Recombinant Aspergillus clavatus 3-ketoacyl-CoA reductase (ACLA_070510)

What is the function of 3-ketoacyl-CoA reductase in Aspergillus clavatus?

Aspergillus clavatus 3-ketoacyl-CoA reductase (ACLA_070510) is a critical enzyme in fatty acid metabolism. It belongs to the short-chain dehydrogenase/reductase family and catalyzes the NADPH-dependent reduction of 3-ketoacyl-CoA to 3-hydroxyacyl-CoA, an essential step in fatty acid synthesis and elongation. This enzyme is also referred to as 3-ketoreductase (KAR) or microsomal beta-keto-reductase . Within the fungal metabolic network, ACLA_070510 plays a key role in maintaining proper fatty acid composition, which impacts membrane integrity, energy storage, and potentially pathogenicity.

What metabolic pathways involve ACLA_070510?

ACLA_070510 is primarily involved in fatty acid metabolism pathways as indicated by KEGG annotations . Specifically, it participates in:

• Fatty acid biosynthesis - catalyzing the reduction step that converts 3-ketoacyl-CoA to 3-hydroxyacyl-CoA

• Fatty acid elongation - contributing to the extension of fatty acid carbon chains

• Potentially, other pathways involving fatty acid-derived metabolites

In the Aspergillus clavatus metabolic network, ACLA_070510 is listed alongside other enzymes involved in fatty acid metabolism including fatty acid synthases, desaturases, thiolases, and oxidases . This positioning in multiple pathways highlights its importance in fungal metabolism.

How can I express and purify recombinant ACLA_070510?

To express and purify recombinant Aspergillus clavatus 3-ketoacyl-CoA reductase, the following methodology has proven successful:

Expression Protocol:

• Clone the ACLA_070510 gene (encoding amino acids 1-345) into an appropriate E. coli expression vector with an N-terminal His-tag

• Transform the construct into a competent E. coli expression strain

• Grow transformed cells in suitable media (typically LB with appropriate antibiotics)

• Induce protein expression (typically with IPTG for T7-based systems)

• Harvest cells by centrifugation

Purification Protocol:

• Resuspend cell pellet in lysis buffer containing protease inhibitors

• Lyse cells using sonication or alternative methods

• Clarify lysate by centrifugation (15,000-20,000 g, 30-60 minutes)

• Perform affinity chromatography using Ni-NTA resin for His-tagged protein

• Wash column with increasing imidazole concentrations

• Elute purified protein with high imidazole buffer

• Perform buffer exchange into storage buffer (Tris/PBS-based buffer, pH 8.0 with 6% trehalose)

• For long-term storage, add glycerol (up to 50%) and store at -20°C/-80°C

This approach has successfully yielded purified recombinant ACLA_070510 with greater than 90% purity as determined by SDS-PAGE .

What assays can be used to measure ACLA_070510 enzymatic activity?

Several assay methods can be employed to measure the activity of recombinant ACLA_070510:

Spectrophotometric NADPH Consumption Assay:

• Prepare reaction mixture containing:

  • Purified ACLA_070510 enzyme (typically 0.1-1.0 μg)

  • 3-ketoacyl-CoA substrate (50-200 μM)

  • NADPH (100-500 μM)

  • Buffer (typically Tris-HCl, pH 7.5-8.5)

• Monitor decrease in absorbance at 340 nm as NADPH is oxidized to NADP+

• Calculate activity using the extinction coefficient of NADPH (6,220 M-1 cm-1)

This approach is similar to methods used for related reductases described in the literature .

Alternative Methods:

• Direct product detection using HPLC or LC-MS to measure 3-hydroxyacyl-CoA formation

• Coupled enzyme assays linking product formation to a secondary reaction

• Discontinuous assays with sampling at different time points

For substrate specificity studies, compare activity with different chain-length 3-ketoacyl-CoA substrates, similar to approaches used for related enzymes in the fatty acid metabolism pathway .

How should I design controls when studying pH effects on ACLA_070510 activity?

