Rational design of lanosterol 14α-demethylase for ergosterol biosynthesis in Saccharomyces cerevisiae

Abstract

Ergosterol is widely used in skin care products and drug preparation. Lanosterol 14α-demethylase (Erg11p, 14DM, CYP51) is the rate-limiting enzyme for the biosynthesis of various steroid compounds in Saccharomyces cerevisiae. Herein, Erg11p was engineered to extend the in vivo catalytic half-life and increase the turnover rate. Single mutations resulting in lower folding energy were selected, and mutant P201H had an ergosterol yield of 576.9 mg·L⁻¹. Through consensus design, single mutations resulting in higher sequence identity to homologs were tested and mutant K352L had an ergosterol yield of 677.9 mg·L⁻¹. The key residues for substrate binding were confirmed via alanine scanning mutagenesis and mutant F384A had an ergosterol yield of 657.8 mg·L⁻¹. Molecular dynamics (MD) simulation was conducted to investigate the contributions of pocket residues and eight residues were found to engage in weak interactions with lanosterol. Saturation mutagenesis was applied to these residues to enhance binding to lanosterol, and mutant F384E had an ergosterol yield of 733.8 mg·L⁻¹. Meanwhile, MD simulations were conducted to assess the impact of mutant F384E on enzyme activity. The results consistently showed that single point mutation F384E had the greatest effect, outperforming the combination mutations. Batch fermentation increased the ergosterol yield of mutant F384E to 3067.5 mg·L⁻¹, the highest reported to date. The successful engineering of Erg11p may pave the way for industrial-scale production of ergosterol and other steroids.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-024-04136-x.

Introduction

Ergosterol, a steroid found extensively in the cell membranes of fungi and protozoa (Ejam et al. 2023), is a component of various products with high pharmacological activity (Zou and Porter 2015) including vitamin D₂ (Ethafa and Al-Manhel 2022) and other steroid hormone drugs (Rella et al. 2015). Ergosterol differs structurally and functionally from lanosterol, which serves as an intermediate in animal cholesterol biosynthesis. Lanosterol acts as a key precursor in the biosynthesis of ergosterol in fungi (Li and Forciniti 2020). In this study, lanosterol was used as the substrate for the enzyme Erg11p, while ergosterol served as a key indicator of the enzyme’s activity. The research on microbial synthesis methods and yield-increasing strategies is, therefore, important (Vil et al. 2018). Given its proficiency in generating intricate organic compounds, Saccharomyces cerevisiae serves as a microbial host for ergosterol synthesis (Chen et al. 2016). The ergosterol synthesis pathway in S. cerevisiae involves catalysis by multiple enzymes, amongst which lanosterol 14α-demethylase (Erg11p, 14DM, CYP51) plays a pivotal role (Baghban et al. 2019). However, Erg11p has limited enzyme activity and substrate selectivity, posing challenges to efficient production of ergosterol. Thus, modifying Erg11p in S. cerevisiae is crucial. Erg11p is associated with many downstream products including triterpenes and steroids. Due to the difficulty in obtaining FF-MAS standards and the complexity of its detection, ergosterol, as a key product, offers a clearer and more accurate measure of the biosynthetic pathway’s efficiency. Thus, this study used ergosterol as a proxy to assess Erg11p efficiency (Fig. 1). Enhancing ergosterol yield was the key to boosting the production of steroid compounds, and this could be achieved by improving Erg11p catalytic activity and specificity, resulting in more efficient steroid drug production.

Fig. 1.

Biosynthetic pathway of ergosterol in S. cerevisiae. Substrate, direct product and detection product structures are shown. The blue part represents the reaction process between substrate and detection product; the green part represents application of the detection product; red font represents enzymes involved in enzyme engineering in this study; green font represents mediators involved in the reaction process

Production of steroid compounds using chemical synthesis faces numerous challenges including lengthy pathways, high volumes of wastewater discharge, and difficulties in handling heavy metal waste. In contrast, microbial fermentation offers the advantages of lower environmental pollution and more convenient production methods, and it has been applied to the production of steroids such as cortisone, prednisone and dexamethasone (Shafiei et al. 2020). Currently, production of steroid compounds using microbial methods primarily involves analysis of steroid pathways, exploration of high value enzymes, modification of key enzymes, and rational modification of microbial metabolic pathways (Desmond and Gribaldo 2009). He et al. produced ergosterol using sugarcane molasses as a low cost carbon source, resulting in 52.6 mg·L⁻¹ in shake flasks and 1707 mg·L⁻¹ in a 5 L bioreactor (He et al. 2007). Sun et al. used self-induced promoters to dynamically control the expression of ARE2, UPC2-1 and ACC1, resulting in 40.6 mg·g⁻¹ ergosterol (Sun et al. 2021). Derkacz et al. reported that K143R mutation of Candida albicans ERG11 enhanced azole resistance and increases ergosterol levels (Derkacz et al. 2022). These approaches underscore the potential of microbial methods in overcoming the limitations of traditional steroid production techniques, offering a sustainable alternative for the pharmaceutical industry.

