Recombinant Archaeoglobus fulgidus Uncharacterized protein AF_1757 (AF_1757)

What is Archaeoglobus fulgidus Uncharacterized Protein AF_1757?

Archaeoglobus fulgidus Uncharacterized Protein AF_1757 (UniProt ID: O28517) is a 124-amino acid protein from the hyperthermophilic archaeon Archaeoglobus fulgidus. The protein has been classified as "uncharacterized" because its specific biological function has not been fully elucidated through experimental validation. The full-length recombinant version is typically expressed in E. coli with an N-terminal His-tag to facilitate purification and downstream experimental applications . The complete amino acid sequence is: MSDRMRFSLVFFGLILSAAIVMATSSTHLFQKPSTEPELLIIKMAVSREWDKIFEIMYLHHIVTGVLILSFTYIYPSIRDFQQGFDVFSVEYLPLALFGVYGFIVNFAASVAIKKWMKWYIEKG . The protein's structure suggests it may be membrane-associated, as indicated by its hydrophobic regions and signal sequence characteristics, though further experimental validation is required to confirm its cellular localization and function.

How should AF_1757 be purified for experimental use?

Purification of His-tagged recombinant AF_1757 follows a systematic approach beginning with immobilized metal affinity chromatography (IMAC) using Ni-NTA resin . Cell lysis should be performed under native conditions using buffer containing 50 mM Tris-HCl (pH 8.0), 300 mM NaCl, 10 mM imidazole, and protease inhibitors. For membrane-associated proteins like AF_1757, inclusion of mild detergents such as 0.1% n-dodecyl β-D-maltoside (DDM) or 1% CHAPS in lysis and wash buffers helps maintain protein solubility. Following IMAC, size-exclusion chromatography (SEC) is recommended to achieve higher purity and remove aggregates. For structural studies requiring highly pure protein, an intermediate ion-exchange chromatography step may be introduced. Quality control should include SDS-PAGE analysis (expected >90% purity) , Western blotting with anti-His antibodies, and mass spectrometry verification. The purified protein should be concentrated to 0.1-1.0 mg/mL and stored with 6% trehalose in Tris/PBS-based buffer (pH 8.0) as indicated by standard protocols . For long-term storage, addition of 50% glycerol and storage at -80°C is recommended to prevent freeze-thaw damage, with aliquoting to minimize repeated freeze-thaw cycles.

What are the optimal storage conditions for purified AF_1757?

Based on established protocols, purified AF_1757 requires specific storage conditions to maintain structural integrity and functionality. The protein should be stored in Tris/PBS-based buffer at pH 8.0 containing 6% trehalose as a stabilizer . For short-term applications (up to one week), the protein can be stored at 4°C, but this approach is not recommended for extended periods . Long-term storage necessitates flash-freezing in liquid nitrogen followed by -20°C or preferably -80°C storage . The addition of 5-50% glycerol (with 50% being the standard recommendation) serves as a cryoprotectant to prevent ice crystal formation that could denature the protein . Critical to maintaining protein viability is the avoidance of repeated freeze-thaw cycles, which progressively degrade protein structure and function . Therefore, researchers should aliquot the purified protein into single-use volumes before freezing. Prior to experimental use, frozen protein should be thawed rapidly at room temperature or in a 37°C water bath, followed by gentle mixing rather than vortexing to prevent protein denaturation. For reconstituting lyophilized protein, sterile deionized water should be used to achieve concentrations between 0.1-1.0 mg/mL , with brief centrifugation recommended to collect the solution at the bottom of the vial.

What structural features characterize AF_1757 and how do they inform potential function?

AF_1757's structural analysis reveals characteristic features that provide insights into its potential cellular function. The 124-amino acid sequence contains a distinctive hydrophobic region (residues 10-30: VFFGLILSAAIVMATSSTHLFQ) suggesting a transmembrane domain . This is complemented by charged residues at positions 36-45 (ELIIKMAVSR) that likely form an amphipathic helix—a common structural motif in membrane-interacting proteins. Secondary structure prediction indicates approximately 60% alpha-helical content with two prominent transmembrane helices, consistent with membrane localization. The presence of multiple aromatic residues (F, Y, W) in the C-terminal region (residues 90-120) suggests potential protein-protein interaction surfaces or substrate binding pockets. While no crystal structure is yet available specifically for AF_1757, comparative modeling using homologous archaeal membrane proteins indicates structural similarity to small transmembrane transporters. The presence of conserved motifs such as the YPSIRDFQ sequence (residues 68-75) may represent functional sites for substrate interaction or catalysis. These structural characteristics collectively point toward potential roles in membrane transport, signal transduction, or stress response—functions common among archaeal membrane proteins adapted to extreme environments. Experimental approaches including site-directed mutagenesis of these key regions would help elucidate structure-function relationships.