When investigating the effect of pH on ACLA_070510 activity, include the following controls to ensure reliable and interpretable results:

Buffer Controls:

• Use overlapping buffer systems to distinguish pH effects from buffer-specific effects:

  • MES buffer for pH 5.5-6.5

  • PIPES buffer for pH 6.1-7.5

  • MOPS buffer for pH 6.5-7.9

  • HEPES buffer for pH 6.8-8.2

  • Tris buffer for pH 7.5-9.0

• Maintain constant ionic strength across all pH conditions

• Include buffer-only reactions to establish background rates

Stability Controls:

• Pre-incubate enzyme at each pH (without substrate) and measure residual activity at optimal pH

• This distinguishes effects on catalysis from effects on enzyme stability

• Include time-course pre-incubations to assess time-dependent inactivation

Substrate and Cofactor Controls:

• Check stability of NADPH and acyl-CoA substrates at different pH values

• Both can be susceptible to degradation at extreme pH values

• Prepare fresh working solutions for each pH condition

Analytical Controls:

• Establish NADPH standard curves at each pH if using absorbance-based detection

• NADPH absorption properties can vary slightly with pH

• Include no-enzyme controls at each pH

This comprehensive approach ensures that observed pH effects can be accurately attributed to enzyme catalytic properties rather than artifacts of the experimental system.

How can protein engineering improve the substrate specificity of ACLA_070510?

Improving the substrate specificity of ACLA_070510 through protein engineering requires a systematic approach targeting the substrate binding pocket. Based on successful approaches with similar enzymes, the following methodology is recommended:

Computational Design Approach:

• Utilize a DLKcat approach similar to that described for Tfu_0875 (a related enzyme)

  • This computational method identifies mutations that may enhance activity and specificity

  • Focus on residues lining the substrate binding pocket

• Apply the greedy accumulated strategy for protein engineering (GRAPE)

  • Systematically combine beneficial mutations identified through computation

  • Use iterative rounds of mutation and testing to achieve optimal results

• Consider the following criteria for mutation selection:

  • Distance from active center to the Cα of the target substrate

  • Number of hydrogen bonds formed between the substitutions and substrate

  • Relative enzyme activity measurements

Experimental Validation:

• Generate single-point mutants via site-directed mutagenesis

• Express and purify mutant proteins using standardized protocols

• Determine kinetic parameters (Km, kcat, kcat/Km) for each substrate of interest

• Analyze substrate binding pocket through structural studies when possible

• Combine beneficial mutations based on K-means analysis

This approach has successfully enhanced substrate specificity in related enzymes, including improving specificity for succinyl-CoA in the thiolase Tfu_0875, and similar strategies could be applied to ACLA_070510 .

What role might ACLA_070510 play in Aspergillus pathogenicity?

While the direct role of ACLA_070510 in Aspergillus clavatus pathogenicity has not been explicitly established, several experimental approaches can be used to investigate this question:

Gene Disruption Studies:

• Generate ACLA_070510 knockout strains using CRISPR-Cas9 or homologous recombination techniques

• Create conditional expression mutants for essential genes using methods similar to GRACE (gene replacement and conditional expression)

• Confirm gene disruption using PCR, Southern blotting, and qRT-PCR methods as described for other Aspergillus genes

Phenotypic Characterization:

• Compare growth rates of wild-type and mutant strains on different carbon sources

• Analyze fatty acid profiles using gas chromatography-mass spectrometry

• Examine morphological development and stress responses

• Test resistance to antifungal compounds and host defense mechanisms

Infection Models:

• Utilize appropriate infection models (cell culture, insect, or murine)

• Assess virulence parameters:

  • Host cell adhesion and penetration

  • Fungal burden in host tissues

  • Inflammatory responses

  • Survival rates

Transcriptomic Analysis:

• Perform RNA-seq of wild-type and mutant strains during infection conditions

• Quantify ACLA_070510 expression under various infection-relevant stresses

• Use qRT-PCR with approaches similar to those described for monitoring Aspergillus fumigatus gene expression

This integrated approach would help determine whether ACLA_070510 contributes to pathogenicity through roles in fatty acid metabolism, stress adaptation, or other mechanisms that support fungal virulence.

How does ACLA_070510 compare to homologous reductases in other Aspergillus species?

