Recombinant Methylocella silvestris UPF0060 membrane protein Msil_1658 (Msil_1658)

What is Methylocella silvestris UPF0060 membrane protein Msil_1658?

Msil_1658 is a small membrane protein (108 amino acids) belonging to the UPF0060 protein family found in Methylocella silvestris strain BL2 (DSM 15510/NCIMB 13906). The protein has a UniProt accession number B8EK66 and is characterized by its transmembrane nature with distinctive hydrophobic regions . The amino acid sequence (mLTALVYVAAALAEIAGCFSFWAWLRLGKSSLWLIPGTASLLLFAWLLTLIDVSAAGRAY AAYGGVYVTVSLLWLWAMEGVWPDRWDLGGATLCLIGAAIIILAPRPA) reveals a typical membrane protein structure with multiple hydrophobic domains suitable for membrane insertion .

What is known about the host organism Methylocella silvestris?

Methylocella silvestris is an acidophilic aerobic methanotroph with several distinctive characteristics compared to other methanotrophic bacteria. It is classified as a "facultative methanotroph," capable of growing on both methane/methanol and multi-carbon substrates including acetate, ethanol, pyruvate, succinate, malate, and propane . This metabolic flexibility distinguishes it from "obligate methanotrophs" that cannot grow on substrates containing carbon-carbon bonds . M. silvestris uniquely oxidizes methane using only soluble methane monooxygenase (sMMO) enzyme and lacks the particulate methane monooxygenase (pMMO) found in other aerobic methanotrophs . The bacterium grows optimally at pH 5.5 (range 4.5-7.0) and temperatures between 4°C and 30°C .

How is recombinant Msil_1658 protein typically prepared for research?

Recombinant Msil_1658 is typically expressed in E. coli expression systems with an affinity tag (often a His-tag) to facilitate purification . The production process involves:

• Cloning the Msil_1658 gene into an appropriate expression vector

• Transformation into a compatible E. coli strain

• Induction of protein expression under optimized conditions

• Cell lysis and membrane fraction isolation

• Solubilization of the membrane protein using appropriate detergents

• Affinity purification using the attached tag

• Optional tag removal depending on experimental requirements

• Buffer exchange to a stabilizing formulation (typically Tris-based buffer with 50% glycerol)

The final purified protein is stored at -20°C for regular use or -80°C for extended storage, with working aliquots maintained at 4°C for up to one week to avoid repeated freeze-thaw cycles .

What are the typical storage and handling recommendations for Msil_1658?

Optimal storage and handling recommendations for Msil_1658 include:

The protein is typically supplied in a Tris-based buffer containing 50% glycerol, specifically optimized for this protein's stability . Repeated freezing and thawing cycles should be strictly avoided as they can lead to protein denaturation and loss of activity . It is advisable to prepare small working aliquots that can be stored at 4°C for experiments spanning up to one week .

What is the biological function of UPF0060 family proteins?

The UPF0060 protein family, to which Msil_1658 belongs, remains functionally uncharacterized (hence the UPF - Uncharacterized Protein Family designation). Current understanding suggests these proteins may play roles in:

• Membrane integrity maintenance

• Potential involvement in metabolic adaptation in methanotrophs

• Possible roles in stress response mechanisms

Research on UPF0060 proteins is still evolving, and definitive functional characterization requires further experimental validation. The distinctive expression of Msil_1658 in a facultative methanotroph suggests potential involvement in the unique metabolic flexibility of M. silvestris, especially concerning adaptation to various carbon sources .

How does the membrane topology of Msil_1658 compare to other UPF0060 family proteins?

The membrane topology of Msil_1658 features characteristic hydrophobic regions typical of transmembrane proteins. Analysis of its amino acid sequence (mLTALVYVAAALAEIAGCFSFWAWLRLGKSSLWLIPGTASLLLFAWLLTLIDVSAAGRAY AAYGGVYVTVSLLWLWAMEGVWPDRWDLGGATLCLIGAAIIILAPRPA) reveals multiple hydrophobic segments that likely span the membrane .

Advanced topology prediction algorithms suggest Msil_1658 contains:

• 3-4 transmembrane helices

• N-terminal oriented toward the cytoplasm

• C-terminal likely facing the periplasmic space

Comparative analysis with other UPF0060 family members indicates conservation of this basic topology across diverse bacterial species, though specific membrane insertion mechanisms may vary. Experimental verification of topology should employ techniques such as:

• Cysteine scanning mutagenesis followed by accessibility studies

• Epitope insertion combined with selective permeabilization

• Fusion protein approaches with reporter enzymes like alkaline phosphatase or GFP

What experimental approaches are most effective for studying Msil_1658 protein-protein interactions?

