Recombinant Thermotoga maritima Uncharacterized protein TM_0562.1 (TM_0562.1)
Description
Introduction to Recombinant Thermotoga maritima Uncharacterized Protein TM_(TM_0562.1)
Recombinant Thermotoga maritima Uncharacterized Protein TM_0562.1 (TM_0562.1) is a protein derived from the hyperthermophilic bacterium Thermotoga maritima. This bacterium is known for its ability to thrive in extremely high temperatures, typically between 60°C and 90°C, with optimal growth at 80°C . The protein TM_0562.1 is part of a broader class of uncharacterized proteins within T. maritima, which have garnered interest due to their potential applications in biotechnology and industrial processes.
Characteristics of Thermotoga maritima
Thermotoga maritima is a rod-shaped bacterium isolated from deep-sea hydrothermal vents. It is renowned for producing thermostable enzymes, such as esterases, lipases, and amylases, which are highly efficient in high-temperature industrial processes . The molecular mechanisms underlying its hyperthermostability are not fully understood but are believed to involve complex regulatory systems, including small non-coding RNAs (ncRNAs) .
Data Table: Characteristics of Thermotoga maritima Proteins
Future Directions
Further research is needed to fully characterize TM_0562.1 and explore its potential applications. This could involve structural studies to understand its fold and potential enzymatic activity, as well as functional assays to determine its role in T. maritima or its utility in biotechnological processes.
Product Specs
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Target Background
Q&A
What is Thermotoga maritima and why is it significant for research?
Thermotoga maritima is a hyperthermophilic rod-shaped bacterium isolated from hydrothermal vents that can grow between 60-90°C with optimal growth at 80°C. This organism has garnered significant attention in the scientific community for several reasons. It possesses the potential to produce thermostable commercial enzymes that can be utilized for saccharification of plant biomass for subsequent fermentation to bioproducts. The hyperthermophilic nature of T. maritima makes it particularly valuable for understanding mechanisms underlying extreme thermal stability in proteins. Additionally, it has been a focus of evolutionary biologists because it harbors characteristics of early evolutionary lineages. T. maritima produces various industrially important biocatalysts including esterases, lipases, and amylases, which demonstrate extremely high thermal stability and efficiency compared to other enzymes in high-temperature industrial processes .
The molecular mechanisms involved in the hyperthermostability of T. maritima remain incompletely understood despite significant research efforts combining computational and experimental approaches. This ongoing challenge makes T. maritima and its proteins, including uncharacterized ones like TM_0562.1, subjects of active investigation in both fundamental and applied research contexts.
What is currently known about the uncharacterized protein TM_0562.1?
The protein TM_0562.1 from Thermotoga maritima (strain ATCC 43589 / MSB8 / DSM 3109 / JCM 10099) remains largely uncharacterized, with the following basic information available:
| Feature | Information |
|---|---|
| UniProt Accession | P58008 |
| Gene Name | TM_0562.1 |
| Protein Length | 192 amino acids |
| Expression Region | 1-192 (full length) |
| Recommended Name | Uncharacterized protein TM_0562.1 |
The amino acid sequence of TM_0562.1 is: mLVKEREEKLNRVLVALLGIPVVFSIIRAKIVETIGYFIFWLGGFSPYVYEKITHQEIPE RTKLmLSSSVFLHSVMGQFLNFYEKIFFWDKILHFYGSFVITYFFYQILTKKSRFWDEVP GAVLMAFLLGVFSGVLWEIAEFTTDKILPDYNTQKGLDDTmLDLIFDLLGCYTMAKIVYR KKTGRFFWRPRS .
What expression systems are appropriate for studying TM_0562.1?