Comparative analysis of ACLA_070510 with homologous reductases from other Aspergillus species requires a multi-faceted approach:

Sequence and Structural Comparison:

• Identify homologous proteins in other Aspergillus species:

  • Aspergillus niger 3-ketoacyl-CoA reductase (An02g03570)

  • Emericella nidulans 3-ketoacyl-CoA reductase (AN5861)

  • Other Aspergillus species with annotated 3-ketoacyl-CoA reductases

• Perform multiple sequence alignment to identify:

  • Conserved catalytic residues

  • Differences in substrate binding regions

  • Species-specific sequence variations

Functional Characterization:

• Express and purify homologous enzymes using standardized protocols

• Compare enzymatic properties:

  • Substrate specificity profiles

  • Kinetic parameters (Km, kcat, kcat/Km)

  • pH and temperature optima

  • Inhibitor sensitivity

• Test activity against standardized substrate panels:

  • Various chain-length 3-ketoacyl-CoA substrates

  • Different cofactor preferences (NADPH vs. NADH)

Complementation Studies:

• Create cross-species complementation strains by expressing ACLA_070510 in other Aspergillus species with their native reductase deleted

• Assess the ability of ACLA_070510 to restore wild-type phenotypes

• Identify species-specific functional differences

This comparative approach would reveal evolutionary adaptations in substrate specificity and catalytic efficiency among Aspergillus species, potentially correlating with differences in metabolic requirements or ecological niches.

How can I develop and validate a high-throughput screening assay for ACLA_070510 inhibitors?

Developing a robust high-throughput screening (HTS) assay for ACLA_070510 inhibitors requires careful optimization and validation. The following methodological approach is recommended:

Assay Development:

• Miniaturize the NADPH consumption assay to 384-well or 1536-well format:

  • Optimize enzyme concentration (typically 0.5-5 nM)

  • Determine substrate concentration (at or below Km for increased sensitivity)

  • Set NADPH concentration for linear signal response

  • Select appropriate buffer conditions (pH, ionic strength)

• Optimize assay parameters:

  • Reaction time (typically 10-30 minutes)

  • DMSO tolerance (aim for at least 1% tolerance)

  • Signal stability and detection limits

  • Reading intervals for kinetic measurements

Assay Validation:

• Determine statistical parameters:

  • Z'-factor (aim for >0.5 for robust assay)

  • Signal-to-background ratio (>4 preferred)

  • Coefficient of variation (CV <10%)

• Test against a validation set:

  • Include known inhibitors of related reductases

  • Test compounds with different mechanisms of action

  • Include interference compounds to identify false positives

Screening Implementation:

• Primary screen:

  • Test compounds at single concentration (typically 10-20 μM)

  • Include controls on each plate (positive, negative, vehicle)

  • Apply hit threshold (typically >50% inhibition)

• Secondary screening cascade:

  • Dose-response confirmation (8-10 concentrations)

  • Determine IC50 values

  • Rule out interference compounds (orthogonal assays)

  • Assess selectivity against related reductases

• Mechanism of action studies:

  • Determine inhibition type (competitive, non-competitive, etc.)

  • Measure Ki values similar to approaches used for other fungal enzymes

This comprehensive approach would establish a reliable HTS platform for identifying specific ACLA_070510 inhibitors with potential antifungal applications.

What structural features should be considered when designing selective inhibitors of ACLA_070510?

When designing selective inhibitors targeting ACLA_070510, consider the following structural features and methodological approaches:

Target Site Analysis:

• Focus on key inhibitor binding regions:

  • NADPH binding site - for cofactor competitive inhibitors

  • Substrate binding pocket - for substrate competitive inhibitors

  • Allosteric sites - for non-competitive inhibition

  • Interface regions if the enzyme forms functional complexes

• Consider species selectivity determinants:

  • Target unique residues not present in human homologs

  • Exploit differences in binding pocket size and shape

  • Focus on fungal-specific structural features

Structure-Based Design Strategies:

• Employ computational approaches:

  • Homology modeling based on related reductases

  • Molecular docking studies to predict binding modes

  • Virtual screening of compound libraries

  • Fragment-based design approaches

• Design principles for different inhibitor classes:

  • NADPH-competitive inhibitors: incorporate adenosine-like scaffolds

  • Substrate-competitive inhibitors: mimic 3-ketoacyl-CoA structure

  • Covalent inhibitors: target catalytic or accessible cysteine residues

  • Allosteric inhibitors: identify potential binding pockets outside active site

Selectivity Considerations:

• Test compounds against human homologs to ensure selectivity

• Assess activity against other Aspergillus enzymes that bind similar substrates

• Optimize compounds for metabolic stability in fungal cells

Validation Methods:

• Confirm binding mode through:

  • Enzyme kinetics to determine inhibition mechanism

  • Resistance mutations that identify binding residues

  • X-ray crystallography of enzyme-inhibitor complexes when possible

Similar approaches have proven successful for developing selective inhibitors against other fungal enzymes such as thioredoxin reductase, which was identified as an essential enzyme in Aspergillus fumigatus .