Several complementary approaches are recommended for studying Msil_1658 protein-protein interactions:

A comprehensive approach should begin with crosslinking and co-immunoprecipitation to identify candidate interacting partners, followed by validation using quantitative techniques. When designing such experiments, consideration must be given to the membrane-embedded nature of Msil_1658, which necessitates appropriate detergent selection for solubilization without disrupting native interactions .

How might genetic manipulation of Msil_1658 affect methane oxidation pathways in M. silvestris?

Genetic manipulation of Msil_1658 in M. silvestris requires consideration of established genetic tools for this organism. A two-step procedure involving electroporation of linear DNA fragments has been successfully used for gene manipulation in M. silvestris .

Potential effects of Msil_1658 manipulation on methane oxidation may include:

The experimental approach should include:

• Gene deletion using the two-step procedure described for M. silvestris

• Complementation studies with wild-type gene to confirm phenotype specificity

• Detailed phenotypic characterization focusing on growth rates on various carbon sources

• Measurement of methane oxidation rates using gas chromatography

• Transcriptomic and proteomic analyses to identify compensatory responses

What bioinformatic approaches can predict functional domains in Msil_1658?

Advanced bioinformatic analyses of Msil_1658 should employ multiple complementary approaches:

• Sequence-based predictions:

  • Multiple sequence alignment with UPF0060 family members across diverse species

  • Hidden Markov Model (HMM) profiling to identify conserved motifs

  • Analysis of coevolution patterns to identify functionally coupled residues

• Structure-based approaches:

  • Ab initio and homology-based 3D structure prediction (AlphaFold2, RoseTTAFold)

  • Molecular dynamics simulations in membrane environments

  • Binding site prediction based on surface properties

• Genomic context analysis:

  • Examination of gene neighborhood conservation across methanotrophs

  • Identification of consistent operon structures or genetic linkage

  • Phylogenetic profiling to correlate presence/absence with metabolic capabilities

• Integration with experimental data:

  • Mapping of any available mutational or structural data onto sequence/structure models

  • Correlation with transcriptomic/proteomic data under various growth conditions

These analyses should be integrated to develop testable hypotheses about Msil_1658 function, particularly focusing on regions showing high conservation or distinctive features compared to other UPF0060 family members.

How does the expression of Msil_1658 vary under different growth conditions in M. silvestris?

While specific expression data for Msil_1658 under varied conditions is not directly available in the search results, a methodological approach to address this question would include:

Experimental design for expression analysis:

• Growth conditions to test:

  • Various carbon sources (methane, methanol, acetate, pyruvate, succinate, malate)

  • Different pH values (range 4.5-7.0)

  • Temperature variations (4°C to 30°C)

  • Nutrient limitation scenarios

  • Exposure to environmental stressors

• Expression analysis techniques:

  • RT-qPCR for targeted mRNA quantification

  • RNA-Seq for transcriptome-wide analysis

  • Western blotting with specific antibodies

  • Proteomic analysis using LC-MS/MS

• Data analysis framework:

  • Normalization strategies appropriate for each data type

  • Statistical analysis of differential expression

  • Integration with physiological parameters

  • Correlation with expression of metabolic pathway genes

Given M. silvestris' unique metabolic flexibility as a facultative methanotroph , expression patterns of Msil_1658 under different carbon sources would be particularly informative for understanding its potential role in adaptive responses.

What is the optimal experimental design for characterizing Msil_1658 function?

A robust experimental design for characterizing Msil_1658 function should incorporate both loss-of-function and gain-of-function approaches within a framework that accounts for potential experimental variables:

1. Experimental variables definition:

• Independent variables: Genetic manipulation status (WT, knockout, complemented, overexpression)

• Dependent variables: Growth rates, substrate utilization, membrane integrity, stress responses

• Control variables: Temperature, pH, media composition

• Confounding variables: Potential polar effects on adjacent genes

2. Experimental groups:

• Wild-type M. silvestris BL2

• Msil_1658 knockout mutant

• Complemented knockout mutant

• Msil_1658 overexpression strain

• Control knockouts of unrelated genes

3. Methodological workflow:

This design implements principles of controlled experimentation , ensuring that variables are properly defined and controlled while maximizing the information gained from each experimental component.

