Recombinant Methanocaldococcus jannaschii Uncharacterized protein MJ0210.1 (MJ0210.1)

What is known about the structure and basic properties of MJ0210.1 protein?

MJ0210.1 is an uncharacterized protein from the hyperthermophilic methanogenic archaeon Methanocaldococcus jannaschii. The protein consists of 172 amino acids in its full-length form . As a protein from an extremophile, it likely possesses thermostable properties that allow it to function at high temperatures characteristic of M. jannaschii's natural environment.

To determine its structure, researchers typically employ X-ray crystallography, similar to the approach used for the related M. jannaschii protein MJ0754, which was resolved at 2.88 Å resolution . For structural characterization, recombinant expression systems using E. coli as the host organism with His-tagging for purification are commonly employed . Further biophysical characterization would typically include circular dichroism spectroscopy to analyze secondary structure elements, differential scanning calorimetry to assess thermostability, and size exclusion chromatography to determine oligomeric state.

How is recombinant MJ0210.1 protein typically expressed and purified for research purposes?

Recombinant MJ0210.1 protein expression typically employs an E. coli-based expression system with a His-tag for affinity purification . The methodological approach involves these key steps:

• Cloning the MJ0210.1 gene into an expression vector with a suitable promoter (typically T7) and a His-tag, either N-terminal or C-terminal.

• Transforming the construct into an E. coli expression strain optimized for archaeal proteins, such as BL21(DE3) with rare codon supplementation.

• Optimizing expression conditions, considering that archaeal proteins often require special conditions:

  • Induction at OD600 of 0.6-0.8

  • IPTG concentration typically between 0.1-1.0 mM

  • Expression temperature of 16-30°C to enhance proper folding

  • Expression duration of 4-16 hours

• Cell lysis under native or denaturing conditions, depending on protein solubility.

• Purification using immobilized metal affinity chromatography (IMAC) with Ni-NTA resin, followed by size exclusion chromatography for higher purity.

• Verification of purity using SDS-PAGE and Western blotting with anti-His antibodies.

For proteins from hyperthermophiles like M. jannaschii, a heat treatment step (65-80°C) is often incorporated after cell lysis to leverage the thermostability of the target protein while denaturing most E. coli host proteins, providing a simple initial purification step.

What approaches are used to determine if MJ0210.1 interacts with other proteins in M. jannaschii?

To determine protein-protein interactions involving MJ0210.1, researchers employ both computational predictions and experimental validation techniques:

Computational approaches:

• Genomic context analysis: Examining if MJ0210.1 is part of an operon structure with functionally related genes .

• Protein co-evolution analysis: Identifying proteins that show correlated evolutionary patterns.

• Interactome databases: Searching existing protein interaction databases for archaeal homologs with known interactions.

Experimental approaches:

• Pull-down assays: Using recombinant His-tagged MJ0210.1 as bait to capture interaction partners from M. jannaschii cell lysates .

• Yeast two-hybrid systems: Modified for archaeal proteins to screen for binary interactions.

• Cross-linking coupled with mass spectrometry: To capture and identify transient interactions.

• Co-immunoprecipitation: Using antibodies against MJ0210.1 to pull down interaction complexes.

• Surface plasmon resonance: To determine binding kinetics of confirmed interactions.

When designing these experiments, it's crucial to consider the native conditions of M. jannaschii, including high temperature (85°C), neutral to slightly acidic pH, and anaerobic environment. Thermostable versions of traditional interaction assays may need to be developed for optimal results.

How can orthologous gene displacement analysis help in predicting the function of MJ0210.1?

Orthologous gene displacement analysis provides a powerful approach for predicting the function of uncharacterized proteins like MJ0210.1 by examining evolutionary patterns across multiple genomes.

When applying this methodology to MJ0210.1, researchers should:

• Identify potential orthologs across archaeal and bacterial species using sensitive sequence alignment tools like PSI-BLAST or HMM-based methods (HMMER).

• Examine genomic context conservation, as genes in the same pathway often remain clustered throughout evolution .

• Analyze patterns of gene presence/absence to identify potential non-orthologous gene displacements, where different genes perform the same function in different species .