Biotransformation is one of the core technologies in biochemical processes, and enzyme-catalysed transformation is an important element of biochemical processes (Ge et al. 2023). The rational design method utilises enzyme bioinformatics and calculations to preselect potential sites and avoid blind selection (Swetha et al. 2022). By identifying potential sites, the large mutation pool of enzyme-directed evolution is eliminated (Hamamatsu et al. 2006), making enzyme modification experiments easier. AlphaFold, PDB, Rosetta, Discovery Studio, mutation screening, limited enzyme cleavage, guided evolution, protein engineering, and strategy combinations can be employed to modify enzymes and thereby enhance stability, activity and solubility. Amino acid sequence changes can significantly impact protein structure and function by altering conformation, active sites, and stability. Wei et al. modified carbonyl reductase through semi-rational design, obtaining mutant S126A/R129Q/V194A with a 8-chloro-6-oxyoctanoic acid ethyl ester yield of 696 g∙L⁻¹∙d⁻¹ (wei et al. 2024). There exists a broad research field for enzyme improvement in microorganisms and new methodological ideas for novel methods of enzyme production. Silva et al. identified a mutation in the coding region of the ERG11 gene and found that the G524R mutation did not affect the function of 14α-demethylase, while Y166S mutation affected the enzyme (Silva et al. 2016). These advances highlight the potential of enzyme modifications to revolutionise biochemical processes and enzyme production techniques.

In the present study, rational design was carried out to improve the stability and catalytic activity of Erg11p. Engineering Erg11p stability was conducted based on folding energy changes upon mutagenesis and mutations lowering the folding energy were selected for validation. Consensus design was applied to further enhance the stability of Erg11p by mutating sites with lower sequence identity to the homolog. To engineer enzyme activity, the substrate-binding pocket was visualised and key binding sites were subjected to alanine scanning mutagenesis to assess their influence on Erg11p activity. MD simulation was performed to investigate interactions between Erg11p and lanosterol. To improve binding of Erg11p to lanosterol, binding pocket residues with weak interactions were selected for saturation mutagenesis. Following multiple enzyme engineering strategies, mutant F384E was obtained, which increased the yield of ergosterol by 50%, reaching 733.8 mg·L⁻¹ in 48-well plates and 3067.5 mg·L⁻¹ in a 5 L bioreactor.

Materials and methods

Chemicals

Ergosterol was purchased from Tanmo Quality Inspection Co Ltd (Changzhou, China), anhydrous ether and other chemicals were purchased from Sangon Biotech (Shanghai, China). Enzymes for Golden Gate Assembly and plasmid template digestion were purchased from Vazyme (Nanjing, China) and Takara Bio Inc (Dalian, China).

Plasmid and strain construction

Escherichia coli JM109 was used for plasmid construction and preservation. S. cerevisiae WQ-10 was used as the starting strain. The genotypes of starting and constructed strains are listed in Table 1. All fragments were cloned into a vector using a Gibson assembly kit (Gibson et al. 2009) with primers listed in Table S1. Erg11p mutation site introduction was achieved using an overlapping PCR strategy. Primers for mutant plasmids are listed in Table S2. All plasmids employed in this study are listed in Table S3. Engineered S. cerevisiae strains were generated by episomal plasmid expression and genome integration. DNA fragments were digested with DpnI (Takara) then assembled to achieve saturation mutagenesis at four positions. The lithium acetate method was employed for efficient transformation of genes into S. cerevisiae for genomic modification and expression (Gietz et al. 2004). A uracil tag was integrated with gene fragments and relevant deficient yeast nitrogen base (YNB) culture medium was used to screen strains expressing integrated genes (Isalan et al. 2016). Strains with the highest ergosterol accumulation were screened in 48-well plates.

Table 1.

