow:

• Initial capture:

  • Immobilized Metal Affinity Chromatography (IMAC) for His-tagged protein

  • Glutathione affinity chromatography for GST-tagged protein

• Intermediate purification:

  • Ion exchange chromatography (based on theoretical pI of DVU_1981)

  • Tag removal using specific proteases (TEV, PreScission)

• Polishing step:

  • Size exclusion chromatography

  • Second IMAC step (reverse purification after tag removal)

Buffer Optimization Parameters:

Critical Quality Control Metrics:

• SDS-PAGE with densitometry (target >95% purity)

• Mass spectrometry for identity confirmation

• Dynamic light scattering for homogeneity evaluation

• Circular dichroism to verify secondary structure

Given that DVU_1981 comes from an anaerobic organism (Desulfovibrio vulgaris) , consider performing purification under anaerobic or low-oxygen conditions to maintain native conformation and activity.

What approaches are most effective for crystallization and structural determination of DVU_1981?

Structural characterization of DVU_1981 requires optimization of experimental conditions specific to this protein, with contingency plans for common obstacles encountered in protein structural biology.

Method Selection Guide:

For DVU_1981 (18.6 kDa) , both X-ray crystallography and NMR spectroscopy represent viable approaches, with the choice depending on protein behavior and available resources.

Crystallization Strategy:

• Initial screening:

  • Commercial sparse matrix screens (96-well format)

  • Systematic grid screens around promising conditions

  • Protein concentration range testing (5-15 mg/mL)

• Optimization parameters:

  • pH fine-tuning (±0.2 pH units)

  • Precipitant concentration adjustment (±2%)

  • Additive screening (metals, small molecules)

  • Temperature variation (4°C, 16°C, 20°C)

• Crystal improvement techniques:

  • Seeding (micro, macro, cross)

  • Surface entropy reduction mutations

  • In situ proteolysis

  • Crystallization chaperones

Given DVU_1981's origin from an anaerobic organism, special attention should be paid to potential oxygen sensitivity. Consider performing crystallization setups in an anaerobic chamber or adding reducing agents to maintain protein integrity during structural studies.

How should researchers approach site-directed mutagenesis to identify functional residues in DVU_1981?

Site-directed mutagenesis provides a powerful approach to identify functional residues in DVU_1981, but requires careful planning and systematic execution.

Residue Selection Strategy:

• Sequence conservation analysis:

  • Multiple sequence alignment of UPF0234 family members

  • Identification of highly conserved residues across species

  • Evolutionary rate analysis to identify sites under selective pressure

• Structure-based prediction:

  • Analysis of predicted active sites or binding pockets

  • Identification of surface-exposed charged residues

  • Evaluation of potential disulfide bonds or metal-binding sites

Mutation Design Approach:

Experimental Validation Framework:

• Western blot analysis to confirm expression levels

• Thermal shift assays to evaluate folding stability

• Activity assays based on predicted function

• Binding assays for interaction partners

• In vivo complementation studies

This approach allows for quantitative comparison across multiple mutations and facilitates the identification of residues critical for DVU_1981 function.

How can researchers integrate multi-omics data to understand the biological context of DVU_1981?

Understanding the biological role of DVU_1981 benefits from integrating multiple types of omics data to place the protein within its broader functional context in Desulfovibrio vulgaris.

Data Integration Framework:

• Transcriptomic analysis:

  • RNA-seq under various growth conditions

  • Identification of co-expressed genes

  • Promoter analysis for regulatory elements

• Proteomic approaches:

  • Global protein expression profiling

  • Post-translational modification mapping

  • Protein-protein interaction networks

• Metabolomic studies:

  • Metabolite profiling in wild-type vs. mutant strains

  • Flux analysis to identify affected pathways

  • Stable isotope labeling to track metabolic fate

Integration Methodology:

Based on contextual information about Desulfovibrio vulgaris, particular attention should be paid to potential relationships between DVU_1981 and the lactate utilization operon, which plays a key role in the organism's energy metabolism . Integration of transcriptomic data under various electron donor/acceptor conditions could reveal functional associations between DVU_1981 and characterized metabolic pathways.

What experimental designs are most effective for determining DVU_1981's role in D. vulgaris?

Determining the biological role of DVU_1981 in Desulfovibrio vulgaris metabolism requires a comprehensive experimental strategy that combines genetic, biochemical, and physiological approaches.

Systematic Experimental Plan:

Specialized Approaches for Anaerobic Organisms:

Given that Desulfovibrio vulgaris is an anaerobic organism with specialized metabolism , particular consideration should be given to:

• Anaerobic cultivation techniques:

  • Proper anaerobic chamber usage

  • Redox potential monitoring

  • Oxygen scavenging systems

• Metabolic considerations:

  • Electron donor/acceptor variation

  • Lactate/sulfate metabolism focus (key for D. vulgaris)

  • Hydrogen metabolism assessment

• Comparative genomics:

  • Function prediction based on genomic context

  • Analysis of UPF0234 family genes in related organisms

  • Correlation with metabolic capabilities across species

Initial experiments should investigate potential connections between DVU_1981 and the well-characterized lactate utilization pathways, which represent a key metabolic signature of this organism .

How can computational approaches predict the impact of mutations in DVU_1981?

Predicting the functional and structural impact of mutations in DVU_1981 requires a multi-faceted computational approach that integrates sequence, structure, and evolutionary information.

Sequence-Based Prediction Methods:

• Position-specific scoring matrices

• Jensen-Shannon divergence calculation

• Evolutionary trace analysis

• Machine learning classifiers

• Statistical coupling analysis for co-evolving residue networks

Structure-Based Prediction Approaches:

Protocol for Comprehensive Mutation Analysis:

• Initial screening:

  • Evolutionary conservation mapping

  • Solvent accessibility calculation

  • Secondary structure propensity

• Detailed energy calculations:

  • Force field-based stability predictions

  • Electrostatic potential changes

  • Hydrogen bond network analysis

• Dynamic impact assessment:

  • Molecular dynamics simulations

  • Normal mode analysis

  • Elastic network models

For DVU_1981, where the function is not fully characterized, computational predictions should focus on identifying structurally destabilizing mutations first, followed by potential functional hotspots based on conservation patterns within the UPF0234 family.