How should researchers optimize recombinant Msil_1658 expression for structural studies?

Optimizing recombinant Msil_1658 expression for structural studies requires consideration of several key factors:

1. Expression system selection:

• E. coli strains specialized for membrane proteins (C41/C43(DE3), Lemo21(DE3))

• Cell-free expression systems for direct integration into nanodiscs or liposomes

• Yeast expression systems (P. pastoris) for enhanced folding of eukaryotic-like features

2. Expression construct design:

• Codon optimization for expression host

• Fusion tags selection (His, FLAG, SUMO) with optimized linkers

• Inclusion of solubility-enhancing partners (MBP, SUMO)

• TEV or PreScission protease sites for tag removal

3. Expression conditions optimization:

4. Purification strategy:

• Gentle solubilization with appropriate detergents

• Two-step purification (affinity followed by size exclusion)

• Buffer optimization for stability (screening with thermal shift assays)

• Addition of lipids during purification to maintain native-like environment

For structural studies specifically, sample homogeneity should be assessed using dynamic light scattering and negative-stain electron microscopy prior to attempting crystallization or cryo-EM studies.

What controls are essential when performing functional assays with Msil_1658?

A comprehensive control strategy for Msil_1658 functional assays should include:

1. Genetic controls:

• Empty vector controls for expression studies

• Non-related protein expression controls (similar size/topology)

• Point mutants of conserved residues

• Truncation variants lacking specific domains

2. Biochemical controls:

3. Experimental design controls:

• Technical replicates (minimum triplicate)

• Biological replicates (minimum three independent preparations)

• Randomization of sample processing order

• Blinding of sample identity where applicable

4. Validation controls:

• Multiple orthogonal assay methods measuring the same parameter

• In vitro to in vivo correlation controls

• Dose-response relationships to verify specific effects

Following established principles for experimental design , these controls ensure that observations can be specifically attributed to Msil_1658 function rather than experimental artifacts or secondary effects.

How can researchers effectively analyze the membrane topology of Msil_1658?

Effective analysis of Msil_1658 membrane topology requires a multi-technique approach combining computational predictions with experimental validation:

1. Computational predictions:

• Transmembrane helix prediction (TMHMM, MEMSAT, TOPCONS)

• Secondary structure prediction (PSIPRED, JPred)

• Homology modeling based on related structures

• Ab initio modeling with membrane-specific force fields

2. Experimental mapping techniques:

3. Integrative workflow:

• Generate computational models and predictions

• Design experiments to test specific topology features

• Generate series of genetic constructs (cysteine mutants or fusion proteins)

• Perform parallel analyses with multiple techniques

• Integrate data into a consensus topology model

• Validate model with targeted experiments on ambiguous regions

This approach implements principles of experimental design where multiple independent methods are used to address the same question, increasing confidence in the resulting topology model .

What strategies can overcome challenges in studying protein-protein interactions involving Msil_1658?

Studying protein-protein interactions involving membrane proteins like Msil_1658 presents unique challenges that can be addressed through specialized strategies:

1. Membrane environment preservation:

• Native membrane isolation techniques

• Nanodiscs or liposomes for reconstitution

• Styrene maleic acid lipid particles (SMALPs) extraction

• Amphipol stabilization of purified complexes

2. Modified interaction detection methods:

3. Validation framework:

• Interaction disruption through mutagenesis

• Competition assays with peptides mimicking interaction interfaces

• Correlation with functional assays

• Reconstruction of interactions with purified components

4. Data analysis considerations:

• Establishment of appropriate negative controls for background determination

• Statistical frameworks for distinguishing specific from non-specific interactions

• Integration of multiple datasets using Bayesian approaches

• Network analysis for contextualizing binary interactions

Applying these strategies within a controlled experimental design framework can overcome the inherent challenges in studying membrane protein interactions while generating reliable and interpretable data.

How should researchers analyze structural data for Msil_1658 in comparison to other UPF0060 family proteins?

Analysis of structural data for Msil_1658 should follow a systematic comparative approach:

1. Structural alignment and comparison:

2. Structure-based classification:

3. Structure-function correlation:

• Mapping of conserved residues onto structural models

• Identification of potential functional motifs based on structural context

• Comparative analysis of binding/interaction sites across family members

• Integration with experimental biochemical and genetic data

4. Visualization and presentation strategies:

• Multi-panel figures showing superpositions from informative angles

• Conservation-colored structural representations

• Electrostatic surface renderings for comparison

• Schematic diagrams of transmembrane topology based on structural data

This analytical framework enables identification of both conserved structural features that likely contribute to core UPF0060 family functions and divergent elements that may underlie specific functions of Msil_1658 in M. silvestris.

