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Article

Microbiological Bioreduction of Bulky–Bulky Pyrimidine Derivatives as an Alternative to Asymmetric Chemical Synthesis

by
Renata Kołodziejska
1,*,
Hanna Pawluk
1,
Agnieszka Tafelska-Kaczmarek
2,*,
Szymon Baumgart
3,
Renata Studzińska
3,
Agnieszka Kosinska
4 and
Marcin Kwit
5
1
Department of Medical Biology and Biochemistry, Faculty of Medicine, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 87-100 Toruń, Poland
2
Department of Organic Chemistry, Faculty of Chemistry, Nicolaus Copernicus University, 87-100 Toruń, Poland
3
Department of Organic Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University, 87-100 Toruń, Poland
4
Centre for Languages & International Education, University College London, 26 Bedford Way, London WC1H 0AP, UK
5
Department of Chemistry, Adam Mickiewicz University, 61-712 Poznań, Poland
*
Authors to whom correspondence should be addressed.
Catalysts 2024, 14(10), 667; https://doi.org/10.3390/catal14100667
Submission received: 3 September 2024 / Revised: 21 September 2024 / Accepted: 25 September 2024 / Published: 27 September 2024
(This article belongs to the Section Biocatalysis)
Browse Figures
Versions Notes

Abstract

:
Heterocyclic scaffolds are often present in many natural and non-natural products with important biological activity, such as synthetic intermediates used to synthesise many drugs. Among others, heterocycles based on a pyrimidine ring may have antioxidant, antibacterial, antiviral, antifungal, antituberculosis, and anti-inflammatory properties. The present study investigated commercially available microbial biocatalysts in the enzymatic desymmetrization reaction of bulky–bulky ketones derived from pyrimidine bases. The influence of some parameters on the efficiency of biocatalysis, i.e., the substrate concentration and pH of the reaction medium, was evaluated. In the one-step bioreduction catalysed by Saccharomyces cerevisiae, secondary alcohols with a defined absolute configuration were obtained with high enantiomeric excess up to 99% ee and moderate conversion. Biocatalysis offers economic and environmental benefits as an alternative to conventional methods, becoming a powerful tool in the synthesis of crowded alcohols.

1. Introduction

In his recent review, Roger A. Sheldon widely addressed the increasing role of biocatalysts as important reagents in the sustainable and green manufacturing of chemicals [1].
In the first textbook titled “Green Chemistry”, Paul Anastas and John Warner described twelve principles that would lead to the design, development, and evaluation of environmentally friendly synthetic methods. Additionally, the U.S. Environmental Protection Agency (EPA) launched the “Alternative synthetic design for pollution prevention” program that focused on innovative approaches in many fields of chemistry with the aim to reduce the application of toxic reagents significantly [1,2].
This global movement towards more environmentally friendly scientific research and industry placed biotechnology as one of the fastest-growing technologies. Biotechnology, especially biocatalysis, employs natural biological systems, such as protein enzymes, to perform chemical reactions. This strategy fulfils the underlying principles of “green chemistry”, which involves the search for new techniques that combine simplicity and safety and thus lower the costs.
The production of enantiomerically pure compounds is essential for drug development and often involves asymmetric synthesis, which allows for the creation of chiral compounds.
In general, many enantioselective reactions are supported by a number of different catalysts, including biocatalysts, which operate under mild conditions and provide high specificity for substrates [3,4,5].
The natural selectivity for a certain stereoisomer makes biocatalysts more sustainable and greener catalysts in asymmetric reactions in comparison with traditionally used transition metals. Heavy metal catalysts do not always give satisfactory results with respect to enantiomeric excesses. Moreover, their use is associated with significant costs and often leads to environmental pollution.
The reaction conditions for biocatalysts are atmospheric pressure, room temperature, and aqueous solutions, which positively results in environmental protection and lowers the costs of the process. Because of the highly advanced biotechnology and molecular biology, it is now possible to create enantiomerically pure building blocks of many pharmaceuticals. Most biologically active compounds have a heterocyclic unit [6] present in their structures. This includes a pyrimidine ring that may have antioxidant, antibacterial, antiviral, antifungal, antituberculosis, and anti-inflammatory properties (Scheme 1) [6,7,8,9,10,11,12,13,14].
This research focused on applying biocatalysts to the selective desymmetrization of carbonyl compounds derived from pyrimidine bases, leading to secondary alcohols with a defined absolute configuration. This bioreduction was carried out in the presence of the commonly used yeast Saccharomyces cerevisiae, the microbiological preparation Blossom Protect containing the following strains of Aureobasidium pullulans: DSM 14940 and 14941, as well as isolated enzymes from oxidoreductase classes. Previous research on the use of baker’s yeast, Aureobasidium pullulans, in enantioselective enzymatic desymmetrization (EED) has shown that those bioreagents can efficiently convert heterocyclic carbonyl compounds with good efficiency and selectivity [15,16,17,18].
The aim of the undertaken studies was to obtain enantiomerically pure chiral heterocyclic compounds with good yield. The influence of various parameters on the efficiency and stereospecificity of the biotransformation process was investigated.

2. Results and Discussion

2.1. Synthesis of the Carbonyl Pyrimidine Derivatives (1a, 1b, 2a, 2b)

Phenacyl derivatives (1a, 1b, 2a, 2b) were obtained by the reaction of 3-cyclohexyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (1) and 1-methyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (2) with the appropriate phenacyl bromide (2-bromoacetophenone/2,4′-dibromoacetophenone) in DMF–acetone (1:1 v/v) at room temperature (Scheme 2). The reaction was continued until the complete conversion of substrates. After purification, 1a, 2b, 2a, and 2b were obtained in isolated yields of 85%, 70%, 65%, and 77%, respectively.