Selecting the optimal expression system for recombinant TM_0562.1 requires considering the protein's characteristics and experimental objectives. Several expression systems can be employed, each with distinct advantages:
| Expression System | Advantages | Limitations | Application Suitability |
|---|---|---|---|
| E. coli | - High yield - Cost-effective - Well-established protocols - Compatible with thermostable proteins | - Potential inclusion body formation - Limited post-translational modifications - Challenges with membrane proteins | Initial characterization studies, structural analysis requiring high protein amounts |
| Yeast (S. cerevisiae, P. pastoris) | - Better protein folding - Post-translational modifications - Secretion capability - Eukaryotic processing | - Lower yield than E. coli - Longer expression timeframes - More complex media requirements | Functional studies requiring proper folding, studies of protein-protein interactions |
| Mammalian cells | - Native-like protein processing - Complete post-translational modifications - Membrane protein expression | - Expensive - Low yields - Technical complexity | Specialized functional studies, interaction studies in eukaryotic context |
| Cell-free systems | - Rapid expression - Direct access to reaction conditions - Suitable for toxic proteins | - Scaling limitations - Higher cost - Short reaction duration | Preliminary functional assessment, rapid mutation screening |
For TM_0562.1, which appears to be a membrane protein based on sequence analysis, E. coli expression with specialized strains (C41/C43) designed for membrane proteins would be a reasonable starting point . If initial expression attempts face challenges with protein folding or solubility, transitioning to a eukaryotic system might be warranted. The selection should be guided by the specific research questions and downstream applications (structural studies, functional characterization, or interaction analyses).
What are the optimal storage conditions for recombinant TM_0562.1?
Maintaining the stability and activity of recombinant TM_0562.1 requires appropriate storage conditions, especially considering its thermophilic origin. Based on information about similar T. maritima proteins, the following storage recommendations can be made:
| Storage Form | Conditions | Buffer Composition | Duration |
|---|---|---|---|
| Lyophilized powder | -20°C to -80°C | N/A | Long-term (years) |
| Concentrated solution | -20°C or -80°C | Tris-based buffer with 50% glycerol | Medium to long-term (months) |
| Working solution | 4°C | Application-specific buffer | Short-term (days to a week) |
For membrane proteins like TM_0562.1, additional considerations include:
-
Inclusion of appropriate detergents at concentrations above their critical micelle concentration to maintain solubility
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Avoidance of repeated freeze-thaw cycles, which can lead to protein denaturation and aggregation
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Aliquoting of purified protein to minimize freeze-thaw events
While T. maritima proteins typically demonstrate exceptional stability due to their thermophilic nature, proper storage conditions remain essential for maintaining structural integrity and biological activity during research applications. The remarkable thermostability of these proteins may allow for less stringent storage conditions than required for mesophilic proteins, but standard protein storage practices should still be followed for optimal results.
What approaches can be used to characterize the function of TM_0562.1?
Characterizing the function of the uncharacterized protein TM_0562.1 requires a multi-faceted approach integrating computational, structural, and experimental methodologies:
| Approach Category | Specific Methods | Expected Outcomes |
|---|---|---|
| Bioinformatic Analysis | - Homology detection (BLAST, HHpred) - Structural prediction (AlphaFold2) - Genomic context analysis - Domain and motif prediction | - Potential homologs - Predicted structure - Functional associations - Conserved functional elements |
| Transcriptomic Analysis | - RNA-seq under various conditions - Co-expression network analysis - Promoter analysis | - Expression patterns - Co-regulated genes - Regulatory mechanisms |
| Genetic Approaches | - Gene deletion/knockdown - Complementation studies - Suppressor analysis | - Phenotypic effects - Essentiality assessment - Genetic interactions |
| Proteomic Methods | - Interaction proteomics (Co-IP, BioID) - Protein localization - Post-translational modification analysis | - Protein partners - Subcellular context - Regulatory modifications |
| Biochemical Characterization | - Substrate screening - Activity assays based on predictions - Ligand binding studies | - Enzymatic function - Binding partners - Biochemical parameters |
| Structural Biology | - X-ray crystallography/Cryo-EM - Hydrogen-deuterium exchange MS - NMR for dynamics | - 3D structure - Conformational changes - Dynamic properties |
For TM_0562.1 specifically, the characterization workflow might begin with in-depth sequence analysis and structural prediction to generate hypotheses about its function. Given its apparent membrane-associated nature, methods adapted for membrane proteins would be essential, such as specialized detergent-based purification protocols and membrane-specific interaction analyses. The integration of data from multiple approaches is crucial, as no single method is likely to provide a complete functional characterization.
How should I design experiments to study the potential role of TM_0562.1 in stress adaptation?