What statistical approaches are most appropriate for analyzing Msil_1658 expression data across different conditions?

The statistical analysis of Msil_1658 expression data should be tailored to the experimental design and data characteristics:

1. Preprocessing and normalization:

• For RT-qPCR: ΔΔCt method with appropriate reference genes

• For RNA-Seq: DESeq2 or edgeR normalization frameworks

• For proteomics: Total ion current or spike-in normalization

• Quality control through PCA and visualization of normalized distributions

2. Statistical testing framework:

3. Multiple testing correction:

• Benjamini-Hochberg procedure for false discovery rate control

• Family-wise error rate control where appropriate (Bonferroni, Holm)

• q-value estimation for large-scale datasets

4. Effect size estimation:

• Cohen's d for pairwise comparisons

• Partial η² for ANOVA designs

• Confidence intervals for all effect size estimates

5. Integration with biological interpretation:

• Gene set enrichment analysis for pathway-level understanding

• Network analysis to identify regulatory relationships

• Integration with existing models of M. silvestris metabolism

This comprehensive statistical approach follows principles of proper experimental design while addressing the specific challenges of gene expression data analysis.

How can researchers interpret contradictory findings about Msil_1658 function?

Resolving contradictory findings about Msil_1658 function requires a systematic approach to data reconciliation:

1. Identification of potential sources of discrepancy:

2. Framework for evidence evaluation:

• Weighted consideration based on methodological rigor

• Preference for orthogonally validated findings

• Examination of dose-response or concentration-dependent effects

• Consideration of physiological relevance of experimental conditions

3. Reconciliation approaches:

• Develop testable hypotheses that could explain contradictions

• Design critical experiments specifically targeting the contradictions

• Consider context-dependent functions or conditional phenotypes

• Explore potential indirect effects through systems biology approaches

4. Meta-analysis strategies:

• Formal statistical meta-analysis where appropriate

• Development of consensus models that incorporate uncertainty

• Bayesian approaches to data integration

This approach emphasizes that contradictions often reveal important biological nuances rather than simply representing experimental failures, and may lead to deeper understanding of Msil_1658's complex roles in M. silvestris biology.

What criteria should be used to assess the quality of recombinant Msil_1658 preparations?

Quality assessment of recombinant Msil_1658 preparations should include multiple complementary criteria:

1. Purity assessment:

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

• Mass spectrometry confirmation of protein identity

• Absence of degradation products or aggregates

• Endotoxin testing if intended for immunological studies

2. Structural integrity evaluation:

3. Functional validation:

• Binding assays for known ligands or interaction partners

• Activity assays if enzymatic function is established

• Reconstitution into membranes or membrane mimetics

• Structural studies (negative-stain EM, crystallization trials)

4. Batch consistency:

• Establishment of reference standards

• Lot-to-lot comparison using multiple quality parameters

• Stability testing under storage conditions (typically -20°C or -80°C)

• Documentation of production parameters for reproducibility

These quality criteria should be applied systematically to ensure that experimental outcomes can be reliably attributed to genuine Msil_1658 properties rather than preparation artifacts.

How can computational modeling enhance understanding of Msil_1658 function in methane metabolism?

Computational modeling can provide valuable insights into Msil_1658 function through several complementary approaches:

1. Structural bioinformatics:

• Homology modeling and threading against known structures

• Molecular dynamics simulations in explicit membrane environments

• Binding site prediction and virtual screening for potential ligands

• Protein-protein docking with potential interaction partners

2. Systems biology modeling:

3. Machine learning applications:

• Functional annotation transfer from characterized proteins

• Feature extraction from sequence and structural data

• Integration of heterogeneous data sources

• Classification of Msil_1658 within protein functional hierarchies

4. Integration with experimental validation:

• Generation of testable hypotheses from computational models

• Design of targeted experiments based on predictions

• Iterative refinement of models based on experimental outcomes

• Development of minimal sufficient models explaining observed phenomena

This computational strategy takes advantage of M. silvestris' unique metabolic properties as a facultative methanotroph to contextualize Msil_1658 function within its broader biological framework.