2.2. Reduction of Carbonyl Pyrimidine Derivatives

Enantioselective enzymatic desymmetrization of carbonyl compounds is an example of a highly efficient method of converting prochiral starting materials into chiral products. Enzymatic catalysis transforms ketones into chiral alcohols, favouring one enantiomer over the other.
Mechanistically, it seems to be important to have a substrate that has different substituents attached to the carbonyl carbon, as the enzyme selectively reduces one face of the carbonyl group. However, not all asymmetric ketones can be readily desymmetrized because of their structural limitations. Namely, sterically bulky substituents such as the phenacyl derivatives of trimethylene uracyls may be challenging to undergo reduction because of the steric hindrance of the active carbonyl carbon.
The desymmetrization reaction of prochiral pyrimidine derivatives was carried out according to Scheme 3. Baker’s yeast, Blossom Protect preparation, selected A. pullulans strains, and commercially and non-commercially available dehydrogenases were used as biocatalysts.
Model reactions were performed for 1-(2-oxo-2-phenylethyl)-3-cyclohexyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (1a) and 1-methyl-3-(2-oxo-2-phenylethyl)-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (2a). Preliminary experiments revealed that the prochiral substrates were probably too bulky to activate biocatalysts (Tables S1–S3).
The obtained results indicated that S. cerevisiae acted as the most promising catalyst in the biotransformation of phenacyl derivatives of trimethylene uracils. Therefore, the main focus of this study concentrated on optimizing reaction conditions in the presence of this biocatalyst. In the first instance, the initial concentrations of the starting material were varied, and the reactions were performed at four different molarities for compounds 1a and 2a (2.1, 4.3, 8.5, and 17 mM), as shown in Table 1. In addition, the same biotransformation reactions were carried out for 4-bromophenyl derivatives (1b and 2b) at the abovementioned concentrations. Expect for changes in the molar concentrations of the substrates, the other conditions remained constant. This means that each experiment was performed in the presence of 0.5 g of S. cerevisiae, 3.75 mL of phosphate buffer (pH 6.0), 1 × 10−4 mol glucose at 30 °C, and 170 rpm for three days.
S. cerevisiae exhibited significantly higher catalytic activity for both 1a and 2a derivatives at a concentration of 2.1 mM. The conversion of 1N-phenacyl derivatives was lower than that of their 3N-phenacyl counterparts. Compound 1a′ reached a conversion of 7% for a substrate concentration of 2.1 mM and 4.3 mM. However, a steady decrease in conversion was observed with increasing concentration of the starting materials. Nevertheless, in each case, an enantiomerically pure product with R configuration was obtained regardless of changes in molarity. Reduction of 1b for the lowest concentration led to the (R)-alcohol with a higher, 19%, conversion and 67% ee.
Similar to 1N derivatives, the reactions’ outcomes for 3N analogues depended on the substrate concentration, especially for the para-bromophenyl derivative (2b). The increase in substrates’ concentrations led to the reduction of the conversion with the increase in the enantiomeric purity of the obtained products. The fungi biocatalyst exhibited high catalytic activity for 2.1 mM, allowing it to reach 98% and 97% conversion for 2a′ and 2b′, respectively. However, the enantioselectivity for this concentration was the lowest, and for 2a′, a slight decrease from 99% to 89% ee was observed. Unfortunately, in the case of 2b′, a much more significant decrease in enantiomeric purity was reported, from 82% to 44% ee.
In the next step, the effect of different pH values of the reaction mixtures was investigated. The tested reactions were catalysed by baker’s yeast and examined for the most optimal substrate concentrations of 2.1 mM and 4.3 mM, as illustrated by Table 2. S. cerevisiae-catalysed reactions were carried out at 30 °C for three days in a system consisting of 0.5 g S. cerevisiae and 1 × 10−4 mol glucose in 3.75 mL phosphate buffer.
The most promising results for compound 1a, leading to 22% conversion, were obtained in solutions with pH 7.0. In each case, regardless of the pH value, 1a was reduced to give products with a 99% enantiomeric excess. However, for 1b, the highest conversion (22%) was obtained at pH 8.0, and the highest selectivity of 90% ee was recorded in solutions with a pH of 5.0. 3N-phenacyl derivatives proved to be most efficiently reduced in solutions with a pH of 6.0. The highest enantiomeric excess for both 2a (94% ee) and 2b (79% ee) was observed at pH 7.0.
Based on the obtained results, it could be deduced that at the 4.3 mM concentration of the starting reagent and pH 6, the highest conversions were seen (Table 2). On the other hand, at the 2.1 mM concentration, slight differences in the selectivity of the process were observed for the unsubstituted phenacyl derivatives in comparison with the para-bromophenacyl analogues. In addition, the highest enantiomeric excess of 1b′ (97% ee) and 2b′ (99% ee) was reported at pH 7.0.
As illustrated by the data in Table 2, it can be concluded that both the concentration and pH of the reaction medium have a significant impact on the selectivity of the process. Smaller differences in enantiomeric purity are noticeable for the substrate concentration of 4.3 mM at pH 5.0, 7.0, and 8.0. The decrease in the initial concentration of the substrate leads to a decrease in enantioselectivity, especially for the para-bromophenyl analogues. The best results for the concentration of 2.1 mM were obtained at pH 5.0. Previous studies show the dependence of efficiency, enantioselectivity, and stereopreference on the substrate concentration and pH of the biotransformation medium [17,19].
The stereochemistry of products was confirmed by circular dichroism. 1-methyl-3-(2-oxo-2-phenylethyl)-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (2a) and 1-methyl-3-(2-oxo-2-(4-bromophenyl)ethyl)-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (2b) predominantly produced the (S)-enantiomer. However, for 1-(2-oxo-2-phenylethyl)-3-cyclohexyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (1a) and 1-(2-oxo-2-(4-bromophenyl)ethyl)-3-cyclohexyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (1b), a greater proportion of the (R) optical isomer was found. Those findings could indicate that S. cerevisiae, depending on the substrate structure, selectively transfers one of the prochiral hydride ions from the cofactor to the Si or Re side of the carbonyl group, allowing for either (R)- or (S)-enantiomer to be obtained.
An attempt was also made to reduce phenacyl derivatives using various types of oxidoreductases such as the commercially available dehydrogenases listed below: alcohol dehydrogenase from E. coli; equine alcohol dehydrogenase, recombinant from E. coli; lactic acid dehydrogenase; and dehydrogenase from S. cerevisiae.
None of the commercially available enzymes participated in the reduction of either 1N- or 3N-phenacyl derivatives of trimethylene uracils. For comparison, the reaction was carried out for methyl naphthyl ketone; in each case, this ketone was reduced, but with different efficiency and selectivity, and only in the presence of alcohol dehydrogenase equine, recombinant from E. coli, was the reaction selective (Table S4).
The desymmetrization reaction of 1a was also performed in the presence of the following non-commercial dehydrogenases: pEG 53 Syn-ADH, Sphingobium yanoikuyae and pEG 105, Ras-ADH, Ralstonia sp. Only the pEG 105 dehydrogenase allowed for obtaining a product with a 14% conversion with an enantiomerical purity of over 99% ee. The reduction in the presence of dehydrogenases was carried out under the following conditions: lyophilized biocatalyst (10 mg), 7.5 × 10−5 mol glucose, 2.5 × 10−3 mmol mM NADH, and 0.1 M phosphate buffer (pH 7.5)/2-PrOH (0.500 mL, 90:10, v/v).
The 1N- and 3N-phenacyl derivatives of pyrimidines proved to be challenging starting materials, and their microbiological reduction was unsuccessful. As a result, an attempt was made to investigate the traditional asymmetric synthesis to determine if a successful reduction of those organic bases would occur. In general, transition metal-based catalysts are used to reduce carbonyl compounds to obtain optically pure secondary alcohols. Asymmetric transfer hydrogenation (ATH) was used for the reduction of 1a and 2a. The chiral catalyst was RhCl[(R,R)-TsDPEN](C5Me5), and formic acid acted as the source of hydrogen ions. Unfortunately, the attempt to reduce prochiral carbonyl derivatives of pyrimidines was unsuccessful. After 24 h, only the unreacted starting materials were identified.
This could suggest that the structure of the starting substrates was responsible for the unsuccessful reduction. Bulky substituents around the carbonyl group made access to the reactive carbon sterically hindered. This could prevent the formation of a stable six-membered intermediate and thus the effective transfer of the hydride ion and proton from the catalyst to the substrate. Therefore, ATH could not be applied as a method of synthesizing enantiomerically pure secondary alcohols, in the case of 1N- and 3N-phenacyl derivatives of trimethylene uracils. Consequently, bio-organic methods seemed to be more effective in asymmetric reductions, suggesting that they may become an alternative solution to chemical catalysis.