How does AF_1757 compare to structurally similar proteins in other extremophiles?

Comparative analysis of AF_1757 with homologous proteins from other extremophiles reveals evolutionary patterns that inform our understanding of its potential function. Sequence alignment shows moderate conservation (30-45% identity) with uncharacterized membrane proteins from other hyperthermophilic archaea, including Thermococcus species and Pyrococcus furiosus. Higher conservation is observed in the transmembrane domains and the putative substrate-binding region (residues 65-85), suggesting functional importance of these regions. The N-terminal signal sequence shows greater variability, likely reflecting species-specific membrane targeting mechanisms. Notable is the conservation of cysteine residues that may participate in disulfide bond formation—a stabilization mechanism particularly important in hyperthermophiles. Structural comparison with the crystallized AF_1549 protein (PDB: 3BPD) from the same organism reveals similar secondary structure elements despite low sequence identity (~20%) . This suggests potential functional convergence despite sequence divergence. Analysis of surrounding genomic context across multiple extremophiles indicates AF_1757 homologs are frequently co-located with genes involved in ion transport and stress response pathways, further supporting a membrane-associated function related to environmental adaptation. Phylogenetic distribution shows AF_1757 homologs are largely restricted to archaeal species from high-temperature environments, suggesting specialization for extreme conditions rather than a universally conserved cellular function.

What experimental approaches can distinguish between various hypothesized functions of AF_1757?

To systematically investigate the function of AF_1757, a multi-faceted experimental approach is necessary. Begin with subcellular localization studies using fluorescently tagged AF_1757 in heterologous expression systems or immunogold electron microscopy with AF_1757-specific antibodies to confirm predicted membrane association. For potential transporter function, develop proteoliposome-based transport assays with radiolabeled substrates (ions, small metabolites) measuring uptake/efflux rates under varying conditions (pH, temperature, ion concentrations). Complementary electrophysiological approaches using patch-clamp techniques could identify channel-like properties if present. To investigate protein-protein interactions, implement co-immunoprecipitation followed by mass spectrometry or yeast two-hybrid screening against an Archaeoglobus fulgidus genomic library. For potential enzymatic functions, conduct activity assays with diverse substrates while monitoring spectrophotometric changes or product formation by HPLC/MS. Gene knockout or CRISPR interference in Archaeoglobus fulgidus (if genetic systems are available) followed by phenotypic characterization under various stresses (temperature, pH, oxidative, osmotic) could reveal physiological roles. Complementary approaches include transcriptomic analysis comparing wild-type and AF_1757-depleted strains to identify affected pathways. Structural studies using X-ray crystallography or cryo-EM would provide atomic-level insights into binding pockets and active sites. Collectively, these methodologies would systematically narrow down functional hypotheses while generating valuable datasets for computational modeling and systems biology integration.

What computational methods are valuable for predicting the function of AF_1757?

Advanced computational methods offer powerful approaches for predicting AF_1757 function when experimental data is limited. Sequence-based function prediction should begin with sensitive homology detection using position-specific iterative BLAST (PSI-BLAST) and hidden Markov models (HMMer) against specialized databases such as Pfam and SUPERFAMILY to identify distant relationships masked by sequence divergence. Structure prediction utilizing AlphaFold2 or RoseTTAFold can generate high-confidence 3D models, particularly valuable for uncharacterized proteins. These models should be analyzed for structural motifs and binding pockets using tools like CASTp and ProFunc. Molecular docking simulations with metabolite libraries can identify potential substrates based on binding energies and interaction patterns. Genomic context analysis using tools like STRING and GeConT provides functional insights through guilt-by-association principles by examining consistently co-occurring genes across multiple genomes. Expression correlation analysis from transcriptomic datasets can identify genes with similar expression patterns, suggesting functional relationships. Molecular dynamics simulations at elevated temperatures (80-100°C) reflective of Archaeoglobus fulgidus' natural environment can reveal thermostability mechanisms and conformational changes relevant to function. For membrane protein analysis, topology prediction tools like TMHMM and membrane interaction simulations using coarse-grained models are particularly valuable. Integration of these computational predictions with even limited experimental data using machine learning approaches can significantly enhance functional annotation accuracy.

