Recombinant Uncharacterized protein Rv1841c/MT1889 (Rv1841c, MT1889)

What is Recombinant Uncharacterized Protein Rv1841c/MT1889?

Recombinant Uncharacterized Protein Rv1841c/MT1889 (UniProt ID: Q50593) is a full-length protein consisting of 345 amino acids that has been recombinantly expressed with an N-terminal His-tag in E. coli expression systems . This protein represents a research target whose biological function has not yet been fully elucidated, making it a valuable subject for structural and functional studies. The recombinant version is produced through heterologous expression, allowing researchers to obtain sufficient quantities for experimental investigation while maintaining structural integrity.

What are the optimal storage conditions for Rv1841c/MT1889?

The recombinant protein is typically supplied as a lyophilized powder and should be stored at -20°C/-80°C upon receipt . For research applications requiring multiple uses, aliquoting is necessary to prevent protein degradation from repeated freeze-thaw cycles. Working aliquots may be stored at 4°C for up to one week . For long-term storage, it is recommended to add glycerol to a final concentration of 5-50% before aliquoting and storing at -20°C/-80°C, with the default recommendation being 50% glycerol .

What reconstitution protocols are recommended for lyophilized Rv1841c/MT1889?

The recommended reconstitution protocol involves:

• Briefly centrifuging the vial prior to opening to ensure all material is at the bottom

• Reconstituting the protein in deionized sterile water to a concentration of 0.1-1.0 mg/mL

• Adding glycerol to a final concentration of 5-50% for improved stability

• Aliquoting to avoid repeated freeze-thaw cycles

The reconstitution buffer is Tris/PBS-based with 6% Trehalose at pH 8.0 . This formulation helps maintain protein stability during the reconstitution process and subsequent storage. For experimental procedures requiring different buffer conditions, a step-wise dialysis approach is recommended to minimize protein aggregation or precipitation.

How can researchers design experiments to elucidate the function of this uncharacterized protein?

When working with uncharacterized proteins like Rv1841c/MT1889, a systematic multi-pronged approach is essential:

• Computational Analysis

  • Sequence homology searching against characterized proteins

  • Domain prediction and motif analysis

  • Secondary structure prediction

  • Machine learning approaches for function prediction

• Expression Pattern Analysis

  • Investigate conditions that upregulate or downregulate the protein

  • Examine tissue/cellular distribution using immunohistochemistry

  • Study temporal expression patterns

• Protein-Protein Interaction Studies

  • Co-immunoprecipitation experiments

  • Yeast two-hybrid screening

  • Proximity labeling approaches (BioID, APEX)

  • Cross-linking mass spectrometry

• Phenotypic Analysis

  • Gene knockout/knockdown experiments

  • Overexpression studies

  • Complementation assays

For rigorous experimental design, researchers should implement multiple orthogonal approaches and include appropriate controls to validate findings across methods.

What analytical techniques are appropriate for quality assessment of Rv1841c/MT1889?

Quality assessment of recombinant Rv1841c/MT1889 requires multiple analytical techniques:

When analyzing results, researchers should compare protein quality metrics against established standards for recombinant proteins used in structural and functional studies.

How can multivariate data analysis be applied to studies involving Rv1841c/MT1889?

Multivariate data analysis approaches are essential when investigating complex relationships in protein characterization studies. For Rv1841c/MT1889 research, integrating data from multiple experimental techniques requires sophisticated statistical methods:

• Principal Component Analysis (PCA): Useful for identifying patterns in multidimensional datasets generated from protein interaction studies or expression analyses

• Hierarchical Clustering: Valuable for grouping similar experimental conditions or identifying related proteins based on multiple parameters

• Partial Least Squares (PLS) Regression: Effective for modeling relationships between protein structural features and functional characteristics

• Longitudinal Data Analysis: Appropriate for studying time-dependent changes in protein expression or activity

These statistical approaches help researchers handle the high dimensionality of proteomics data and extract meaningful patterns that might not be apparent through univariate analyses.

What strategies can researchers use to address conflicting experimental results when studying Rv1841c/MT1889?

When confronted with conflicting experimental results, a systematic analytical approach is essential:

• Methodological Validation

  • Examine differences in experimental conditions, reagent sources, and protocol variations

  • Validate antibody specificity using positive and negative controls

  • Verify protein identity using orthogonal methods (e.g., mass spectrometry)

• Statistical Reassessment

  • Evaluate statistical power and sample sizes

  • Consider applying more robust statistical models appropriate for longitudinal or multivariate data

  • Implement Bayesian approaches to incorporate prior knowledge

• Contextual Integration

  • Consider cell type-specific or condition-specific effects

  • Evaluate potential post-translational modifications affecting protein function

  • Examine subcellular localization differences

• Meta-analysis Approach

  • Systematically compare methodologies across studies

  • Weight evidence based on methodological rigor

  • Identify patterns across seemingly contradictory results

By approaching discrepancies methodically, researchers can often reconcile conflicting results and generate more robust hypotheses about protein function.

How can researchers predict the three-dimensional structure of Rv1841c/MT1889?