Strains and genotypes used in this study

Name	Description	Source
JM109	Used for amplification and preservation of plasmids	This lab
C800	CENPK2-1D; MATα, ura3-52, trp1-289, leu2-3,112, his3Δ1, MAL2-8C, SUC2, gal80::KanMX	This lab
WQ-10	ΔTy1::PGAL1-tHMG1-PGAL10-IDI1-SpHIS5deg ΔTy2::PTEF1-tHMGR-PGAL7-IDI1-KlLEU2deg ΔTy3::PGAL7-ERG2-PTDH3-ERG3-PPGK1-POS5-PTDH1-ERG4-ScTRP1deg ΔGAL2::NADH-HMG; ΔYPL062W; ΔROX1; ΔNEM1 ΔYJL064W::PTDH3-ERG4-PGAL7-ERG11-PTDH1-ERG1 ΔDOS2::PGAL7-ERG2-PGAL1-ERG3-PGAL10-CTT1-PTDH1-ERG4 ΔPERG1,7::PGAL7-PTEF1-ERG4-PPGK1-INO2; ΔPADH2::PTEF1	This lab
WQ-11	WQ-10; PTDH1-URA3- PTEF1-ERG11F384E	This study

Strain cultivation

Plasmid cloning and DNA extraction were performed using E. coli JM109 cells cultured in Luria–Bertani medium (10 g·L⁻¹ tryptone, 5 g·L⁻¹ yeast extract, 10 g·L⁻¹ NaCl). Yeast strains expressing auxotrophic markers were selected on synthetic medium (20 g·L⁻¹ glucose, 1.74 g L⁻¹ amino acid-free YNB, 5 g·L⁻¹ ammonium sulphate, 0.05 g·L⁻¹ leucine, 0.05 g·L⁻¹ histidine, 0.05 g·L⁻¹ tryptophan, 0.05 g·L⁻¹ uracil). Engineered yeast strains were cultured in YPD (20 g·L⁻¹ glucose, 10 g L⁻¹ yeast extract, 20 g·L⁻¹ peptone). Single colonies were picked and cultured in a 48-well plate in 1.5 mL YPD medium at 30 °C with shaking at 220 rpm for 3 days (Wei et al. 2022).

For 5 L fermentations, a single colony of an engineered strain was inoculated into 10 mL YPD medium in a 50 mL shaker flask and cultured for 17–20 h at 30 °C and 220 rpm. An inoculum (1%) was then transferred to a 500 mL flask containing 200 mL YPD medium (seed solution) and cultured for 17–20 h at 30 °C and 220 rpm. The culture was then inoculated into a 5 L fermenter containing 2.5 L YPD medium at 8% inoculum and fed-batch fermentation was performed at 30 °C, using 5 M NaOH to maintain the pH at 5.5. Feed medium 1 (1 L) was composed of 800 g·L⁻¹ glucose, 18 g·L⁻¹ KH₂PO₄, 10.24 g·L⁻¹ MgSO₄·7H₂O, 7 g·L⁻¹ K₂SO₄, 0.56 g·L⁻¹ Na₂SO₃, 20 mL·L⁻¹ trace element A (comprising 5.75 g·L⁻¹ ZnSO₄·7H₂O, 0.32 g·L⁻¹ MnCl₂·4H₂O, 0.47 g·L⁻¹ CoCl₂·6H₂O, 0.48 g·L⁻¹ NaMoO₄·2H₂O, 2.9 g·L⁻¹ CaCl₂·2H₂O, 2.8 g·L⁻¹ FeSO₄·7H₂O and 80 mL 0.5M EDTA, adjusted to pH 8.0) and 24 mL·L⁻¹ trace element B (comprising 0.05 g·L⁻¹ biotin, 1 g·L⁻¹ calcium pantothenate, 1 g·L⁻¹ nicotinic acid, 25 g·L⁻¹ myoinositol, 1 g·L⁻¹ thiamine HCl, 1 g·L⁻¹ pyridoxal HCl and 0.02 g·L⁻¹ p-aminobenzoic acid). Feed medium 2 (1 L) was composed of 210 g·L⁻¹ (NH₄)₂SO₄ and 65 g·L⁻¹ KH₂PO₄. When the residual sugar was 0, feed medium 1 was supplemented, controlling the residual sugar between 0.5 and 1.5 g·L⁻¹, and maintained the ethanol content below 5–10 g·L⁻¹. After fermentation for ~ 22 h, feed medium 2 was fed at a flow rate of 3–10 mL·h⁻¹ until fermentation was complete.