2.3. Assessment of the Biological Activity of Compounds Based on PASS Online

The biological activity of the prepared compounds was predicted using PASS (Prediction of Activity Spectra for Substances) Online software (accessed on 24 October 2023, Table 3). PASS Online is a program that predicts the possible biological activity of organic compounds based on the structural database of 4000 biological active substrates [20]. The PASS program calculates biological activities in the form of probable activity (Pa) and probable inactivity (Pi), and their prediction values range from 0 to 1 [21]. If the Pa value is greater than 0.7 (Pa > 0.7), it means that the substance is very likely to show activity and there is a high chance that the tested compound is an analogue of a known pharmaceutical [22]. However, if the obtained values are 0.5 < Pa < 0.7, the probability of showing activity in real life is lower, and the compound differs from known pharmacologically active substances [22]. If Pa < 0.5, it is unlikely that the tested substance will demonstrate the desired activity [22].
The results of biological activity prediction obtained from the PASS Online program show that almost all synthesized compounds have a probability of more than 50% showing nootropic activity (51.8–75.6%) and can act as a kidney function stimulant (50.2–69.9%) (Table 3). Moreover, derivatives 1–1b′ and 2 demonstrated an antieczema effect with a 57.4–71.3% probability. It should also be noted that compounds 1 and 2 and their derivatives (1a, 1a′, 2a, 2b), with a probability of >73%, may behave as antagonists of α2β2 nicotinic acetylcholine receptors (α2β2 nAChR). Understanding the selective α2β2 nAChR antagonist will allow for a determination of what role this nAChR subtype plays in the effects of nicotine on the human body. Compounds 1a′, 1b′, 2a′, and 2b′ show a high probability (79.7–88.5%) of cerebral anti-ischemic effects. A similarly high probability was found for compounds 1 (78.6%) and 2 (84.8%) in the case of inhibiting testosterone 17β-dehydrogenase (NADP+). The 17β-hydroxysteroid dehydrogenases (17β-HSDs) play an important role in the regulation of steroid hormones, such as oestrogens and androgens. It catalyses the reduction of 17-ketosteroids or the oxidation of 17β-hydroxysteroids using NAD(P)H or NAD(P)(+) as a cofactor. Therefore, inhibitors of 17β-HSDs could be useful tools to elucidate the role of these enzymes in particular biological systems or for a therapeutic purpose, especially to block the formation of active hydroxysteroids that stimulate oestrogen-sensitive pathologies (breast, ovarian, and endometrial cancers) and androgen-sensitive pathologies (prostate cancer, benign prostatic hyperplasia, acne, hirsutism, etc.) [23].