• Compare paralogous gene families to distinguish between orthology (genes diverged after speciation) and paralogy (genes diverged after duplication) .

• Integrate metabolic pathway analysis to identify potential functional roles in known archaeal pathways.

As demonstrated in studies of the citric acid cycle, non-orthologous gene displacement is common in metabolic pathways across diverse species . The identification of genes that appear in the same pathway context but differ in sequence can provide critical clues to the function of uncharacterized proteins like MJ0210.1.

For example, if MJ0210.1 consistently appears in genomes lacking a known metabolic enzyme, but in similar genomic contexts, it might represent a non-orthologous displacement of that enzyme. This pattern was observed in various enzymes of the citric acid cycle across bacterial and archaeal species , providing a methodological framework for analyzing MJ0210.1.

What computational approaches can predict potential enzymatic functions of MJ0210.1?

Predicting the enzymatic function of an uncharacterized protein like MJ0210.1 requires a multi-faceted computational approach:

• Structural analysis and fold recognition:

  • Thread the MJ0210.1 sequence onto known protein structures using tools like Phyre2, I-TASSER, or AlphaFold

  • Identify structural motifs associated with specific enzymatic activities

  • Analyze the presence and spatial arrangement of potentially catalytic residues

• Active site prediction:

  • Use tools like CASTp or POOL to identify potential binding pockets

  • Analyze conservation patterns of residues in these pockets across homologs

  • Compare with known enzyme active sites using resources like CSA (Catalytic Site Atlas)

• Domain architecture analysis:

  • Identify conserved domains using Pfam, SMART, or CDD

  • Analyze domain combinations that might suggest specific functions

  • Examine archaeal-specific domain architectures

• Metabolic context integration:

  • Map potential functions to the M. jannaschii metabolic network

  • Identify metabolic gaps where MJ0210.1 might function

  • Analyze potential association with known pathways

• Comparison with characterized proteins:

  • Compare with other archaeal proteins of known function, like MJ0754

  • Examine subtle sequence patterns beyond global homology

  • Consider function predicted from genomic context

By combining these approaches, researchers can generate testable hypotheses about the enzymatic function of MJ0210.1, which can then be validated through biochemical assays, genetic approaches, or structural studies.

How can crystallographic studies of MJ0210.1 inform understanding of extremophile protein adaptations?

Crystallographic studies of MJ0210.1 can provide significant insights into protein adaptations to extreme environments, similar to studies conducted on MJ0754 . A comprehensive approach would include:

• High-resolution structure determination:

  • Optimize crystallization conditions considering the extremophilic origin

  • Collect diffraction data at resolutions better than 3.0 Å

  • Solve the structure using molecular replacement or experimental phasing methods

  • Refine the structure to identify key structural features and potential active sites

• Analysis of thermostability determinants:

  • Quantify intramolecular interactions (hydrogen bonds, salt bridges, disulfide bonds)

  • Analyze hydrophobic core packing efficiency

  • Examine surface charge distribution and electrostatic interactions

  • Compare helix capping motifs with mesophilic homologs

• Comparative structural analysis:

  • Superimpose with homologous structures from mesophilic organisms

  • Identify key structural differences that may contribute to thermostability

  • Analyze loop regions and their conformational rigidity

• Temperature-dependent structural studies:

  • Collect diffraction data at multiple temperatures

  • Analyze B-factors as indicators of thermal motion

  • Identify regions with differential flexibility

• Structure-guided functional hypothesis:

  • Identify potential binding pockets or catalytic sites

  • Map conservation patterns onto the structure

  • Generate testable hypotheses about function

This approach has successfully revealed adaptation mechanisms in other M. jannaschii proteins, including increased internal hydrophobicity, optimized surface charge distribution, and strategic placement of stabilizing interactions. Similar analysis of MJ0210.1 would contribute to our understanding of extremophile adaptations while potentially revealing its functional role.

What are the key considerations when designing experiments to determine the biochemical function of MJ0210.1?