For uncharacterized proteins like Rv1841c/MT1889, structural determination follows a tiered approach:

• Computational Prediction Methods

  • Homology modeling based on related structures

  • Ab initio modeling for novel folds

  • AlphaFold2 and RoseTTAFold deep learning approaches

  • Molecular dynamics simulations to refine predicted structures

• Experimental Structure Determination

  • X-ray crystallography (requires protein crystals)

  • Nuclear Magnetic Resonance (NMR) spectroscopy (for proteins <30 kDa)

  • Cryo-electron microscopy (especially valuable for membrane proteins)

  • Small-angle X-ray scattering (SAXS) for low-resolution envelope

• Integrated Approaches

  • Combining computational predictions with limited experimental data

  • Using crosslinking mass spectrometry to validate predicted interactions

  • Hydrogen-deuterium exchange mass spectrometry to probe structural dynamics

The amino acid sequence provided for Rv1841c/MT1889 suggests potential membrane-associated regions , which may require specialized approaches for structural determination such as detergent optimization or lipid nanodisc reconstitution.

How can functional assays be designed for an uncharacterized protein like Rv1841c/MT1889?

Designing functional assays for uncharacterized proteins requires a hypothesis-driven approach based on sequence analysis and structural predictions:

• Enzymatic Activity Screening

  • Screen for common enzymatic activities (hydrolase, transferase, oxidoreductase)

  • Substrate specificity profiling using compound libraries

  • Activity-based protein profiling with activity-specific probes

• Ligand Binding Assays

  • Thermal shift assays to identify stabilizing ligands

  • Surface plasmon resonance for binding kinetics

  • Isothermal titration calorimetry for thermodynamic parameters

• Cellular Function Assays

  • Localization studies using fluorescent protein fusions

  • Phenotypic screens following gene knockout/knockdown

  • Rescue experiments with mutant variants

• Pathway Analysis

  • Phosphoproteomics to identify signaling pathways

  • Metabolomics to detect metabolic changes

  • Transcriptomics to identify regulated genes

For each assay, researchers should implement appropriate positive and negative controls, concentration gradients, and statistical validation to ensure reliable interpretation of results.

What are the best approaches for studying potential protein-protein interactions of Rv1841c/MT1889?

Investigating protein-protein interactions for uncharacterized proteins requires multiple complementary approaches:

An integrative approach combining multiple methods provides the most comprehensive and reliable interaction network. The His-tag present on the recombinant Rv1841c/MT1889 can be utilized for initial pull-down experiments, followed by validation using orthogonal techniques.

How should researchers approach study design for longitudinal experiments involving Rv1841c/MT1889?

Longitudinal studies examining Rv1841c/MT1889 expression, localization, or function over time require careful experimental design:

• Statistical Power and Sampling

  • Determine appropriate sample sizes through power analysis

  • Plan for biological and technical replicates

  • Account for potential missing data points

• Data Analysis Considerations

  • Implement specialized statistical models for longitudinal data

  • Consider latent variable approaches for handling multivariate responses

  • Account for time-dependent correlations in measurements

• Experimental Controls

  • Include time-matched controls for each experimental condition

  • Consider including housekeeping proteins as internal standards

  • Implement normalization strategies for cross-time point comparisons

• Documentation and Reporting

  • Record detailed metadata about experimental conditions

  • Document any deviations from protocols

  • Report both positive and negative findings

Proper longitudinal study design is particularly important when investigating proteins of unknown function, as temporal patterns may provide crucial insights into biological roles.

How can researchers leverage "People Also Ask" data to enhance their Rv1841c/MT1889 research?

The "People Also Ask" (PAA) feature on Google can serve as a valuable resource for researchers studying uncharacterized proteins:

• Identifying Knowledge Gaps

  • Analyze frequently asked questions to identify areas of scientific interest

  • Note recurring themes that may indicate important research directions

• Methodology Enhancement

  • Discover alternative methodological approaches that others are implementing

  • Identify common technical challenges through related questions

• Research Planning

  • Utilize PAA expansion patterns to build comprehensive research strategies

  • Prioritize experiments based on the frequency and depth of related questions

• Communication Improvement

  • Structure research papers to address commonly asked questions

  • Frame research findings to address gaps identified in PAA questions

Researchers should start by typing relevant keywords to discover frequently asked PAA questions and then integrate these insights into their experimental design and communication strategies .

What bioinformatic resources are most valuable for researchers studying Rv1841c/MT1889?

Researchers investigating uncharacterized proteins should utilize these specialized bioinformatic resources:

• Sequence Analysis Tools

  • UniProt (Q50593): Primary protein information repository

  • InterPro: Domain and motif identification

  • PSIPRED: Secondary structure prediction

  • TMHMM: Transmembrane region prediction

• Structural Resources

  • AlphaFold DB: AI-predicted protein structures

  • PDB: Repository of experimentally determined structures

  • SWISS-MODEL: Homology modeling server

• Functional Prediction

  • Gene Ontology: Standardized functional annotations

  • STRING: Protein-protein interaction networks

  • KEGG: Pathway mapping and analysis

• Research Literature Integration

  • Academic search systems: For comprehensive literature review

  • Google Scholar: To track citations and related work

For optimal results, researchers should use multiple complementary tools and critically evaluate predictions by comparing outputs across different algorithms.