Investigating the potential role of TM_0562.1 in stress adaptation requires a systematic experimental design that addresses both expression regulation and functional characterization:
| Experimental Phase | Approaches | Controls | Data Analysis |
|---|---|---|---|
| Expression Analysis | |||
| Temperature stress | - Growth at 60°C, 80°C, 90°C - RT-qPCR for TM_0562.1 - Western blot if antibodies available | - Constitutive genes - Known temperature-responsive genes | - Fold change calculation - Statistical significance testing |
| Oxidative stress | - H₂O₂ exposure (various concentrations) - RNA-seq analysis | - Untreated cultures - Antioxidant enzyme genes | - Differential expression analysis - Pathway enrichment |
| Nutrient limitation | - Carbon, nitrogen restriction - Targeted expression analysis | - Rich media controls - Metabolic regulator genes | - Time-course expression patterns - Correlation with growth phases |
| Genetic Manipulation | |||
| Gene deletion | - Homologous recombination - CRISPR-Cas9 approaches - Phenotypic characterization | - Wild-type strain - Complemented mutant | - Growth curve analysis - Stress survival ratios - Metabolic profiling |
| Protein localization | - GFP/tag fusion constructs - Fractionation studies | - Empty vector - Known localization markers | - Microscopy image analysis - Quantitative distribution |
| Functional Assessment | |||
| Membrane integrity | - Permeability assays - Lipid composition analysis | - Wild-type under same conditions - Known membrane proteins | - Statistical comparison - Correlation with stress conditions |
| Protein interactions | - Pull-down under stress conditions - Crosslinking mass spectrometry | - Non-stress conditions - Irrelevant protein controls | - Differential interaction networks - Enrichment analysis |
This experimental framework should systematically test the hypothesis that TM_0562.1 contributes to stress adaptation in T. maritima. The approach combines transcriptional regulation studies with functional characterization to establish both correlation and causation between protein function and stress response. Data interpretation should consider the unique context of T. maritima as a hyperthermophile, where baseline "stress" conditions differ from mesophilic organisms.
What bioinformatic approaches can predict the structure and function of TM_0562.1?
Predicting the structure and function of an uncharacterized protein like TM_0562.1 requires sophisticated bioinformatic approaches that can extract information from limited data:
For TM_0562.1, given its apparent membrane protein characteristics, special emphasis should be placed on tools optimized for membrane protein analysis. The integration of these diverse prediction methods can provide complementary insights that collectively generate testable hypotheses about the protein's structure and function, even in the absence of direct experimental data.
How does the amino acid composition of TM_0562.1 contribute to thermostability?
The amino acid composition and sequence features of TM_0562.1 contribute to its thermostability through several mechanisms typical of proteins from hyperthermophilic organisms:
| Thermostability Feature | Observation in TM_0562.1 | Biophysical Contribution |
|---|---|---|
| Charged amino acid content | Enriched in Arg (R), Lys (K), Glu (E) | - Increased ionic interactions - Formation of stabilizing salt bridges - Enhanced electrostatic networks |
| Hydrophobic core | High content of Ile (I), Leu (L), Val (V), Phe (F) | - Strengthened hydrophobic interactions - Compact core packing - Reduced solvent accessibility |
| Proline residues | Strategic Pro residues in loop regions | - Reduced backbone flexibility - Entropy reduction - Conformational rigidity |
| Glycine reduction | Limited glycine content in structured regions | - Decreased conformational flexibility - Reduced backbone entropy - Structural rigidity |
| Secondary structure propensity | High alpha-helical propensity | - Optimized hydrogen bonding - Stabilized secondary structures - Reduced unfolding susceptibility |
| Surface properties | Optimized surface charge distribution | - Favorable surface interactions - Reduced surface area to volume ratio - Solvent-accessible stabilization |
What controls should be included when studying the biochemical properties of TM_0562.1?