Processing and analysis of fermentation samples

First, a potassium hydroxide ethanol saponification solution, with a mass fraction of 30% (90% ethanol) was prepared. Next, 1 mL fermentation medium was centrifuged at 12,000 rpm for 5 min, the supernatant was removed, and the pellet was resuspended in 1 mL saponification solution and heated to 86–88 °C for 3.5–4.0 h to undergo a reflux saponification reaction. Anhydrous ether was the extractant (Wang et al. 2018). A high-performance liquid chromatography system (Waters, Milford, MA, USA) coupled to a C18 column (30 m × 0.25 mm, 0.25 µm film thickness) at 30 °C was used to identify ergosterol. The mobile phase was 100% methanol, the flow rate was 1 mL·min⁻¹, the separation duration was 15 min, and the absorbance was monitored at 280 nm (Zhang et al. 2023a).

Rational design of structural stability

The rational design of structural stability was divided into Rosetta Cartesian ddg (Cartddg) and Rosetta Supercharge. Rosetta Cartddg was used for screening virtual variants with reduced folding free energy. Rosetta supercharge was then used to modify the surface charge of enzymes by introducing mutations that could increase the electrostatic charge of protein surfaces to prevent intracellular aggregation and sedimentation (Zhang et al. 2023b). The amino acid sequence of Erg11p was obtained in NCBI (NCBI, Accession number: QHB08993.1). The crystal structure and other information for Erg11p from S. cerevisiae was obtained from the Protein Data Bank (PDB ID: 4LXJ). Rosetta relax was used for structural optimization. Virtual amino acid mutation scanning was performed using the affinity prediction scripts of Rosetta Cartddg and Rosetta Supercharge.

Consensus design

In the context of protein engineering, consensus design is a sophisticated approach that synthesises artificial proteins by combining the most prevalent amino acid residues found across homologs in a given protein family. This technique hinges on the assumption that frequently occurring amino acids at specific positions play a pivotal role in enhancing the stability and operational efficiency of a protein across various versions (García-Guevara et al. 2017). The crystal structure and amino acid sequence of Erg11p were obtained from the kazlab website (http://kazlab.umn.edu/). A homology search was performed, followed sequence alignment, and amino acids with the highest frequency of occurrence at each locus were predicted. Then, these amino acids were replaced by the amino acid with the highest frequency of specific site identification.

Alanine scanning mutagenesis

Alanine scanning mutagenesis is the systematic substitution of amino acid residues in proteins with alanine to evaluate the impact on protein function or stability. This approach can accurately locate amino acids that are crucial for protein activity or structural integrity, helping to understand protein interactions and functions. By using the small and unreactive alanine, this method minimises structural alterations while revealing the importance of the original residues (Gomez-Fernandez et al. 2020). The crystal structure of Erg11p was downloaded from the PDB website, and the structure of the small molecule ligand lanosterol was obtained from PubChem website (CID: 246,983) (http://pubchem.ncbi.nlm.nih.gov). Amino acid residues located within a 5 Å radius of the ligand were designated as mutation sites. Based on the preceding analysis, DS software was employed for alanine scanning mutagenesis.

MD simulation

The structure of the enzyme and ligand complex was obtained from the PDB (PDB ID: 4LXJ) and used as the starting structure for MD simulation (Coskuner-Weber & Uversky 2019). After neutralising using Na⁺ or Cl⁻, the system was energy-minimised by the steepest descent method in which both short-range van der Waals and short-range electrostatic interactions were truncated at 14 Å. Lanosterol and Erg11p (or its mutants) were embedded with GAFF and FF14sb force fields, respectively. After minimisation and equilibration, isochoric-isothermal ensemble (NVT) and isothermal-isovolumetric ensemble (NPT) analyses at a constant pressure (1 atm) and a constant temperature (300 K) were performed with lanosterol in the active sites of wild type (WT) and mutant Erg11p to study changes in binding during the duration of the 100 ns simulation (Kutzner et al. 2019). RMSD was used to assess the overall structural stability and dynamic changes of enzymes and substrates, while RMSF was used to assess local changes along protein chains. PyMOL (https://pymol.org) was employed to visualise the results.

Results

Engineering structural stability of Erg11p

Enzyme stability is correlated with intracellular half-life, which contributes to determining the overall substrate turnover rate (Peccati et al. 2023). Folding free energy influences enzyme stability and function, with lower values indicating greater structural stability. The structure of Erg11p monomer was used for folding energy and surface charge design. The fundamental purpose of Rosetta Supercharge is to enhance intermolecular repulsion, thereby preventing protein aggregation and sedimentation and enhancing protein stability. Meanwhile, virtual saturation mutagenesis was carried out based on Rosetta Cartddg to screen variants with higher folding capacity. The obtained ddg by whole protein saturation mutagenesis ranged from -17.7 to 73.7 kcal/mol. Surface charge-related mutagenesis identified nine mutations that could alter the surface charge to -50 (Wang et al. 2020).