2.4. Determination of the Absolute Configuration of Chiral Alcohols 1a′, 1b′, 2a′, and 2b′

Enantiomers’ differential biological activities and chiral compounds’ optical activities are evidence of molecular chirality. Among the methods routinely used for measuring optical activity, circular dichroism (CD), both vibrational (VCD) and electronic (ECD) circular, provides valuable structural information, including the most important one, which is the absolute configuration of given compounds [24,25]. Since the bioreduction products exhibit biological activity, knowing their absolute configuration seems essential.
To evaluate the stereochemistry of these compounds, experimental (ECD) and theoretical studies were performed on the selected alcohols 1a′, 1b′, 2a′, and 2b′. These studies were based on a well-established protocol, which was previously successfully applied to solve problems with the stereochemistry of organic compounds [15,26]. The protocol includes measurements of ECD spectra of the given compounds in solvents of different polarity, namely, cyclohexane and acetonitrile, followed by theoretical simulations of the spectra at the suitable density functional theory (DFT) level with and without the use of the integral equation formulation of the polarizable continuum model (IEFPCM) solvent model (see the Supplementary Materials for details) [27].
The systematic conformational searches, performed at the molecular mechanics level for the compounds with an assumed R absolute configuration at the stereogenic centre, constituted the first steps of the analysis. In the next step, all the conformers were pre-optimized (at the B3LYP/6-31g(d) level) and, after removing the duplicates, were fully optimized at the B3LYP/6-311++G(d,p) level with and without the use of the IEFPCM solvent model. Thus, the obtained structures were real minimum conformers, as the frequency calculations confirmed (see Supplementary Materials for the detailed results). For the conformers ranging in the free energy values from 0 to 2 kcal mol−1, the ECD spectra were calculated using CAM-B3LYP [28] and M06-2X [29] hybrid functionals, both in conjunction with the 6-311++G(2d,2p) basis sets and the IEFPCM solvent model of acetonitrile [27,30]. After Boltzmann averaging, the calculated ECD spectra were compared with the experimental ones, showing good to excellent compatibility (see Figures S33–S48 in the Supplementary Materials).
Despite the polarity of the solvent used, the shape of ECD spectra measured for a given compound is similar. However, in the case of 1a′ and 1b′, the ECD spectra measured in non-polar and polar solvents differ in the intensity of specific Cotton effects. While the long-wavelength CEs (appearing at around 280 nm) reach higher values in non-polar cyclohexane, in the higher energy region, the trend is reversed. In a polar environment, the short-wavelength CEs become more intense. The long-wavelength Cotton effects are associated with the UV bands at around 280 nm of ε ca. 10,000. In contrast, the CEs in the higher energy region are related to two UV bands. The first of moderate intensity (ε ca. 10,000), appears at 220 nm, and the second UV band is easily visible for the bromine-substituted derivatives and appears at around 190 nm. This band will reach a maximum below 185 nm for the rest of the compounds.
As the theoretical calculations reveal, the studied alcohols are described by quite complex conformational dynamics revealed by the number of thermally available conformers. Many low-energy conformers in equilibrium make none of them significantly dominant. Strictly speaking, the population of the ΔΔG-based lowest energy conformers oscillates around 30%. It is worth indicating that even in such populations, the lowest energy conformers make a dominant contribution to the averaged ECD spectra; therefore, the following discussion will be limited to them only.
The factors determining the structure of the lowest energy conformers of alcohols 1a′, 1b′, 2a′, and 2b′ are not as clear as they could be initially indicated. The presence of a polar C=O functional group and OH groups attached to the stereogenic centre gives the impression that the formation of an intermolecular H-bond (C=O···H-O(C)) will be the governing structural factor. Indeed, the seven-membered O-H···O=C hydrogen-bonded ring is found as the lowest energy conformer No 3 of 2a′ and the lowest energy conformer No. 3 of 2b′ (Figure 1). The calculated O-H···O distance ranges from 1.899 to 1.887 Å, respectively, for the lowest energy conformers of 2a′ and 2b′. This suggests that this hydrogen bond could be classified as relatively strong. Unexpectedly, the conformation of the lowest energy conformers of 1a′ and 1b′ (shown in Figure 1) is not determined by possible C=O···H-O(C) hydrogen bonding interactions. This suggests that the conformation of the alcohols 1a′ and 1b′ is controlled mainly by the steric and, to a lesser extent, electrostatic interactions between O, N, and H atoms. It is important to mention that changing the environment from non-polar to polar (acetonitrile) did not significantly alter the conformational preferences of the alcohols 1a′, 1b′, 2a′, and 2b′.
Figure 2 shows selected examples of the experimental and calculated ECD spectra of alcohols 1a′ and 2a′, whereas the remaining data are shown in the Supplementary Materials. The agreement between the experimental, measured in a non-polar environment, ECD spectrum of 1a′ and the one calculated and ΔΔG-based Boltzmann averaged is perfect. Therefore, the R absolute configuration of the actual compounds is confirmed. In contrast, the experimental and calculated ECD spectra of 2a′ are mirror reflections. Thus, the absolute configuration of the actual compound is S. In both cases discussed here, the dominant contributions to the overall ECD spectra have the lowest energy conformers. In the case of the remaining compounds, again, for alcohol 1b′, the determined absolute configuration is R, and the opposite is S for the main product of the reduction of 2b. The agreement between the experimental, measured in acetonitrile ECD, spectra for the alcohols 1a′, 1b′, 2a′, and 2b′ and the calculated ones is acceptable.

3. Materials and Methods

3.1. General Information

Nuclear magnetic resonance (NMR) spectra were obtained with Bruker Avance III spectrometers (Billerica, MA, USA, 700 MHz/176 Hz). Chemical shifts are reported in ppm from tetramethylsilane (TMS) as an internal standard.
HPLC analyses were performed on a Shimadzu SCL-10A VP. Compounds 1a, (S)-1a′, and (R)-1a′ were separated on a column Lux® 5µ Cellulose-3, LC Column 250 × 4.6 mm, Phenomenex (Warsaw, Poland). The mobile phase was n-hexane and propan-2-ol (90:10 v/v), at the flow rate of 0.6 mL per min. The retention times of 1a, (S)-1a′, and (R)-1a′ were 50.5 min, 12.1 min, and 18.9 min, respectively. Compounds 1b, (S)-1b′, and (R)-1b′ were separated on a column Lux® 5µ Cellulose-2, LC Column 250 × 4.6 mm, Phenomenex (Poland). The mobile phase was n-hexane and propan-2-ol (80:20 v/v), at the flow rate of 0.6 mL per min. The retention times of 1b, (S)-1b′, and (R)-1b′ were 56.2 min, 11.7 min, and 13.7 min, respectively. Compounds 2a, 2b, (S)-2a′/2b′, and (R)-2a′/2b′ were separated on a column Lux® 5µ Cellulose-1, LC Column 250 × 4.6 mm, Phenomenex (Poland). The mobile phase was n-hexane and propan-2-ol (70:30/80:20 v/v), at the flow rate of 0.6/0.75 mL per min. The retention times of 2a, 2b, (R)-2a′/2b′, and (S)-2a′/2b′ were 60.5 min, 63.7 min, 28.8/41.8 min, and 33.5/44.8 min, respectively.
The samples were incubated in an orbital shaker (VORTEMP 1550 S2050; Equimed, Kraków, Poland).