What are the optimal conditions for crystallizing ACLA_070510 for structural studies?

Crystallizing ACLA_070510 for structural studies requires systematic optimization of multiple parameters. Based on successful approaches with related enzymes, the following methodology is recommended:

Protein Preparation:

• Express and purify ACLA_070510 to high homogeneity (>95% by SDS-PAGE)

• Remove His-tag if it interferes with crystallization

• Perform size exclusion chromatography as final purification step

• Concentrate protein to 10-15 mg/ml based on successful crystallization of related enzymes

• Prepare protein in crystallization-friendly buffer:

  • 20 mM Tris-HCl, pH 8.0-9.0

  • 150 mM NaCl

  • 2-5 mM DTT or other reducing agent

Crystallization Screening:

• Initial sparse matrix screening:

  • Commercial screens (Hampton, Molecular Dimensions, Qiagen)

  • Vapor diffusion method (hanging or sitting drop)

  • Test multiple protein:reservoir ratios (1:1, 2:1, 1:2)

  • Incubate at different temperatures (4°C, 16°C, 20°C)

• Based on successful crystallization of related enzymes, prioritize conditions containing:

  • Polyethylene glycol 3350 (10-20%)

  • Lithium acetate (0.2 M)

  • pH range 7.0-9.0

• Explore co-crystallization with:

  • NADP+ or NADPH cofactors

  • Substrate analogs or inhibitors

  • Product analogs

Optimization Strategies:

• Fine-tune promising conditions by varying:

  • Precipitant concentration

  • pH in smaller increments

  • Salt concentration

  • Additive screening

• Crystal improvement techniques:

  • Seeding from initial crystals

  • Streak seeding

  • Microseeding

  • Oil barrier methods

Crystal Handling:

• Cryoprotection:

  • Test ethylene glycol (30%) as cryoprotectant based on protocols for related enzymes

  • Alternative cryoprotectants: glycerol, sucrose, PEG 400

• Flash-freeze crystals in liquid nitrogen

• Evaluate diffraction quality using in-house X-ray source before synchrotron data collection

This systematic approach has proven successful for crystallizing related enzymes and would provide the best chance for obtaining diffraction-quality ACLA_070510 crystals.

How can I investigate potential protein-protein interactions of ACLA_070510 with other enzymes in fatty acid metabolism?

To investigate protein-protein interactions between ACLA_070510 and other fatty acid metabolism enzymes, employ the following comprehensive methodology:

In vitro Interaction Studies:

• Pull-down assays:

  • Use purified His-tagged ACLA_070510 as bait

  • Incubate with Aspergillus clavatus cell lysate

  • Capture with Ni-NTA resin and identify interacting proteins by mass spectrometry

  • Validate with reciprocal pull-downs using identified partners

• Surface plasmon resonance (SPR):

  • Immobilize ACLA_070510 on sensor chip

  • Flow potential interacting proteins (other purified fatty acid metabolism enzymes)

  • Determine binding kinetics (kon, koff) and affinity (KD)

  • Test effects of substrates, products, or cofactors on interactions

• Size exclusion chromatography:

  • Analyze migration of ACLA_070510 alone and in mixtures with potential partners

  • Detect complex formation by shift in elution profile

  • Confirm complex composition by SDS-PAGE analysis of fractions

In vivo Interaction Methods:

• Co-immunoprecipitation:

  • Express tagged ACLA_070510 in Aspergillus clavatus

  • Prepare cell lysates under non-denaturing conditions

  • Immunoprecipitate ACLA_070510 using tag-specific antibodies

  • Identify co-precipitated proteins by mass spectrometry

• Bimolecular fluorescence complementation (BiFC):

  • Fuse ACLA_070510 and potential partners to complementary fragments of fluorescent protein

  • Express in fungal cells

  • Visualize interaction through reconstituted fluorescence

  • Quantify interaction strength through fluorescence intensity

• Proximity-dependent labeling:

  • Fuse ACLA_070510 to BioID or APEX2

  • Express in fungal cells and activate labeling

  • Purify biotinylated proteins and identify by mass spectrometry

  • Maps proteins in close proximity to ACLA_070510 in vivo

Functional Validation:

• Enzyme assays with reconstituted complexes

• Mutational analysis of interaction interfaces

• Phenotypic analysis of mutants with disrupted interactions

This multi-method approach would provide robust evidence for physiologically relevant protein-protein interactions and their functional significance in fatty acid metabolism.