Rigorous experimental design for studying TM_0562.1 requires appropriate controls to ensure valid and interpretable results:
| Control Type | Specific Controls | Purpose | Implementation |
|---|---|---|---|
| Positive Controls | |||
| Known T. maritima proteins | - Well-characterized membrane proteins - Proteins from the same family if known | - Validate experimental conditions - Provide reference behaviors | Include in parallel experiments |
| Thermostability benchmarks | - Proteins with known melting temperatures - Enzymes with known temperature optima | - Calibrate thermal measurements - Validate assay conditions | Use as reference standards |
| Negative Controls | |||
| Expression system controls | - Empty vector preparations - Irrelevant protein (e.g., GFP) | - Account for host contamination - Control for non-specific effects | Process identically to TM_0562.1 |
| Buffer controls | - Buffer-only samples - Detergent-only samples | - Establish baseline signals - Control for buffer/detergent effects | Include in all measurements |
| Experimental Controls | |||
| Temperature series | - Measurements at 25°C, 40°C, 60°C, 80°C, 95°C | - Establish temperature dependence - Identify optimal conditions | Perform identical procedures at each temperature |
| pH variations | - Measurements across pH range (5-9) | - Determine pH effects - Optimize conditions | Use consistent buffer systems |
| Technical Controls | |||
| Technical replicates | - Multiple measurements of same sample | - Assess measurement precision - Quantify technical variability | Minimum triplicate measurements |
| Biological replicates | - Independent protein preparations - Different expression batches | - Account for preparation variability - Ensure reproducibility | Minimum three independent preparations |
| Specificity Controls | |||
| Mutagenesis | - Alanine substitutions of key residues - Conservative mutations | - Validate functional predictions - Control for structural disruption | Generate and test multiple variants |
| Domain truncations | - Systematic domain removal - Isolated domains | - Map functional regions - Identify minimal functional units | Express and test truncated constructs |
For membrane proteins like TM_0562.1, additional controls should address membrane-specific considerations, such as reconstitution into liposomes of defined composition to control for lipid environment effects and detergent screening to identify optimal solubilization conditions without compromising function.
How can I validate predicted functions of TM_0562.1 using wet-lab approaches?
Validating bioinformatically predicted functions of TM_0562.1 requires a comprehensive experimental strategy that combines multiple approaches:
| Validation Category | Methodological Approaches | Expected Outcomes | Analysis Methods |
|---|---|---|---|
| Genetic Validation | |||
| Gene knockout/knockdown | - CRISPR-Cas9 or homologous recombination - Conditional expression systems | - Phenotypic consequences - Growth defects under specific conditions | - Comparative growth analysis - Metabolic profiling |
| Complementation studies | - Expression in mutant strains - Cross-species complementation | - Functional rescue - Conservation of function | - Statistical comparison to wild-type - Phenotype recovery metrics |
| Suppressor screening | - Suppressor mutation identification - Genetic interaction mapping | - Functional pathways - Compensatory mechanisms | - Next-gen sequencing analysis - Genetic network construction |
| Biochemical Validation | |||
| Direct activity testing | - Substrate screening based on predictions - Enzyme kinetics if catalytic | - Substrate specificity - Kinetic parameters | - Michaelis-Menten analysis - Activity comparison across conditions |
| Binding assays | - Surface plasmon resonance - Microscale thermophoresis - Isothermal titration calorimetry | - Binding affinities - Interaction specificity - Thermodynamic parameters | - Binding curve fitting - Thermodynamic analysis |
| Structural Validation | |||
| Structural determination | - X-ray crystallography - Cryo-EM - NMR for dynamics | - 3D structure confirmation - Binding site verification - Conformational changes | - Structure-function correlation - Comparison with predictions |
| Mutational analysis | - Structure-guided mutagenesis - Activity testing of mutants | - Critical residue identification - Mechanism insights | - Comparative activity analysis - Structure-based interpretation |
| Systems-level Validation | |||
| Transcriptomics/proteomics | - RNA-seq of knockout strains - Comparative proteomics | - Global effects of protein absence - Regulatory networks | - Differential expression analysis - Pathway enrichment |
| In vivo localization | - Fluorescent protein fusions - Immunolocalization | - Subcellular location - Temporal dynamics | - Microscopy image analysis - Co-localization studies |
For TM_0562.1, the validation strategy should prioritize methods compatible with membrane proteins, such as detergent-based activity assays or reconstitution into membrane mimetics. The validation process should be iterative, with each experimental result informing the design of subsequent experiments and refining the functional model.
What techniques are most suitable for studying protein-protein interactions involving TM_0562.1?