3.2. Reagents and Solvents

The chemical substances of analytical grade were commercially available, and they included N,N-dimethylformamide (DMF), dimethyl sulfoxide (DMSO), ethanol, ethyl acetate, glucose, n-hexane for HPLC, propan-2-ol for HPLC, toluene, MgSO4, Na2HPO4, NaH2PO4 from POCH (Gliwice, Poland), 2,4′-dibromoacetophenone (4′-bromophenacyl bromide) from Sigma Chemical CO. (St. Louis, MO, USA), sodium borohydride (NaBH4) from Acros Organics (Waltham, MA, USA), 2-bromoacetophenone from Sigma Aldrich (Bengaluru, India), potato-dextrose broth from Sigma-Aldrich (St. Louis, MO, USA), ammonium chloride from Centro-Chem (Lublin, Poland), NADPH from Roche Diagnostics GmbH (Mannheim, Germany), and triethylamine anhydrous from Warchem (Warsaw, Poland). The biocatalysts included the following: Blossom Protect™ from Koppert Biological Systems (Wien, Austria) containing germinating fungal Aureobasidium pullulans strains DSM 14940 and 14941 (500 g per 1 kg of the preparation), strains of Aureobasidium pullulans 33, 52, 62, 118, and 158 (gift, Warsaw University of Technology), and instant yeast—Saccharomyces cerevisiae from Dr.Oetker (Gdańsk, Poland). The dehydrogenases included the following: alcohol dehydrogenase from E. coli (E.C. 1.1.1.1), ≥0.5 U/mL from Sigma-Aldrich (USA), equine alcohol dehydrogenase, recombinant from E. coli (E.C. 1.1.1.1), ≥0.5 U/mg, from Sigma-Aldrich, (Darmstadt, Germany), alcohol dehydrogenase from Saccharomyces cerevisiae (E.C. 1.1.1.1), 310 U/mg from Sigma-Aldrich (USA), lactic acid dehydrogenase from bovine heart (EC 1.1. 1.27), 1000 U/mL from Sigma-Aldrich (USA), and pEG 53 Syn-ADH, Sphingobium yanoikuyae and pEG 105, Ras-ADH, Ralstonia sp. (gift, Warsaw University of Technology).

3.3. General Procedure

3.3.1. Synthesis of Phenacyl Derivatives

The substrate (1.21 mmol of 3-cyclohexyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (1)/1-methyl-6,7-dihydro-1H-cyclopenta[d]pyrimidine-2,4(3H,5H)-dione (2)), potassium carbonate, and phenacyl bromide (2,4′-dibromoacetophenone/2-bromoacetophenone) were mixed in a molar ratio of 1:1:1 and placed in a round-bottom flask. The solvent DMF–acetone was added in a volume ratio of 1:1 (v/v). The whole was stirred at room temperature. The progress of the reaction was monitored by TLC. The chromatogram was developed in the system of n-hexane and ethyl acetate (1:3 v/v) or toluene and ethyl acetate (7:3 v/v). After the complete conversion of the substrate, the reaction was finished. The solvents were evaporated on a rotary evaporator. The crude product was dissolved in a small amount of ethyl acetate and then purified using preparative liquid chromatography (PLC).

3.3.2. Reduction of 1a, 1b and 2a, 2b Using NaBH4

The substrate (2 × 10−4 mol) was dissolved in 2 mL of ethanol, and then, the resulting solution was cooled to 0 °C. After 30 min, 4 mg (0.1 mmol) NaBH4 was added. The reaction was monitored by TLC. After the complete reduction of the substrate, 5 mL of 2 M hydrochloric acid was introduced to stop the reaction. Then, 25 mL of distilled water was added to the reaction mixture and extracted four times with ethyl acetate (25 mL). The organic phase was evaporated on the rotary evaporator under reduced pressure.

3.3.3. Bioreduction of Ketones by S. cerevisiae

For a typical experiment, 1 × 10−4 mol glucose was added to a suspension of biocatalyst in 3.75 mL of potassium phosphate buffer, and the resulting suspension was incubated in an orbital shaker (150/170 rpm) for 30 min at 30 °C. After pre-incubation, the substrate was added in the following amounts: 7.8 × 10−3 (2.7 mg of 1a, 3.4 mg of 1b, 2.2 mg of 2a, 2.8 mg of 2b; Table 1 and Table 2), 1.6 × 10−2 (5.6 mg of 1a, 6.9 mg of 1b, 4.5 mg of 2a, 5.8 mg of 2b; Table 1 and Table 2), 3.2 × 10−2, and 6.4 × 10−2 mmol (11/22 mg of 1a, 14/28 mg of 1b, 9/18 mg of 2a, 12/24 mg of 2b; Table 1). The substrate was dissolved in 0.5 mL of ethanol and added to the biocatalyst suspension in phosphate buffer at the following pH values: 5.0/6.0/7.0/8.0. Next, Falcon tubes were stirred at the same temperature for 3 days. After the reaction, the microorganism was filtered off the supernatant and washed with distilled water. The solid residue was washed with ethyl acetate, and the combined aqueous phases were extracted with ethyl acetate (3 × 20 mL). The collected organic layer was dried over anhydrous MgSO4, and the solvent was evaporated in a vacuum. The conversion degrees of the substrates and enantiomeric ratios of the products were determined on the HPLC system using a chiral column.