Designing experiments to determine the biochemical function of MJ0210.1 requires a systematic approach that considers the extremophilic nature of M. jannaschii and the uncharacterized status of the protein:

• Experimental variable identification:

  • Independent variable: Potential substrates, cofactors, or reaction conditions

  • Dependent variable: Measurable activity (product formation, substrate consumption)

  • Control variables: pH, temperature, ionic strength

• Hypothesis formulation based on bioinformatic analysis:

  • Develop specific, testable hypotheses about potential functions

  • Consider homology-based predictions, genomic context, and structural predictions

  • Design control experiments to test alternative hypotheses

• Experimental conditions optimization:

  • Temperature range: 70-95°C (reflecting M. jannaschii's hyperthermophilic nature)

  • pH optimization: Test range 5.0-8.0, focusing on the native pH of M. jannaschii

  • Buffer selection: Thermostable buffers like PIPES or HEPES

  • Anaerobic conditions: Consider using anaerobic chambers for experimental setup

• Activity assay development:

  • Design assays compatible with high temperatures

  • Consider coupled enzyme assays with thermostable coupling enzymes

  • Develop direct detection methods (spectrophotometric, HPLC, mass spectrometry)

• Substrate screening approach:

  • Test metabolites from pathways identified in genomic context analysis

  • Screen cofactor requirements (metal ions, nucleotides, vitamins)

  • Consider combinatorial approaches for complex reaction systems

• Validation through complementary approaches:

  • Site-directed mutagenesis of predicted key residues

  • Isothermal titration calorimetry for binding studies

  • Structural studies to confirm substrate binding modes

This experimental design framework balances hypothesis-driven approaches with broader screening methods to maximize the chance of identifying the true biochemical function of MJ0210.1.

How should researchers design gene knockout or complementation studies for MJ0210.1 in M. jannaschii?

Designing gene knockout or complementation studies for MJ0210.1 in M. jannaschii presents unique challenges due to the extremophilic nature of this archaeon and limited genetic tools. A comprehensive experimental design would include:

1. Knockout strategy development:

• Design a suicide vector system containing:

  • Homologous regions flanking MJ0210.1 (typically 500-1000 bp each)

  • Selectable marker suitable for M. jannaschii (typically a thermostable antibiotic resistance gene)

  • Origin of replication functional in the delivery strain but not in M. jannaschii

• Consider CRISPR-Cas9 approaches adapted for archaeal systems:

  • Design guide RNAs targeting MJ0210.1

  • Ensure thermostability of Cas9 expression system

  • Optimize homology-directed repair templates

2. Transformation optimization:

• Test multiple transformation methods:

  • Polyethylene glycol-mediated transformation

  • Electroporation with modified parameters for archaeal cell walls

  • Liposome-mediated DNA delivery

• Optimize transformation conditions:

  • Cell growth phase (early to mid-log phase typically optimal)

  • DNA concentration and quality

  • Recovery conditions under anaerobic, high-temperature conditions

3. Phenotypic analysis design:

• Compare growth kinetics between wild-type and knockout strains:

  • Growth rates under standard conditions

  • Stress response under varying temperatures, pH, and nutrient limitations

• Metabolomic analysis:

  • Target metabolite profiling focusing on pathways identified in genomic context analysis

  • Untargeted metabolomics to identify unexpected metabolic changes

4. Complementation system design:

• Develop shuttle vector system:

  • Origin functional in M. jannaschii

  • Thermostable selectable marker different from knockout marker

  • Inducible or constitutive promoter active in M. jannaschii

• Design complementation variants:

  • Wild-type MJ0210.1

  • Site-directed mutants of key residues

  • Homologs from related species

5. Between-subjects experimental design:

• Group assignment:

  • Wild-type M. jannaschii

  • MJ0210.1 knockout

  • Complemented strain (knockout + wild-type gene)

  • Complemented strain with mutant variants

6. Data collection and analysis plan:

• Quantitative measurements:

  • Growth rates

  • Metabolite concentrations

  • Transcriptomic changes in related pathways

• Statistical analysis approach:

  • ANOVA for multi-group comparisons

  • Post-hoc tests for specific pairwise comparisons

  • Multiple testing correction for omics data analysis

This experimental design accounts for the unique challenges of working with archaeal extremophiles while providing multiple approaches to determine the function of MJ0210.1 through genetic manipulation.