The top 27 sites with the lowest ddg values and the nine sites predicted by Rosetta Supercharge were selected for experimental verification using the high ergosterol- producing S. cerevisiae WQ-10 strain (Fig. 2c and Fig S1a). The results showed that four mutants, P201H, P38Y, P201Q and E354R, resulted in a 10–20% increase in ergosterol production. Meanwhile, changes in 64% of the sites showed negligible differences in ergosterol production. The best mutant, P201H, had an increase ergosterol of 18%, reaching 576.9 mg·L⁻¹ (Fig. 1c and Fig S1b). The mutations were combined for four loci (P201H, P38Y, P201Q and E354R) showing good single point effects. Five mutant plasmids were constructed and introduced into the S. cerevisiae WQ-10 strain for experimental verification. The results showed that the combined mutation effect was not significant (Fig. 1b).

Fig. 2.

Engineering structural stability of Erg11p. a Structure of the Erg11p-lanosterol complex. Substrate-binding sites and predicted signal peptide are labelled. b Production of ergosterol by double mutants following stability modification. c Production of ergosterol by single point mutants following stability modification. Ergosterol was separated by HPLC with a mobile phase of 100% methanol, monitoring the absorbance at 280 nm over 15 min at 30 °C (*P < 0.05 vs. WT; **P < 0.01 vs. WT; ***P < 0.001 vs. WT; ****P < 0.0001 vs. WT)

Modifying amino acid sequence conservation in Erg11p

Consensus design is a representative sequence-based protein design method that involves extensive homology alignment followed by mutation of less conserved to more conserved residues (Kozuka et al. 2021). Consensus design was conducted using consensus finder, resulting in specific homology values for 22 amino acid sites of Erg11p, with a pre-mutation homology of 1–12% and a post-mutation homology increase of 26–95%. The predicted percentage change before and after amino acid mutation is shown in Fig. 3A. A total of 22 single point mutant plasmids were constructed and transformed into the S. cerevisiae WQ-10 strain for experimental verification.

Fig. 3.

Modifying amino acid sequence conservation in Erg11p. a Percentage change before (blue) and after (purple) evolutionarily conserved mutations. b Production of ergosterol by single point mutants following consensus design. c Production of ergosterol by double mutants following consensus design (*P < 0.05 vs. WT; **P < 0.01 vs. WT; ***P < 0.001 vs. WT; ****P < 0.0001 vs. WT)

The results showed that four mutants, K352L, A127S, A260K and A124E, boosted ergosterol yield by 20–40%. There were only two sites that increased or decreased ergosterol production less than 10%, and 32% of sites increased or decreased ergosterol production by 10–20%. The increase in ergosterol with the best mutant K352L was 38%, reaching 677.9 mg·L⁻¹, and this site had 67% amino acid similarity (Fig. 3b). Mutations were combined for four loci (K352L, A127S, A260K, and A124E) with good single point effects. Six mutant plasmids were constructed and transformed into the S. cerevisiae WQ-10 strain for experimental verification but the combined mutation effect was not significant (Fig. 3c).

Validating key active site binding pocket residues of Erg11p

Alanine scanning mutagenesis is an experimental technique in which residues in the target protein are sequentially mutated into alanine (Takei et al. 2021), with the aim of identifying the residues that contribute the most to structural stability, ligand binding affinity and catalytic activity. The strategies previously employed for producing ergosterol did not show significant improvement and this study aimed to use a more direct method to enhance enzyme activity, namely verifying whether mutations in the substrate pocket have a significant positive or negative impact on enzyme activity. Therefore, amino acid residues within 5 Å of the Erg11p active site and lanosterol ligand were subjected to alanine scanning mutagenesis by Discovery Studio (DS) software to identify key sites that had a significant impact on enzyme activity (Fig. 4a–c and Table S5).

Fig. 4.

Validating key binding pocket residues of Erg11p by DS. a Two-dimensional amino acid site map of Erg11p residues involved in binding lanosterol substrate based on alanine scanning. b Three-dimensional amino acid site map of Erg11p residues involved in lanosterol substrate binding based on alanine scanning. c Mutation energy for single point mutants. d Production of ergosterol by double mutants following alanine scanning. e Production of ergosterol by single point mutants(*P < 0.05 vs. WT; **P < 0.01 vs. WT; ***P < 0.001 vs. WT; ****P < 0.0001 vs. WT)

Fifteen mutation sites with a stable mutation effect were selected, and corresponding mutated plasmids were constructed and transformed into the S. cerevisiae WQ-10 strain for experimental verification. The results showed that production of ergosterol by mutants F384A, L380A and G314A was increased by > 25%, while Y126A increased production by 24%. Meanwhile, 47% of site mutations had no significant effect on Erg11p enzyme activity. Ergosterol production by the best mutant F384A was increased by 34%, reaching 657.8 mg·L⁻¹ (Fig. 4e). Therefore, residues F384, L380 and G314 were identified as key sites for binding substrates. Mutations were combined for four loci (F384A, L380A, G314A and Y126A) with good single point effects. Six mutant plasmids were constructed and transformed into the S. cerevisiae WQ-10 strain for experimental verification, but the combined mutation effect was not significant (Fig. 4d).