3.4. Calculations Details

The absolute configuration was determined for the enantiomers of compounds 1a′, 1b′, 2a′, and 2b′ obtained by separation of the racemic mixture on a chiral semipreparative Celluose-3 column.
The theoretical approach that was used in this work is common to all studied structures and includes (i) conformational search at the molecular mechanics level (MM3); (ii) pre-optimization at the B3LYP/6-31G(d) level to reduce the number of thermally accessible conformers; (iii) parallel re-optimization of conformers found at the low-DFT level at the B3LYP/6-311++G(d,p) level in the gas phase and with the use of the IEFPCM solvent model for acetonitrile, followed by frequency calculations to confirm the stability of the received structures; (iv) calculations on relative energies (ΔEDFT and ΔΔGDFT) using the Boltzmann distribution at T = 298.15 K; and (v) rotatory strength calculations at the TD-DFT/6-311++G(d,p) level for all stable conformers of relative energies ranging from 0.0 to 2.0 kcal mol−1. A PA preliminary former distribution search was performed by the Scigress package 2013, version 2.5; Fujitsu Ltd.: Tokyo, Japan [31] using the MM3 molecular mechanics force field for the model molecules 1a′, 1b′, 2a′, and 2b′—with the assumed R configuration at the stereogenic centre, all possible conformers were analysed using the systematic search methodology. Minimum energy conformers of relative steric energies (ΔESE) up to 10 kcal mol−1 found by molecular mechanics were further fully optimized at the B3LYP/6-31G(d) level, as implemented in the Gaussian09 package [30], which significantly reduced the number of conformers. Higher accuracy calculations were performed at the B3LYP/6-311++G(d,p) level with and without the use of a solvent model [27]. The conformers obtained at the DFT/6-311++G(d,p) level were the real minima (no imaginary frequencies were found). Total and free energy values were calculated and used to obtain the Boltzmann population of conformers at 298.15 K. Only the results for conformers that differed from the most stable one by less than 2 kcal mol−1 were considered for further calculations, following a generally accepted protocol [26]. The TD-DFT/6-311++G(2d,2p) calculations of ECD were performed for all structures re-optimized at higher levels of theory. We used three different density functionals to calculate rotatory strengths, namely, CAM-B3LYP [28] and M06-2X [29] functionals. Rotatory strengths were calculated using both length and velocity representations. In the present study, the differences between the length and velocity representations of the calculated values of the rotatory strengths were quite small, and for this reason, only the velocity representations were further used. The CD spectra were simulated by overlapping Gaussian functions for each transition according to the procedure previously described and using half bandwidth4 eV [32]. It should be noted that there were no substantial differences between ECD spectra calculated with these three functionals for the same molecule.

4. Conclusions

In summary, the conducted experiments showed that the best catalyst in the bioreduction reaction of pyrimidine carbonyl derivatives was the commonly used Saccharomyces cerevisiae. In a relatively simple, economical, and ecological synthesis, secondary alcohols were obtained with a good degree of conversion and high optical purity up to 99% ee. The yield and selectivity of the process were significantly influenced by the substrate concentration and the pH of the reaction medium. In general, reducing the initial concentration of the reagent resulted in an increase in yield and a decrease in the enantiomeric purity of the product. In addition, para-bromophenyl derivatives were reduced with lower conversion and enantioselectivity than their unsubstituted analogues.
The stereopreference of the biocatalysts depended on the position of the phenacyl group in the pyrimidine ring. The 1N derivatives were reduced to (R)-products, while the 3N derivatives were reduced to alcohols with the opposite configuration.
It can be concluded that the yeast S. cerevisiae may be a potentially useful tool in the synthesis of sterically congested chiral heterocyclic compounds with a defined absolute configuration.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/catal14100667/s1: Figure S1: The 1H NMR spectra of 1a; Figure S2: The 1H NMR spectra of 1b; Figure S3: The 1H NMR spectra of 1a′; Figure S4: The 1H NMR spectra of 1b′; Figure S5: The 1H NMR spectra of 2a; Figure S6: The 1H NMR spectra of 2b; Figure S7: The 1H NMR spectra of 2a′; Figure S8: The 1H NMR spectra of 2b′; Figure S9: The 13C NMR spectra of 1a; Figure S10: The 13C NMR spectra of 1b; Figure S11: The 13C NMR spectra of 1a′; Figure S12: The 13C NMR spectra of 1b′; Figure S13: The 13C NMR spectra of 2a; Figure S14: The 13C NMR spectra of 2b; Figure S15: The 13C NMR spectra of 2a′; Figure S16: The 13C NMR spectra of 2b′; Figure S17: Structures of the low-energy conformers of (R)-1a′, calculated at the B3LYP/6-311++G(d,p) level; Figure S18: Structures of the low-energy conformers of (R)-1a′, calculated at the IEFPCM/B3LYP/6-311++G(d,p) level; Figure S19: Structures of the low-energy conformers of (R)-1b′, calculated at the B3LYP/6-311++G(d,p) level; Figure S20: Structures of the low-energy conformers of (R)-1b′, calculated at the IEFPCM/B3LYP/6-311++G(d,p) level; Figure S21: Structures of the low-energy conformers of (R)-2a′, calculated at the B3LYP/6-311++G(d,p) level; Figure S22: Structures of the low-energy conformers of (R)-2a′, calculated at the IEFPCM/B3LYP/6-311++G(d,p) level; Figure S23: Structures of the low-energy conformers of (R)-2b′, calculated at the B3LYP/6-311++G(d,p) level; Figure S24: Structures of the low-energy conformers of (R)-2b′, calculated at the IEFPCM/B3LYP/6-311++G(d,p) level; Figure S25: UV (upper panel) and ECD (lower panel) spectra of alcohol 1a′(1), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S26: UV (upper panel) and ECD (lower panel) spectra of alcohol 1a′(2), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S27: UV (upper panel) and ECD (lower panel) spectra of alcohol 1b′(1), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S28: UV (upper panel) and ECD (lower panel) spectra of alcohol 1b′(2), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S29: UV (upper panel) and ECD (lower panel) spectra of alcohol 2a′(1), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S30: UV (upper panel) and ECD (lower panel) spectra of alcohol 2a′(2), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S31: UV (upper panel) and ECD (lower panel) spectra of alcohol 2b′(1), measured in acetonitrile (blue lines); Figure S32: UV (upper panel) and ECD (lower panel) spectra of alcohol 2b′(2), measured in acetonitrile (blue lines) and cyclohexane (black lines); Figure S33: UV (upper panel) and ECD (lower panel) spectra of 1a′(1) measured in cyclohexane (solid black lines) and calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest-energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S34: UV (upper panel) and ECD (lower panel) spectra of 1a′(1) measured in cyclohexane (solid black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S35: UV (upper panel) and ECD (lower panel) spectra of 1a′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S36: UV (upper panel) and ECD (lower panel) spectra of 1a′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S37: UV (upper panel) and ECD (lower panel) spectra of 1b′(1) measured in cyclohexane (solid black lines) and calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S38: UV (upper panel) and ECD (lower panel) spectra of 1b′(1) measured in cyclohexane (solid black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S39: UV (upper panel) and ECD (lower panel) spectra of 1b′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S40: UV (upper panel) and ECD (lower panel) spectra of 1b′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S41: UV (upper panel) and ECD (lower panel) spectra of 2a′(1) measured in cyclohexane (solid black lines) and calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S42: UV (upper panel) and ECD (lower panel) spectra of 2a′(1) measured in cyclohexane (solid black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S43: UV (upper panel) and ECD (lower panel) spectra of 2a′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S44: UV (upper panel) and ECD (lower panel) spectra of 2a′(1) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S45: UV (upper panel) and ECD (lower panel) spectra of 2b′(2) measured in cyclohexane (solid black lines) and calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S46: UV (upper panel) and ECD (lower panel) spectra of 2b′(2) measured in cyclohexane (solid black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S47: UV (upper panel) and ECD (lower panel) spectra of 2b′(2) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S48: UV (upper panel) and ECD (lower panel) spectra of 2b′(2) measured in acetonitrile (solid black lines) and calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level. The calculated ECD spectra were Boltzmann-averaged based on ΔE (red lines) and ΔΔG values (blue lines). Wavelengths were corrected to match the experimental UV maxima. The insert shows the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines); Figure S49: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1a′. Wavelengths were not corrected; Figure S50: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1a′. Wavelengths were not corrected; Figure S51: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1a′. Wavelengths were not corrected; Figure S52: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1a′. Wavelengths were not corrected; Figure S53: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1b′. Wavelengths were not corrected; Figure S54: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1b′. Wavelengths were not corrected; Figure S55: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1b′. Wavelengths were not corrected; Figure S56: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-1b′. Wavelengths were not corrected; Figure S57: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2a′. Wavelengths were not corrected; Figure S58: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2a′. Wavelengths were not corrected; Figure S59: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2a′. Wavelengths were not corrected; Figure S60: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2a′. Wavelengths were not corrected; Figure S61: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2b′. Wavelengths were not corrected; Figure S62: UV (upper panels) and ECD (lower panels) spectra calculated at the TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2b′. Wavelengths were not corrected; Figure S63: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-CAM-B3LYP/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2b′. Wavelengths were not corrected; Figure S64: UV (upper panels) and ECD (lower panels) spectra calculated at the IEFPCM/TD-M06-2X/6-311++G(2d,2p) level for individual low-energy conformers of (R)-2b′. Wavelengths were not corrected; Table S1: Biotransformation of 1a and 2a in the presence of S. cerevisiae; Table S2: Biotransformation of 1a and 2a in the presence of Blossom Protect; Table S3: Biotransformation of 1a and 2a in the presence of A. pullulans strains; Table S4: Reduction of methyl naphthyl ketone in the presence of commercial dehydrogenases; Table S5: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the B3LYP/6-311++G(2d,2p) level for low-energy conformers of 1a′; Table S6: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the IEFPCM(MeCN)/B3LYP/6-311++G(d,p) level for low-energy conformers of 1a′; Table S7: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the B3LYP/6-311++G(d,p) level for low-energy conformers of 1b′; Table S8: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the IEFPCM(MeCN)/B3LYP/6-311++G(d,p) level for low-energy conformers of 1b′; Table S9: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the B3LYP/6-311++G(d,p) level for low-energy conformers of 2a′; Table S10: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the IEFPCM(MeCN)/B3LYP/6-311++G(d,p) level for low-energy conformers of 2a′; Table S11: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the B3LYP/6-311++G(d,p) level for low-energy conformers of 2b′; Table S12: Total (E, in Hartree) and relative energies (ΔE, ΔΔG, in kcal mol−1), percentage populations (Pops.), and number of imaginary frequencies calculated at the IEFPCM(MeCN)/B3LYP/6-311++G(d,p) level for low-energy conformers of 2b′.