What strategies can be employed to identify potential substrates or binding partners of MJ0210.1?

Identifying substrates or binding partners of MJ0210.1 requires a multi-faceted approach that combines computational predictions with diverse experimental techniques:

1. Computational substrate prediction:

• Structural modeling and docking:

  • Generate a structural model using AlphaFold or similar tools

  • Perform virtual screening of metabolite libraries focusing on M. jannaschii metabolome

  • Analyze binding pocket conservation across homologs

• Genomic context analysis:

  • Identify gene clusters containing MJ0210.1 homologs

  • Analyze co-occurrence patterns with genes of known function

  • Map potential functions to metabolic pathways with gaps

2. Activity-based protein profiling:

• Design activity-based probes:

  • Select reactive groups based on predicted enzyme class

  • Incorporate clickable handles for enrichment

  • Ensure thermostability for compatibility with extremophile proteins

• Experimental workflow:

  • Incubate recombinant MJ0210.1 with probe libraries

  • Perform click chemistry to attach visualization/purification tags

  • Analyze labeled proteins using mass spectrometry

3. Thermal shift assays for ligand screening:

• Differential scanning fluorimetry:

  • Screen metabolite libraries for compounds that alter protein thermal stability

  • Adapt protocol for high-temperature baseline (start at 60-70°C)

  • Analyze melting curves to identify stabilizing ligands

• Optimized ligand screening:

  • Test concentration series (10 μM to 10 mM) of candidate ligands

  • Include appropriate controls (buffer, known stabilizing compounds)

  • Validate hits using orthogonal binding assays

4. Metabolomic approaches:

• Untargeted metabolomics comparison:

  • Compare metabolite profiles between wild-type and MJ0210.1 knockout strains

  • Identify metabolites with significant concentration differences

  • Map changes to known metabolic pathways

• In vitro metabolite consumption assays:

  • Incubate purified MJ0210.1 with M. jannaschii cell extract

  • Monitor metabolite changes using LC-MS

  • Identify consumed or produced metabolites

5. Protein interaction screening:

• Pull-down coupled with mass spectrometry:

  • Use His-tagged MJ0210.1 as bait

  • Process M. jannaschii lysate under native conditions

  • Identify binding partners using mass spectrometry

• Crosslinking mass spectrometry:

  • Use thermostable crosslinking reagents

  • Identify interaction interfaces

  • Map interaction networks

This comprehensive strategy integrates computational prediction with multiple experimental approaches, maximizing the chances of identifying the true substrates or binding partners of MJ0210.1.

How can researchers determine if MJ0210.1 plays a role in the citric acid cycle or related metabolic pathways?

Determining if MJ0210.1 participates in the citric acid cycle (CAC) or related metabolic pathways requires a multi-disciplinary approach that integrates genomic, biochemical, and metabolic analyses:

1. Genomic context analysis:

• Examine gene neighborhood conservation patterns:

  • Identify if MJ0210.1 is consistently located near known CAC genes

  • Compare with the genomic organization of CAC genes in other archaea

  • Look for co-occurrence patterns with CAC enzymes across species

• Analyze potential gene displacements:

  • Compare with known cases of non-orthologous gene displacement in the CAC

  • Identify if MJ0210.1 appears in genomes lacking specific CAC enzymes

  • Assess if MJ0210.1 could represent a paralogous gene displacement

2. Biochemical activity testing:

• Screen for activities of CAC enzymes:

  • Systematically test MJ0210.1 for each of the CAC enzyme activities

  • Use thermostable assay systems compatible with hyperthermophilic proteins

  • Include proper controls with known CAC enzymes from M. jannaschii

• Test for substrate binding:

  • Perform thermal shift assays with CAC intermediates

  • Use isothermal titration calorimetry to measure binding constants

  • Employ NMR to detect substrate interactions

3. Metabolomic analysis:

• Compare metabolite profiles:

  • Wild-type M. jannaschii vs. MJ0210.1 knockout

  • Focus on CAC intermediates and related compounds

  • Measure concentrations using targeted LC-MS/MS

• Flux analysis:

  • Use 13C-labeled substrates to trace metabolic fluxes

  • Determine if MJ0210.1 deletion affects carbon flow through the CAC

  • Identify potential alternative pathways activated in the absence of MJ0210.1

4. Structural comparison with known CAC enzymes:

• Analyze structural similarities:

  • Compare MJ0210.1 structure with known CAC enzymes

  • Identify potential catalytic residues or substrate binding motifs

  • Assess if MJ0210.1 could represent a divergent CAC enzyme

5. Heterologous complementation:

• Test functional complementation:

  • Express MJ0210.1 in model organisms with CAC enzyme deletions

  • Determine if MJ0210.1 can rescue growth phenotypes

  • Test under various growth conditions to identify specific pathway connections

The results from these approaches should be integrated to build a comprehensive understanding of MJ0210.1's potential role in the CAC or related metabolic pathways, similar to the approach used to analyze the evolution of the CAC across different species .

What methods can reveal the impact of temperature on MJ0210.1 structure and function?

Investigating the impact of temperature on MJ0210.1 structure and function requires specialized approaches that account for the hyperthermophilic nature of M. jannaschii:

1. Thermal stability analysis:

• Differential scanning calorimetry (DSC):

  • Measure thermal unfolding transitions from 25°C to 120°C

  • Determine melting temperature (Tm) and thermodynamic parameters

  • Compare with mesophilic homologs to quantify thermostability

• Circular dichroism (CD) spectroscopy:

  • Monitor secondary structure changes across temperature range

  • Perform thermal melting curves (25-110°C)

  • Analyze temperature-dependent conformational changes

• Intrinsic fluorescence spectroscopy:

  • Track tertiary structure changes via tryptophan fluorescence

  • Identify temperature-induced conformational transitions

  • Determine the cooperativity of unfolding

2. Activity-temperature relationship:

• Temperature-dependent enzyme kinetics:

  • Measure activity across temperature range (30-100°C)

  • Determine temperature optima and activation energy

  • Analyze Arrhenius plots to identify transition points

• Thermodynamic parameter determination:

  • Calculate ΔH‡, ΔS‡, and ΔG‡ as a function of temperature

  • Compare with mesophilic homologs to identify thermoadaptation mechanisms

  • Correlate with structural features

3. Temperature-dependent structural analysis:

• X-ray crystallography at multiple temperatures:

  • Collect diffraction data at different temperatures (20-80°C)

  • Analyze B-factors to identify flexible regions

  • Compare with related protein MJ0754

• Hydrogen-deuterium exchange mass spectrometry:

  • Measure exchange rates at different temperatures

  • Identify regions with temperature-dependent flexibility

  • Map temperature-sensitive regions onto structure

4. Molecular dynamics simulations:

• Temperature replica exchange simulations:

  • Simulate protein behavior across temperature range (25-100°C)

  • Analyze conformational ensembles at different temperatures

  • Identify key stabilizing interactions

• Analysis of temperature-dependent motion:

  • Calculate root-mean-square fluctuations at different temperatures

  • Identify rigid versus flexible regions

  • Correlate with experimental findings

5. Temperature-dependent binding studies:

• Isothermal titration calorimetry at different temperatures:

  • Measure binding constants from 25-95°C

  • Determine temperature dependence of binding affinity

  • Calculate entropy and enthalpy contributions

• Surface plasmon resonance with temperature control:

  • Measure association and dissociation kinetics

  • Determine temperature effects on binding mechanisms

  • Compare with mesophilic homologs

This multi-faceted approach will provide comprehensive insights into how temperature affects MJ0210.1's structure-function relationship and reveal adaptations that enable its function in extreme environments.

How can researchers investigate post-translational modifications of MJ0210.1 in M. jannaschii?