Site-directed saturation mutation of Erg11p

Site-directed saturation mutation of enzymes is used to investigate the effects of specific amino acid residues on enzyme activity, stability, and substrate specificity (Li et al. 2023). Due to certain amino acid mutations being overlooked in virtual computations, this study aimed to identify the optimal amino acids for these sites to further enhance the activity of the enzyme. A decrease in binding energy typically indicates more stable enzyme–substrate interactions or a lower reaction energy barrier, which directly enhances enzyme activity and catalytic efficiency. To achieve this, kinetic simulation of the enzyme-ligand complex was conducted for 100 ns at 300 K (Fig. 5a). To identify the key binding sites of substrates, force analysis and disassembly based on MMPBSA were used (Fig. 5b). RMSD analysis results indicated that the system gradually entered equilibrium after 5 ns (Fig. S2). Based on the priority of energy contribution, eight weak binding sites were selected: M509, L380, F384, Y140, Y72, F241, G314 and P238 (Fig. 5b).

Fig. 5.

Site-directed saturation mutation of Erg11p. a Crystal structure of Erg11p bound to lanosterol for predicting saturated mutation sites. b Energy map of residues involved in binding lanosterol based on force analysis and disassembly of MMPBSA by Rosetta between. c Production of ergosterol by double mutants following site-directed saturation mutation. d Production of ergosterol by single point mutants following site-directed saturation mutation (*P < 0.05 vs. WT; **P < 0.01 vs. WT; ***P < 0.001 vs. WT; ****P < 0.0001 vs. WT)

Mutant plasmids were introduced into the S. cerevisiae WQ-10 strain for experimental verification. The results showed that saturation mutations at sites M509, Y140, Y72, and P238 did not significantly increase the yield of ergosterol, with fluctuations ranging from 10  to  20%, while the yield of ergosterol for mutants at sites L380 and G314 was increased by 20–30%, and the maximum increase of 33% was observed for F241. It is worth noting that saturation mutation at F384 resulted in extremely significant changes in ergosterol production, with significant fluctuations ranging from 30 to  50%. The best mutant F384E showed a 50% increase, reaching 733.8 mg·L⁻¹, confirming the strengthening of weak action sites and enhancing the effectiveness of enzyme catalysis (Fig. 5d).

Paired combination mutations were performed at four loci, F384D, F384E, G314V and G314W, that had a positive effect as single point mutations. Four corresponding mutant plasmids were constructed and transformed into the S. cerevisiae WQ-10 strain for experimental verification. Ergosterol production for the combined F384D/G314W mutants was increased by 34%, compared with an increase of 15% for F384E/G314V (Fig. 5c).

Mechanism of the high conversion rate of the Erg11p mutant

MD simulation is employed to examine the structure and dynamics of specific S. cerevisiae enzymes, offering insights into substrate binding, reaction mechanisms, stability and molecular interactions (Moretti et al. 2017). We used MD to simulate the motion of two complexes, Erg11p-Haem and Erg11p-F384E-Haem, at 300 K, to explore how the Erg11p-F384E mutation boosted ergosterol production. RMSD analysis of Erg11p and its mutant Erg11p-F384E underwent multiple equilibrium stages during the 100 ns simulation, indicating slight RMSD fluctuation differences. Erg11p-F384E exhibited a higher skeletal RMSD, with Erg11p peaking at 0.49 nm and Erg11p-F384E reaching a maximum of 0.65 nm (Fig. 6a). The RMSD for Erg11p spiked at 14 ns, peaking at 23 ns, then stabilised around 0.28 nm with minor fluctuations until the end of the simulation. The RMSD for Erg11p-F384E varied between 0.1 and 0.2 nm initially, peaking at 0.37 nm at 7 ns, then surged and stabilised after 36 ns, reaching its maximum of 0.65 nm at 65 ns, although some small and large fluctuations were observed.

Fig. 6.