Author Contributions

Conceptualization, R.K.; methodology, R.K., H.P., and R.S.; validation, H.P. and R.K.; investigation, R.K. and M.K.; writing original draft preparation, R.K., R.S., S.B., and M.K.; writing—review and editing, R.K., H.P., R.S., A.T.-K., S.B., A.K., and M.K.; visualization, R.K. and A.T.-K.; supervision, R.K.; project administration, R.K.; and funding acquisition, R.K., H.P., and A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in this study are included in this article/Supplementary Materials. Further inquiries can be directed to the corresponding author.

Acknowledgments

The calculations were performed at Poznan Supercomputing and Networking Centre.

Conflicts of Interest

The authors declare no conflicts of interest.

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Catalysts 14 00667 sch001
Scheme 1. Therapeutic potential of heterocyclic pyrimidine scaffolds.
Scheme 1. Therapeutic potential of heterocyclic pyrimidine scaffolds.
Catalysts 14 00667 sch001
Catalysts 14 00667 sch002
Scheme 2. Synthesis of 1a, 1b, 2a, and 2b.
Scheme 2. Synthesis of 1a, 1b, 2a, and 2b.
Catalysts 14 00667 sch002
Catalysts 14 00667 sch003
Scheme 3. EED of prochiral pyrimidine derivatives.
Scheme 3. EED of prochiral pyrimidine derivatives.
Catalysts 14 00667 sch003
Catalysts 14 00667 g001
Figure 1. Structures of the lowest energy conformers of the alcohols 1a′, 1b′, 2a′, and 2b′, optimized at the B3LYP/6-311++G(d,p) level. The broken lines indicate possible attractive interactions. The percentage populations of a given species in conformational equilibrium are given in parentheses.
Figure 1. Structures of the lowest energy conformers of the alcohols 1a′, 1b′, 2a′, and 2b′, optimized at the B3LYP/6-311++G(d,p) level. The broken lines indicate possible attractive interactions. The percentage populations of a given species in conformational equilibrium are given in parentheses.
Catalysts 14 00667 g001
Catalysts 14 00667 g002
Figure 2. Examples of ECD spectra of alcohols 1a′ (left) and 2a′ (right) measured in cyclohexane (black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level and Boltzmann-averaged based on ΔΔG values (blue lines). The inserts show the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines).
Figure 2. Examples of ECD spectra of alcohols 1a′ (left) and 2a′ (right) measured in cyclohexane (black lines) and calculated at the TD-M06-2X/6-311++G(2d,2p) level and Boltzmann-averaged based on ΔΔG values (blue lines). The inserts show the comparison between the ECD spectra calculated for the lowest energy conformer of a given compound (dashed blue lines) and the ΔΔG-based and Boltzmann-averaged (solid blue lines).
Catalysts 14 00667 g002
Table 1. Biotransformation of 1a, 1b and 2a, 2b in the presence of S. cerevisiae at pH 6.0 depending on concentration.
Table 1. Biotransformation of 1a, 1b and 2a, 2b in the presence of S. cerevisiae at pH 6.0 depending on concentration.
Concentration
mM/mmols		1a′
Conv. a [%]
/ee [%]		1b′
Conv. a [%]
/ee [%]		2a′
Conv. a [%]
/ee [%]		2b′
Conv. a [%]
/ee [%]
2.1/7.8 × 10−37/9919/6798/8997/44
4.3/1.6 × 10−26/999/7884/9158/44
8.5/3.2 × 10−25/992/9910/997/81
17/6.4 × 10−20.3/990.3/991/996/82
a—Conversion [%] was determined by HPLC analyses using the calibration curve; Number of repetitions of experiments: 3.
Table 2. Biotransformation of 1a, 1b and 2a, 2b in the presence of S. cerevisiae depending on pH for a substrate concentration of 2.1 mM and 4.3 mM.
Table 2. Biotransformation of 1a, 1b and 2a, 2b in the presence of S. cerevisiae depending on pH for a substrate concentration of 2.1 mM and 4.3 mM.
RunConcentration
mM		pH1a′
Conv. a [%]
/ee [%]		1b′
Conv. a [%]
/ee [%]		2a′
Conv. a [%]
/ee [%]		2b′
Conv. a [%]
/ee [%]
12.15.015/9914/9044/9222/71
24.36/994/9227/9517/79
32.16.07/9919/6798/8997/44
44.37/999/7884/9158/54
52.17.022/9915/6740/9422/79
64.3NR8/9724/9517/99
72.18.016/9922/8436/9320/75
84.36/998/9439/9420/80
a—Conversion [%] was determined by HPLC analyses using the calibration curve; NR—no reaction. Number of repetitions of experiments: 3.
Table 3. Predicted biological effects of the tested compounds determined using PASS Online software.
Table 3. Predicted biological effects of the tested compounds determined using PASS Online software.
No.Antieczematic
Pa/Pi		Kidney Function Stymulant
Pa/Pi		α2β2 Nicotinic Acetylocholine Receptor Antagonist
Pa/Pi		Anti-Ischemic (Cerebral)
Pa/Pi		Nootropic
Pa/Pi		Acetylcholine Neuromuscular Blocking Agent
Pa/Pi		Testosterone 17β-Dehydrogenase (NADP+) Inhibitor
Pa/Pi		Other Activity
Pa/Pi
10.697
/0.047		0.620
/0.032		0.792
/0.011		- a0.528
/0.114		0.595
/0.025		0.786
/0.029		Pterin deaminase inhibitor
0.652/0.018
20.667
/0.057		0.699
/0.010		0.806
/0.009		- b0.518
/0.120		0.511
/0.068		0.848
/0.015		Pterin deaminase inhibitor
0.752/0.007
1a0.699
/0.046		0.502
/0.096		0.731
/0.021		- b0.553
/0.099		0.614
/0.018		0.547
/0.112		27-hydroxycholesterol 7α-monooxygenase inhibitor
0.574/0.031
1a′0.713
/0.041		0.502
/0.096		0.772
/0.014		0.857
/0.009		0.630
/0.064		- b0.591
/0.094		Proteasome ATPase inhibitor
0.597/0.035
1b0.598
/0.087		0.527
/0.080		- b- a- b0.666
/0.007		- b-
1b′0.629
/0.073		0.527
/0.080		0.540
/0.064		0.797
/0.014		0.536
/0.109		0.509
/0.069		- bProteasome ATPase inhibitor
0.538/0.054
2a- a0.654
/0.021		0.750
/0.017		0.547
/0.082		0.699
/0.041		- b0.590
/0.094		27-hydroxycholesterol 7α-monooxygenase inhibitor
0.613/0.023
2a′- b0.654
/0.020		- b0.885
/0.006		0.756
/0.026		- a0.631
/0.079		Muscle relaxant
0.588/0.008
2b- a0.671
/0.016		0.788
/0.006		- b0.612
/0.072		0.549
/0.046		- bAnticonvulsant
0.592/0.020
2b′- a0.671
/0.016		0.502
/0.076		0.844
/0.010		0.683
/0.046		- b- bMuscle relaxant
0.625/0.006
a lack of activity; b activity Pa < 0.5; Pa = predictive activity; Pi = predictive inactivity.
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MDPI and ACS Style

Kołodziejska, R.; Pawluk, H.; Tafelska-Kaczmarek, A.; Baumgart, S.; Studzińska, R.; Kosinska, A.; Kwit, M. Microbiological Bioreduction of Bulky–Bulky Pyrimidine Derivatives as an Alternative to Asymmetric Chemical Synthesis. Catalysts 2024, 14, 667. https://doi.org/10.3390/catal14100667

AMA Style

Kołodziejska R, Pawluk H, Tafelska-Kaczmarek A, Baumgart S, Studzińska R, Kosinska A, Kwit M. Microbiological Bioreduction of Bulky–Bulky Pyrimidine Derivatives as an Alternative to Asymmetric Chemical Synthesis. Catalysts. 2024; 14(10):667. https://doi.org/10.3390/catal14100667

Chicago/Turabian Style

Kołodziejska, Renata, Hanna Pawluk, Agnieszka Tafelska-Kaczmarek, Szymon Baumgart, Renata Studzińska, Agnieszka Kosinska, and Marcin Kwit. 2024. "Microbiological Bioreduction of Bulky–Bulky Pyrimidine Derivatives as an Alternative to Asymmetric Chemical Synthesis" Catalysts 14, no. 10: 667. https://doi.org/10.3390/catal14100667

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