Investigating protein-protein interactions for membrane proteins like TM_0562.1 requires specialized approaches that maintain the membrane environment or accommodate hydrophobic surfaces:
| Technique | Implementation for TM_0562.1 | Advantages | Limitations |
|---|---|---|---|
| Affinity-based Methods | |||
| Pull-down assays | - His-tagged TM_0562.1 as bait - Detergent-solubilized membrane preparations | - Direct physical interaction - Compatible with membrane proteins - Can identify novel partners | - Detergent effects on interactions - Potential loss of weak interactions - Background binding issues |
| Co-immunoprecipitation | - Antibodies against TM_0562.1 or epitope tags - Crosslinking prior to solubilization | - More physiological conditions - Can detect native complexes | - Requires specific antibodies - Membrane protein solubilization challenges |
| Proximity-based Methods | |||
| Crosslinking mass spectrometry | - Chemical crosslinkers (DSS, BS3) - Photoactivatable crosslinkers for membrane regions | - Captures interactions in native environment - Can identify interaction interfaces - Works with transient interactions | - Complex data analysis - Potential for false positives - Crosslinker accessibility bias |
| BioID/TurboID | - TM_0562.1 fused to biotin ligase - Expression in T. maritima or heterologous system | - Maps proximal proteins in vivo - No solubilization needed before labeling - Detects weak/transient interactions | - May identify proximal but non-interacting proteins - Requires genetic manipulation |
| Biophysical Methods | |||
| Microscale Thermophoresis | - Fluorescently labeled TM_0562.1 - Titration with potential partners in detergent | - Low sample consumption - Works in solution - Label on single position | - Requires purified components - Potential detergent interference - Labeling may affect interactions |
| Surface Plasmon Resonance | - Immobilized TM_0562.1 in lipid nanodiscs - Flow of potential interacting partners | - Label-free detection - Real-time kinetics - Quantitative affinity measurement | - Surface immobilization challenges - Flow system limitations - Nanodisc/detergent compatibility |
| Genetic Methods | |||
| Bacterial Two-Hybrid | - TM_0562.1 fusions to split reporter fragments - Screen against genomic library | - In vivo detection - High-throughput capability - No protein purification needed | - Modified for membrane proteins - False negatives with membrane proteins - Expression level variations |
| Genetic suppressor screening | - TM_0562.1 mutation/deletion - Selection for compensatory mutations | - Physiologically relevant - Can reveal functional relationships | - Indirect evidence of interaction - Labor intensive - Requires genetic system |
For TM_0562.1, a multi-method approach is recommended, starting with proximity labeling or crosslinking to identify potential interaction partners in their native environment, followed by targeted validation using biophysical methods. The extreme growth conditions of T. maritima (80°C optimal temperature) present additional challenges that may require adaptation of standard protocols or the use of thermostable variants of interaction detection systems.
How do I interpret discrepancies between in silico predictions and experimental results for TM_0562.1?