Mechanism of the high conversion rate of Erg11p and Erg11p-F384E. a RMSD changes for Erg11p and Erg11p-F384E under 100 ns MD simulation. b RMSF changes for Erg11 and Erg11p-F384E under MD simulation. c Two-dimensional amino acid site map for the best Erg11p cluster. d Two-dimensional amino acid site map for the best Erg11p-F384E cluster. e Three-dimensional structure of lanosterol and heme in Erg11p. f Three-dimensional structure of lanosterol and heme in Erg11p-F384E

RMSF analysis revealed differences of residue dynamic behaviour in the simulation trajectory for Erg11p and Erg11p-F384E due to the codon 384 mutation. Erg11p-F384E exhibited significantly higher RMSF values and more pronounced fluctuations than Erg11p, indicating increased flexibility. Specifically, Erg11p showed a sharp increase in RMSF between residues 50 and 70, then stabilised, but remained relatively stable. However, Erg11p-F384E displayed its highest RMSF value for amino acid residues 6–36, with a maximum RMSF value of 1.14 nm, compared with 0.69 nm for Erg11p (Fig. 6b). Erg11p-F384E exhibited significant fluctuations and deviations from the WT structure throughout the simulation, highlighting the impact of the mutation on residue flexibility. Notably, Erg11p-F384E also showed increased RMSF for residues 437–442, a region distant from the mutation site but close to the (Fig. 6e–f). This remote effect might facilitate bending of the carbon side chain of the lanosterol substrate towards the heme center, enhancing Erg11p enzyme activity, which may promote the catalytic reaction and improve the yield (Uversky et al. 2013).

Scaling up the production of ergosterol through fed-batch fermentation

To further enhance the yield of the ergosterol product and to examine the scaled-up production capabilities of WT and mutant F384E enzymes, we conducted fed-batch fermentation in 5 L bioreactors for both strains over 80 h (Fig. 7). The results showed that the ethanol concentration for WT reached a low level around 14 h, prompting glucose supplementation. By 22 h, biomass reached 92, prompting addition of organic nitrogen sources. Cell growth peaked at 40 h with biomass reaching 121.5, and by 48 h ergosterol accumulation reached its maximum of 2340.1 mg·L⁻¹. Subsequently, WT activity decreased, leading to a decline in ergosterol accumulation. For the F384E mutant, ethanol concentration reached a low level around 22 h, prompting glucose supplementation. By 24 h, biomass reached 92, with growth almost uninterrupted; biomass continued to increase, reaching a maximum of 128.5 by 48 h. Ergosterol accumulation peaked at 3067.5 mg·L⁻¹ after 71 h, an increase of 31% compared to WT.

Fig. 7.

Ergosterol production in a 5 L bioreactor. a Fermentation for 80 h with WT Erg11p. Glucose and (NH₄)₂SO₄ were added as supplementary carbon and nitrogen sources, respectively. b Fermentation for 80 h with Erg11p-F384E. Glucose and (NH₄)₂SO₄ were added as supplementary carbon and nitrogen sources, respectively

Discussion

To promote the bioconversion of ergosterol, microbial fermentation, enzyme catalysis and genetic engineering methods are often used, which can increase yield, purity, and efficiency, and can selectively synthesise specific sterol compounds. The present work mainly focussed on enzyme modification of Erg11p guided by computational approaches. Structural stability design, consensus design, alanine scanning mutagenesis, and site-specific saturation mutation modification resulted in mutant F384E with an ergosterol yield of 3067.5 mg·L⁻¹ in a 5 L bioreactor. It was worth noting that double mutant combinations displayed lower ergosterol conversion efficiency than single mutants, therefore only the best single mutant was selected for optimisation of fermentation.

Enzyme stability engineering is an effective strategy for increasing target product yields. By rationally designing and introducing the E56S mutation, the activity and thermal stability of Bacillus subtilis L-aspartate α-decarboxylase were significantly enhanced, resulting in a β-alanine yield of 215.3 g·L⁻¹ (Zhang et al. 2018). Using a semi-rational design strategy, combined with surface amino acid engineering and thermophilic amino acid preferences, phenylalanine aminomutase derived from Pantoea agglomerans was engineered, and the mutant K340R achieved a (S)-β-phenylalanine yield of 0.47 g·L⁻¹·h (Zhou et al. 2019). Molecular engineering optimisation of B. subtilis ADC enzymes yielded two mutants with 29–64% improved catalytic stability, with the best mutant increasing β-alanine production by 50% compared to the WT enzyme after 8 h (Pei et al. 2017). By lowering the folding energy to enhance enzyme structural stability, the P201H mutant of Erg11p achieved an ergosterol yield of 576.9 mg·L⁻¹, while the consensus design-selected mutant K352L further increased the yield to 677.9 mg·L⁻¹. In conclusion, enhancing enzyme structural and functional stability significantly improved target product yields, providing new insights and strategies for industrial enzyme engineering and biosynthetic pathway optimisation.