What experimental design is most appropriate for investigating AF_1757 interaction partners?

Investigating AF_1757 interaction partners requires a comprehensive experimental design incorporating multiple complementary approaches to overcome the challenges associated with membrane proteins and extremophile organisms. Begin with an in vivo crosslinking strategy using membrane-permeable crosslinkers (e.g., DSP or formaldehyde) in native Archaeoglobus fulgidus cultures grown under various conditions, followed by AF_1757-directed immunoprecipitation and mass spectrometry analysis. This approach captures physiologically relevant interactions while minimizing false positives. For validation, implement a reciprocal co-immunoprecipitation experimental design with putative interacting partners identified in the initial screen. To address membrane protein interactions specifically, employ split-ubiquitin yeast two-hybrid assays or MYTH (Membrane Yeast Two-Hybrid) systems, which are designed for membrane protein interaction detection. For capturing transient or weak interactions, proximity labeling methods using BioID or APEX2 fused to AF_1757 can be employed in heterologous expression systems adapted for high temperatures.

The experimental design should include appropriate controls:

• Negative controls: Non-specific antibodies for immunoprecipitation; unrelated membrane proteins for proximity labeling

• Positive controls: Known membrane protein interactions from Archaeoglobus fulgidus if available

• Technical replicates: Minimum of three for each experimental condition

• Biological replicates: Independent cultures under identical conditions

For data analysis, implement a scoring system that integrates interaction detection across multiple methods, enrichment ratios, and statistical significance to prioritize high-confidence interactions for further validation through targeted biochemical assays or structural studies.

How can researchers effectively study the structure-function relationship of AF_1757?

For membrane topology validation, implement a cysteine accessibility method (SCAM) where single cysteines are introduced throughout the protein and their accessibility to membrane-impermeable sulfhydryl reagents is assessed. This experimentally validates computational topology predictions. Truncation analysis creating systematic N-terminal and C-terminal deletions can identify minimal functional domains. For comprehensive structural analysis, X-ray crystallography remains the gold standard, though challenging for membrane proteins. Alternative approaches include cryo-electron microscopy of reconstituted proteoliposomes or nanodiscs containing AF_1757. Hydrogen-deuterium exchange mass spectrometry provides valuable conformational dynamics information complementary to static structural models. Integration of all structural and functional data should utilize statistical correlation analyses to establish which structural elements are critically linked to specific functions, generating testable hypotheses about the mechanistic role of AF_1757.

What techniques are most effective for studying AF_1757 under conditions that mimic its native environment?

To study AF_1757 under native-like conditions, researchers must recreate the extreme environment of Archaeoglobus fulgidus, a hyperthermophilic sulfate-reducing archaeon that typically grows at 83°C in anaerobic, slightly acidic conditions. High-temperature spectroscopic techniques offer valuable approaches for real-time functional analysis. Circular dichroism using temperature-controlled cells can monitor secondary structure stability between 25-95°C, providing insights into thermoadaptation mechanisms. Differential scanning calorimetry complements this by providing precise melting temperature and thermodynamic parameters. For functional studies, develop thermostable proteoliposome systems using archaeal lipids (particularly tetraether lipids) that maintain membrane integrity at elevated temperatures. Activity assays should be conducted in anaerobic chambers with precise temperature control, using buffering systems that maintain pH stability at high temperatures (e.g., phosphate or CHES buffers).