Resolving discrepancies between computational predictions and experimental data for TM_0562.1 requires systematic analysis and consideration of multiple factors:
| Discrepancy Type | Potential Causes | Resolution Strategies | Interpretation Framework |
|---|---|---|---|
| Functional Discrepancies | |||
| Predicted vs. observed activity | - Novel function in thermophiles - Moonlighting protein - Incomplete annotations in databases | - Expanded functional testing - Multiple complementary assays - Context-dependent activity testing | - Consider T. maritima-specific biology - Evaluate evidence strength hierarchy - Assess evolutionary context |
| Gene essentiality mismatch | - Conditional essentiality - Functional redundancy - Adaptation to laboratory conditions | - Testing under multiple conditions - Double knockout studies - Growth competition assays | - Define phenotypic spectrum - Consider growth condition relevance - Evaluate fitness vs. essentiality |
| Structural Discrepancies | |||
| Predicted vs. experimental structure | - Limitations in modeling algorithms - Membrane environment effects - Temperature-dependent conformations | - Refinement with experimental constraints - Membrane-specific modeling - Structure determination at different temperatures | - Quantify structural differences (RMSD) - Identify discrepant regions - Assess functional relevance of differences |
| Domain boundary differences | - Incorrect domain prediction - Thermophilic adaptation effects - Unique structural elements | - Limited proteolysis validation - Domain expression studies - Thermal stability of isolated domains | - Re-evaluate domain definitions - Consider thermophile-specific domains - Integrate multiple structural evidence |
| Interaction Discrepancies | |||
| Predicted vs. observed interactions | - Temperature-dependent interactions - Membrane environment effects - Method-specific limitations | - Multiple detection methods - Temperature-dependent interaction studies - Native membrane environment preservation | - Prioritize direct physical evidence - Consider detection method biases - Evaluate interaction specificity |
| Network position differences | - T. maritima-specific pathways - Incomplete interactome data - Method sensitivity limits | - Pathway-focused validation - Directed protein-protein interaction testing - Functional relationship testing | - Integrate multiple data types - Apply network topology analysis - Consider evolutionary conservation |
A systematic approach to resolving these discrepancies might include:
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Critically evaluating the quality and confidence of both computational predictions and experimental data
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Considering T. maritima's unique biology as a hyperthermophile when interpreting results
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Designing targeted experiments to specifically address major discrepancies
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Developing an integrated model that accommodates both computational and experimental evidence
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Remaining open to novel functions that may not be predicted based on mesophilic homologs
What statistical approaches are appropriate for analyzing thermal stability data for TM_0562.1?
Analyzing thermal stability data for TM_0562.1 requires statistical methods suitable for thermodynamic and kinetic measurements:
| Data Type | Statistical Methods | Visualization Approaches | Interpretation Guidelines |
|---|---|---|---|
| Thermal Denaturation | |||
| Thermal shift assays | - Non-linear regression (Boltzmann sigmoid) - Comparison of Tm values (ANOVA) - Confidence interval calculation | - Thermal denaturation curves - ΔTm bar charts - Thermal stability maps | - Evaluate curve fit quality (R²) - Consider cooperativity of unfolding - Compare relative vs. absolute stability |
| Differential scanning calorimetry | - Peak deconvolution - van't Hoff analysis - Multiple transition modeling | - Thermogram plots - Enthalpy-temperature profiles - Domain stability comparison | - Identify distinct transitions - Calculate thermodynamic parameters - Correlate with structural domains |
| Circular dichroism | - Secondary structure calculation - Temperature-dependent changes - Statistical comparison of transition points | - Wavelength scans - Temperature melt curves - Secondary structure proportion plots | - Analyze unfolding intermediates - Compare secondary structure elements - Evaluate reversibility |
| Activity vs. Temperature | |||
| Enzyme kinetics | - Arrhenius plot analysis - Temperature optimum determination - Activation energy calculation | - Temperature-activity profiles - Arrhenius plots - Temperature coefficient (Q10) plots | - Compare Ea values - Identify temperature breakpoints - Assess kinetic stability |
| Binding assays | - Temperature-dependent affinity analysis - van't Hoff plots - Thermodynamic parameter calculation | - Affinity-temperature plots - Thermodynamic parameter plots - Comparative binding profiles | - Calculate ΔH, ΔS, ΔG - Evaluate entropic/enthalpic contributions - Assess temperature optimum |
| Comparative Analysis | |||
| Mutant analysis | - Multiple comparison testing (ANOVA with post-hoc) - Multiple linear regression - Principal component analysis | - Stability heatmaps - Structure-stability correlation plots - Mutation effect matrices | - Identify stabilizing/destabilizing mutations - Map effects to structure - Quantify additive/non-additive effects |
| Condition effects | - Two-way ANOVA (temperature × condition) - Response surface methodology - Interaction term analysis | - Condition-temperature contour plots - Interaction plots - Stability landscape maps | - Identify synergistic/antagonistic effects - Determine optimal condition combinations - Model stability across condition space |
For rigorous analysis, all experiments should include:
-
Minimum of three biological replicates per condition
-
Appropriate technical replicates for each measurement
-
Statistical power analysis to ensure adequate sample size
-
Normalization procedures appropriate to the specific assay
-
Careful consideration of the non-independence of temperature series data
These statistical approaches allow for robust characterization of TM_0562.1's thermal properties and provide a quantitative framework for comparing it with other proteins or mutant variants.