The identification and optimisation of key substrate-binding residues is an effective strategy for increasing the yields of target products. Two notable studies demonstrated the crucial impact of key residues on enzyme activity (Chen et al. 2021). The mutation of four residues around the pocket of the active site (D45W/L225Y/H226L/H343I) resulted in a 104-fold increase in the hydrolysis of methyl parathion (Yang et al. 2021). Meanwhile, conservation analysis of 13 mutation sites in the active site structure of SsChi18A revealed the important roles of Tyr286 and Glu287 (Zhao et al. 2023). Due to its relative rigidity in protein structures, substitution with Ala has the lowest impact on structure and function, and is commonly used for catalytic pocket modification through alanine scanning virtual mutation (Nakayoshi et al. 2021). Herein, production of ergosterol by the F384A mutant was increased to 657.8 mg·L⁻¹ through alanine scanning mutagenesis, demonstrating the effectiveness of this method. Using MD simulation and saturation mutation strategies, mutant F384E was developed with an ergosterol yield of 733.8 mg·L⁻¹, demonstrating the strong potential of saturation mutation in enzyme engineering.

Previous studies have not explored Erg11p using MD simulation or quantum mechanics. The present study investigated the increase in ergosterol production by the Erg11p-F384E mutant using MD simulation and other approaches. Liu et al. used MD simulation to verify the activity, structure, active sites and substrate channels of polygalacturonase under infrared single thermal and non-thermal effects (Liu et al. 2024). Kalita et al. used MD simulation and mixed QM/MM calculations to show that the effective C-H amination of engineered CYP411 involved both electronic and spatial effects (Kalita et al. 2021). Lisandra et al. provided structural insights into the coupling mechanism between CYTB5 and CYP17 interfaces through MD simulation (Lisandra et al. 2023). Following mutation at position 384, the RMSF for residues 437–442 was increased significantly, but in the Erg11p-F384E structure this is a rigid region distant from the mutation at position 384. Residues 437–442 were located on the other side of the heme centre, and they had a remote effect that caused the carbon side chain of the substrate lanosterol to bend towards the heme centre, forcing the hydroxyl group of the substrate closer to the heme centre, leading to an increase in Erg11p enzyme activity. As a result, the yield of ergosterol was increased.

By combining saturation mutation and MD simulation, mutant F384E was developed with an ergosterol yield of 3067.5 mg·L⁻¹ in a 5 L bioreactor. However, the combination of mutations actually reduced ergosterol production. This study focussed on screening and optimising single and double mutations, but multiple mutation strategies and combined approaches offer great potential for enhancing enzyme activity and ergosterol yield. Future research should explore the feasibility of multiple mutations, leveraging computer-aided design and high-throughput screening to optimise effects. Detailed analysis of S. cerevisiae Erg11p’s structure and function could help identify mutations that boost ergosterol production. Moreover, integrating molecular modelling with machine learning can accelerate rational design, enabling more accurate predictions of mutation effects on enzyme performance.

In summary, this study employed multiple enzyme engineering strategies to optimise Erg11p enzyme activity and increase ergosterol production. Enzyme molecular modification is a highly customised approach that can meet specific application requirements by altering the properties and functions of enzymes. The future research directions should include identifying factors that regulate the subcellular localisation and function of Erg proteins, as well as controlling fungal specificity for ERG gene expression, such as characterising the structure and function of regulatory proteins. This could help to improve the quality and quantity of steroid production in S. cerevisiae, providing better solutions for applications in related fields.

Supplementary Information

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Author contributions

R. L.: conceived and designed the study performed the experiment and prepared the original draft. K. X.: performed the experiment and analysed the data. X. W.: assisted in conceptualising and designing this study and provided technical help. W. W.: investigation and validation. Q. C.: provided technical help. Z. Q.: revised the original draft. W. Z.: investigation and validation. J. Z.: supervision, funding acquisition, writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Data availability

The authors declare that all data supporting the findings of this study are available in the article and its supplementary file, Figs. 1, 2, 3, 4, 5, 6, 7 and Table 1 provided in the article, Figs S1, S2 and Tables S1, S2, S3, S4, S5 provided in the supplementary materials.

Declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical standards

This article does not contain any studies with human participants or animals performed by any of the authors.