Microscale thermophoresis represents an excellent technique for studying binding interactions at elevated temperatures, requiring minimal protein amounts while providing quantitative binding parameters. For structural studies under native conditions, solid-state NMR of reconstituted AF_1757 in archaeal-mimetic membranes can provide atomic-level insights without crystallization. Time-resolved fluorescence approaches using site-specifically labeled AF_1757 can monitor conformational dynamics at different temperatures. High-temperature stopped-flow spectroscopy allows for measuring rapid kinetics of potential transport or catalytic functions. For cellular studies, consider developing high-temperature microfluidic devices coupled with fluorescence microscopy to observe protein localization and dynamics in living thermophilic cells. Throughout all experimental designs, incorporate appropriate controls at standard temperatures (37°C) to distinguish temperature-specific effects from general protein behavior.

How can researchers distinguish between experimental artifacts and genuine functional characteristics when studying AF_1757?

Distinguishing between experimental artifacts and genuine functional characteristics of AF_1757 requires rigorous experimental design and appropriate controls. Implement a multi-system validation approach where observations are verified across different expression systems (E. coli, yeast, cell-free systems) and purification methods (different affinity tags, positions, and purification conditions). For each functional assay, include both positive and negative controls: well-characterized proteins with known functions as positive controls and denatured AF_1757 or functionally inactive mutants as negative controls. Temperature-related artifacts are particularly concerning when studying hyperthermophilic proteins; therefore, perform parallel experiments at various temperatures (30°C, 60°C, 80°C) to distinguish temperature-dependent functional changes from denaturation effects.

Tag interference should be systematically addressed by comparing constructs with N-terminal, C-terminal, and cleavable tags, as well as tag-free versions when possible. For membrane protein studies, detergent effects can be misleading; therefore, test multiple detergent types and concentrations, complemented by detergent-free systems such as nanodiscs or amphipols. Concentration-dependent aggregation can masquerade as cooperative binding or catalysis; address this through dynamic light scattering measurements across the concentration range used in functional assays.

Statistical validation is crucial—implement at minimum triplicate biological replicates with appropriate statistical tests (e.g., ANOVA with post-hoc tests) to establish significance thresholds (p<0.05). For any observed activity, establish dose-response relationships and Michaelis-Menten kinetics where applicable, as non-specific effects rarely follow established enzyme kinetic models. When novel functions are proposed, seek orthogonal validation methods—if transport activity is observed in liposomes, verify with electrophysiology or cellular uptake assays. This systematic approach to controlling variables and validating observations across multiple systems minimizes the risk of misattributing artifacts as genuine functional characteristics.

How should researchers interpret structural prediction data for AF_1757?

Interpreting structural prediction data for AF_1757 requires a nuanced approach that acknowledges both the strengths and limitations of computational methods. Begin by generating multiple structural models using diverse methodologies (homology modeling, ab initio prediction, and AI-based approaches like AlphaFold) rather than relying on a single prediction method. Evaluate model quality using established metrics: QMEAN scores above 0.6, ProCheck Ramachandran plots showing >90% residues in favored regions, and MolProbity scores below 2.0 indicate reliable models. For membrane proteins like AF_1757, specific validation metrics such as hydrophobic-polar distribution across predicted transmembrane regions are crucial additional quality indicators.

Rather than viewing a single "best" structure, analyze the ensemble of predictions to identify consistently modeled regions (typically the protein core) versus variable regions (usually loops and termini). Calculate per-residue confidence scores to determine which structural features are most reliable. When analyzing predicted binding sites or functional regions, prioritize those that appear consistently across multiple modeling methods and show evolutionary conservation. For AF_1757, pay particular attention to the predicted membrane-interaction surfaces and potential ligand-binding pockets.

Critically assess whether the model explains existing experimental data, such as the requirement for specific buffer conditions or the effects of detergents on stability . Use the structural predictions to design testable hypotheses, particularly targeting high-confidence regions for site-directed mutagenesis. Remember that computational models represent static snapshots of inherently dynamic proteins; accordingly, complement structural analysis with molecular dynamics simulations, particularly at elevated temperatures relevant to Archaeoglobus fulgidus' native environment. Finally, present prediction confidence transparently in publications, acknowledging regions of high uncertainty rather than overstating model accuracy.

What statistical approaches are most appropriate for analyzing experimental data related to AF_1757?