Investigating post-translational modifications (PTMs) of MJ0210.1 in M. jannaschii requires specialized approaches that account for both the archaeal origin and hyperthermophilic nature of the protein:

1. Mass spectrometry-based PTM identification:

• Sample preparation strategy:

  • Isolate native MJ0210.1 from M. jannaschii cultures

  • Prepare samples under conditions that preserve PTMs (avoid reducing agents for disulfide bonds, include phosphatase inhibitors)

  • Perform parallel analysis of recombinant protein as control

• MS analysis workflow:

  • Employ multiple proteolytic enzymes (trypsin, chymotrypsin, Glu-C) for improved sequence coverage

  • Perform LC-MS/MS using electron transfer dissociation (ETD) and higher-energy collisional dissociation (HCD)

  • Use neutral loss scanning to detect specific PTMs (phosphorylation, glycosylation)

• Data analysis approach:

  • Search against modified and unmodified peptide databases

  • Validate PTM sites using localization probability scores

  • Quantify modification stoichiometry

2. PTM-specific enrichment methods:

• Phosphorylation analysis:

  • Enrich phosphopeptides using titanium dioxide or immobilized metal affinity chromatography

  • Perform parallel dephosphorylation of controls with thermostable phosphatases

  • Use Phos-tag gels for mobility shift analysis

• Glycosylation analysis:

  • Enrich glycopeptides using hydrazide chemistry or lectin affinity

  • Characterize archaeal-specific glycans using permethylation analysis

  • Analyze site occupancy using PNGase F or similar glycosidases

• Methylation/acetylation analysis:

  • Use antibody-based enrichment for methylated/acetylated residues

  • Perform chemical derivatization to enhance detection

  • Compare modification patterns under different growth conditions

3. Functional impact assessment:

• Site-directed mutagenesis strategy:

  • Generate mutants of identified PTM sites (e.g., Ser→Ala, Lys→Arg)

  • Create phosphomimetic mutants (Ser→Asp/Glu) for phosphorylation sites

  • Assess impact on activity, stability, and binding properties

• Structural analysis:

  • Map PTM sites onto structural models

  • Analyze proximity to functional domains or binding interfaces

  • Perform molecular dynamics simulations to assess PTM impact on protein dynamics

4. Archaeal-specific PTM identification:

• Targeted analysis for known archaeal modifications:

  • N-terminal acetylation

  • Protein methylation

  • Archaeal-specific glycosylation patterns

  • Thermostable disulfide bond formation

• Comparative analysis with related archaeal proteins:

  • Compare with PTM patterns in MJ0754 and other M. jannaschii proteins

  • Identify conserved modification sites across archaeal homologs

  • Connect modifications to extremophile adaptations

5. PTM dynamics analysis:

• Targeted quantification under varying conditions:

  • Compare PTM profiles at different growth phases

  • Assess PTM changes under stress conditions

  • Monitor temporal dynamics during adaptation to temperature shifts

This comprehensive approach integrates discovery proteomics with targeted validation and functional characterization to provide insights into the role of PTMs in regulating MJ0210.1 function under extreme conditions.

How can researchers trace the evolutionary history of MJ0210.1 across archaeal and bacterial domains?

Tracing the evolutionary history of MJ0210.1 across archaeal and bacterial domains requires a comprehensive phylogenetic approach combined with comparative genomics:

1. Homolog identification across domains:

• Sensitive sequence similarity searches:

  • Use PSI-BLAST with multiple iterations to detect distant homologs

  • Employ profile Hidden Markov Models (HMMER) for improved sensitivity

  • Include both archaeal and bacterial genome databases

• Domain architecture analysis:

  • Identify proteins with similar domain organization

  • Account for domain shuffling events

  • Compare with proteins like MJ0754 from M. jannaschii

• Structural homology detection:

  • Use structure-based alignment tools (DALI, FATCAT)

  • Identify proteins with similar folds despite low sequence identity

  • Incorporate structural information from related proteins

2. Phylogenetic reconstruction:

• Multiple sequence alignment strategy:

  • Use structure-aware alignment tools (PROMALS3D, T-Coffee)

  • Manual curation of alignments to ensure homologous positions

  • Mask highly variable regions for tree building

• Tree construction methodology:

  • Maximum likelihood methods with appropriate evolutionary models

  • Bayesian inference for confidence assessment

  • Test multiple models and perform model selection

• Tree interpretation:

  • Root trees using outgroups or midpoint rooting

  • Identify major clades and their taxonomic composition

  • Locate MJ0210.1 within the evolutionary context

3. Gene evolutionary pattern analysis:

• Detection of horizontal gene transfer:

  • Identify phylogenetic incongruence with species trees

  • Analyze genomic context conservation across lineages

  • Assess nucleotide composition biases indicative of HGT

• Gene duplication and loss patterns:

  • Reconcile gene trees with species trees

  • Map duplication and loss events across lineages

  • Identify orthologs versus paralogs

• Detection of non-orthologous gene displacement:

  • Compare genomic context across species

  • Identify functional replacements with different evolutionary origins

  • Analyze cases of convergent evolution

4. Selection pressure analysis:

• Evolutionary rate calculation:

  • Compute dN/dS ratios across lineages

  • Identify sites under positive or purifying selection

  • Compare evolutionary rates with functionally characterized homologs

• Coevolution analysis:

  • Identify coevolving residues within the protein

  • Detect correlated evolution with potential interaction partners

  • Map coevolving sites onto structural models

5. Functional inference from evolutionary patterns:

• Ancestral sequence reconstruction:

  • Infer ancestral sequences at key nodes

  • Express and characterize ancestral proteins

  • Trace functional evolution through geological time

• Conservation pattern analysis:

  • Map conservation scores onto structural models

  • Identify highly conserved patches indicative of functional sites

  • Compare with known functional sites in homologous proteins

This comprehensive evolutionary analysis will place MJ0210.1 in its proper phylogenetic context, reveal its evolutionary history across domains, and provide insights into its potential function based on evolutionary patterns.

What can comparative genomics reveal about the function of MJ0210.1 and its homologs in other extremophiles?

Comparative genomics offers powerful approaches to illuminate the function of uncharacterized proteins like MJ0210.1 by analyzing patterns across multiple genomes, particularly in extremophiles:

1. Genomic context conservation analysis:

• Synteny mapping across extremophiles:

  • Compare gene neighborhoods around MJ0210.1 homologs in thermophiles, halophiles, and acidophiles

  • Identify consistently co-localized genes that may function in the same pathway

  • Quantify genomic context conservation across evolutionary distances

• Operonic structure analysis:

  • Determine if MJ0210.1 is part of operons in different species

  • Compare operon structures across extremophiles

  • Identify regulatory elements associated with co-transcribed genes

• Phylogenetic profiling:

  • Create presence/absence matrices of MJ0210.1 and other genes across species

  • Identify genes with correlated evolutionary patterns

  • Apply statistical measures to quantify correlation significance

2. Extremophile-specific adaptation analysis:

• Comparison across extremophile types:

  • Analyze sequence adaptations in thermophiles vs. psychrophiles

  • Compare halophilic vs. non-halophilic homologs

  • Identify convergent adaptations in different extremophile lineages

• Amino acid composition analysis:

  • Calculate amino acid frequencies in MJ0210.1 homologs

  • Compare with composition trends known in extremophiles

  • Identify extremophile-specific signatures

• Structural feature comparison:

  • Analyze charge distribution patterns across extremophile homologs

  • Compare hydrophobic core packing efficiency

  • Identify stabilizing elements specific to different extreme environments

3. Metabolic context integration:

• Pathway gap analysis:

  • Identify incomplete metabolic pathways in M. jannaschii and other extremophiles

  • Determine if MJ0210.1 homologs could fill specific functional gaps

  • Compare with pathway variants observed in the citric acid cycle

• Metabolic reconstruction comparison:

  • Compare full metabolic networks across extremophiles with and without MJ0210.1 homologs

  • Identify differentially present pathways

  • Correlate with growth capabilities and environmental adaptations

• Thermoadaptation of metabolic pathways:

  • Analyze how extremophiles modify standard metabolic pathways

  • Identify extremophile-specific enzymes or reactions

  • Determine if MJ0210.1 could participate in such adaptations

4. Non-orthologous gene displacement detection:

• Functional replacement identification:

  • Search for cases where MJ0210.1 homologs replace known functional genes

  • Compare with documented cases in the citric acid cycle

  • Validate predictions using available experimental data

• Domain architecture analysis:

  • Compare domain organizations across homologs

  • Identify fusion events that might suggest functional associations

  • Detect extremophile-specific domain arrangements

5. Correlation with phenotypic data:

• Growth condition correlation:

  • Correlate presence of MJ0210.1 homologs with growth temperature optima

  • Analyze association with other environmental parameters (pH, salinity)

  • Determine if homologs predict specific metabolic capabilities

• Experimental data integration:

  • Incorporate available transcriptomic and proteomic data

  • Analyze expression patterns under various stress conditions

  • Connect genomic presence to physiological responses

This comprehensive comparative genomics approach, similar to that used to analyze the citric acid cycle across species , will provide significant insights into the potential function of MJ0210.1 based on evolutionary patterns specific to extremophiles.

How can researchers use phylogenetic analysis to predict the functional importance of specific residues in MJ0210.1?

Phylogenetic analysis provides powerful tools for predicting functionally important residues in uncharacterized proteins like MJ0210.1. A comprehensive approach includes:

1. Conservation analysis across evolutionary depth:

• Hierarchical conservation mapping:

  • Create alignments at different taxonomic levels (genus, family, order, etc.)

  • Calculate position-specific conservation scores at each level

  • Identify residues conserved across all archaeal homologs versus domain-specific conservation

• Rate of evolution analysis:

  • Calculate site-specific evolutionary rates using maximum likelihood methods

  • Identify slowly evolving positions indicative of functional constraints

  • Compare rates across different clades to identify clade-specific constraints

• Conservation pattern visualization:

  • Map conservation scores onto structural models

  • Identify clusters of conserved residues on protein surface

  • Compare with known functional sites in related proteins like MJ0754

2. Detection of correlated mutations:

• Coevolution analysis:

  • Calculate mutual information between all residue pairs

  • Apply statistical corrections for background signal

  • Identify networks of coevolving residues using tools like GREMLIN or EVcouplings

• Structural interpretation:

  • Map coevolving residue networks onto 3D structure

  • Identify residues that maintain structural integrity

  • Distinguish between structural and functional coevolution

• Sector analysis:

  • Identify semi-independent groups of coevolving residues

  • Connect different sectors to potential functional aspects

  • Compare with known protein sectors in characterized homologs

3. Analysis of selective pressure:

• Site-specific dN/dS analysis:

  • Apply codon-based models to calculate residue-specific dN/dS ratios

  • Identify sites under positive selection (potential adaptation)

  • Detect sites under strong purifying selection (functional constraint)

• Lineage-specific selection analysis:

  • Implement branch-site models to identify residues under selection in specific lineages

  • Compare hyperthermophilic versus mesophilic lineages

  • Identify residues potentially involved in thermoadaptation

• Radical versus conservative substitution analysis:

  • Analyze biochemical properties of observed substitutions

  • Identify positions that maintain physicochemical properties despite substitutions

  • Detect positions with property-changing substitutions in specific lineages

4. Ancestral sequence reconstruction and analysis:

• Inference of ancestral states:

  • Reconstruct sequences at key ancestral nodes

  • Identify residue changes along specific lineages

  • Correlate changes with adaptation to different environments

• Resurrection of ancestral proteins:

  • Express and characterize reconstructed ancestral proteins

  • Test functional hypotheses experimentally

  • Determine the impact of specific historical substitutions

5. Integration with structure-function predictions:

• Active site prediction:

  • Combine evolutionary data with structural information

  • Identify putative catalytic residues based on conservation and positioning

  • Compare with known enzyme active sites

• Substrate binding site prediction:

  • Detect surface patches with evolutionary constraints

  • Identify potential ligand-binding residues

  • Design mutagenesis experiments to test functional predictions

This integrated phylogenetic approach provides a powerful framework for prioritizing residues for experimental characterization, generating testable hypotheses about protein function, and understanding the evolutionary forces that shaped MJ0210.1.