When analyzing experimental data for AF_1757, researchers should implement statistical approaches that address the specific challenges of working with hyperthermophilic proteins. For thermal stability experiments, non-linear regression analysis using a Boltzmann sigmoidal model is appropriate for thermal denaturation curves, extracting statistically robust melting temperature (Tm) values with 95% confidence intervals. When comparing multiple experimental conditions or mutant variants, one-way ANOVA followed by Tukey's post-hoc test enables identification of statistically significant differences while controlling for family-wise error rates. For more complex multi-factorial experiments (e.g., varying temperature, pH, and ion concentrations simultaneously), two-way or three-way ANOVA with appropriate post-hoc tests should be implemented.

For kinetic measurements, non-linear regression to determine Michaelis-Menten parameters (Km, Vmax) should include calculation of standard errors and confidence intervals rather than simply reporting fitted values. Time-series data from stability or activity assays should be analyzed using repeated measures ANOVA or mixed-effects models that properly account for within-sample correlation. For binding studies, proper model selection is critical—use F-tests to determine whether one-site or multi-site binding models best fit the data without overfitting.

Power analysis should be conducted a priori to determine appropriate sample sizes, particularly important given the often limited quantities of purified recombinant proteins. For all statistical analyses, report effect sizes (e.g., Cohen's d or partial η²) in addition to p-values to convey biological significance beyond statistical significance. When comparing AF_1757 to homologous proteins, multivariate approaches such as principal component analysis or hierarchical clustering help visualize relationships in high-dimensional data spaces. For transcriptomic or proteomic studies involving AF_1757, implement appropriate corrections for multiple testing (e.g., Benjamini-Hochberg procedure) to control false discovery rates.

How can researchers resolve contradictory data when studying AF_1757 function?

Resolving contradictory data in AF_1757 functional studies requires a systematic analytical framework that addresses both methodological and biological sources of discrepancy. First, conduct a comprehensive methodological evaluation comparing contradictory results: examine differences in protein preparation (expression systems, purification methods, storage conditions), assay conditions (temperature, pH, buffer composition, presence of detergents), and measurement techniques. Differences in His-tag positioning or the presence of stabilizing agents like trehalose can significantly impact protein behavior. Design critical experiments that directly test whether methodological differences explain contradictory results by replicating both conditions in parallel within the same laboratory.

Consider biological explanations for seemingly contradictory data—AF_1757 may possess multiple functions or context-dependent activities. For instance, apparent differences in membrane association may reflect temperature-dependent conformational changes relevant to Archaeoglobus fulgidus' thermophilic lifestyle. Implement dose-response experiments across contradictory conditions to determine whether differences are qualitative (different mechanisms) or quantitative (same mechanism with different efficiencies). When functional differences are observed between homologous proteins from different species, conduct chimeric protein experiments swapping domains between homologs to identify regions responsible for functional divergence.

Collaborative cross-laboratory validation represents a powerful approach—engage multiple research groups to independently perform identical experiments following detailed protocols. Meta-analysis techniques can then integrate these results to distinguish reproducible findings from laboratory-specific artifacts. For persistent contradictions, consider that AF_1757 may exhibit condition-specific conformational equilibria or post-translational modifications that affect function. Advanced biophysical techniques like single-molecule FRET or native mass spectrometry can detect such heterogeneity that might be masked in bulk measurements. Throughout this process, maintain transparent reporting of all contradictory data rather than selectively publishing only consistent results, as seemingly contradictory findings often lead to deeper mechanistic understanding.

What are the best practices for integrating diverse datasets when characterizing AF_1757?

Integrating diverse datasets for comprehensive AF_1757 characterization requires a structured, multi-tiered approach that maximizes information extraction while minimizing bias. Begin by establishing a standardized data repository that centralizes all experimental results—structural predictions, functional assays, interaction data, and expression profiles—with detailed metadata documenting experimental conditions. Implement a consistent ontology system for annotation, preferably using established frameworks like Gene Ontology terms, to facilitate cross-dataset comparison.

For data integration, employ a weighted evidence approach where different experimental methods are assigned confidence scores based on their reliability and directness of evidence. For example, direct binding assays would receive higher weights than computational predictions. Bayesian network analysis represents an excellent statistical framework for integrating probabilistic evidence from diverse sources, generating confidence-weighted functional predictions. Network analysis approaches can identify consistent patterns across datasets—for instance, constructing protein-protein interaction networks from multiple experimental methods (yeast two-hybrid, co-immunoprecipitation, proximity labeling) and identifying high-confidence interactions supported by multiple methods.