Unveiling cytochrome P450 enzymes that catalyze steroid side-chain cleavage in bacteria

Domain: neuroscience
Published: 2025-12-10
Editorial outcome: major_revision

Abstract
Steroids are among the most valuable and widely used pharmaceuticals. The cholesterol side-chain cleavage enzyme (P450scc) is critical for steroid metabolism and hormone biosynthesis. While mammalian eukaryotic P450scc enzymes are well-characterized, bacterial counterparts remain underexplored despite their industrial promise and potential contributions to bacterial steroid catabolism. Here, we identify a series of CYP204 family P450 enzymes, widely distributed across diverse steroid-degrading bacterial species, that catalyze the side-chain cleavage of cholesterol, phytosterol, and cholestenone to produce pregnenolone and progesterone. Unlike mammalian enzymes, which exhibit strict cholesterol specificity, bacterial P450scc enzymes display relaxed substrate specificity, preferentially converting cholestenone to progesterone—a key precursor in steroid drug semi-synthesis. Structural and mechanistical analyses demonstrate that CYP204 enzymes employ a flexible, dual-regioselective C–H activation mechanism distinct from the sequential hydroxylation of mammalian P450scc enzymes. Iterative saturation mutagenesis identified critical residues for side-chain cleavage, improving catalytic efficiency up to 6.5-fold, and computational analyses clarified sequence–function relationships. This finding of bacterial P450scc enzymes not only underscores their potential function in bacterial steroid catabolism but also lays a foundation for promising biocatalytic strategies for pregnenolone and progesterone synthesis.

Introduction
Steroid hormones, such as pregnenolone (PREG) and progesterone (PROG), are indispensable in medicine due to their anti-inflammatory, anti-allergic, and endocrine effects, serving as critical precursors for glucocorticoids, mineralocorticoids, and sex hormones used in clinical therapies1–6. In mammals, the precursor to all steroid hormones, PREG is synthesized from cholesterol (CHL) via a well-characterized three-step reaction catalyzed by the cytochrome P450 enzyme mCYP11A1, also known as the side-chain cleavage enzyme (P450scc)7–9. This process involves sequential hydroxylation of cholesterol to 22R-hydroxycholesterol (22R-HC), then to 20R, 22R-dihydroxycholesterol (20R,22R-DHC), followed by C20–C22 bond cleavage to yield PREG (Fig. 1a)10–12, which is subsequently converted to PROG by 3β-hydroxysteroid dehydrogenase/isomerase13. Notably, mCYP11A1 exhibits narrow substrate specificity and shows negligible activity toward converting cholestenone (CHO)9. Beyond mammals, plant P450scc enzymes belong to the CYP87A family, which is involved in cardenolide biosynthesis, and have recently been identified14–16 (Fig. 1b). In microorganisms, steroids are ubiquitous in natural environments and serve as vital growth substrates, making microbial steroid catabolism pivotal for both pathogenicity and biotechnological applications17–19. However, the equivalent P450scc enzymes in prokaryotes remain unexplored.

In addition to their central role in hormone biosynthesis, PREG and PROG, particularly the latter, also serve as key precursors in the industrial synthesis of a significant number of steroid drugs (over 200)1,4. Traditional production of PROG relies on a 7-steps semisynthetic process from diosgenin, known as Marker degradation, but the low yield and environmental concerns have spurred interest in biotransformation alternatives20,21. Recent approaches combining bacterial conversion of phytosterols with semisynthetic methods show promise for producing PROG, though they still require multiple chemical steps—such as converting intermediates like 21-hydroxy-20-methyl-pregn-4-ene-3-one (4-HBC) into PROG—or even more complex reactions to generate PREG4. In comparison, the P450scc pathway provides an environmentally sustainable and direct synthesis route for both target compounds. However, eukaryotic P450scc enzymes are less suitable for industrial biocatalysis due to membrane association, poor heterologous expression, and restricted engineering potential from high sequence conservation22–27. In contrast, prokaryotic P450 offer superior biotechnological applicability through their soluble nature and compatibility with industrial bacterial hosts.

In this study, we report the discovery and characterization of a series of bacterial P450scc enzymes through phylogeny-guided genome mining from steroid-degrading microorganisms, which exhibits a substrate preference for CHO as well as CHL and phytosterols (Fig. 1c). Unlike the strict, sequential C22 to C20 hydroxylation of mammalian CYP11A1, these bacterial enzymes employ a flexible, simultaneous C20 and C22 hydroxylation mechanism, enabled by an expanded substrate cavity that accommodates diverse sterol conformations. Structure-guided engineering of CYP204A5 further significantly enhances the enzyme’s catalytic efficiency. These findings provide valuable insights into bacterial steroid catabolism and highlight their potential for more sustainable and scalable steroid drug production.

Results
Discovery of bacteria-derived P450scc
The bacterial steroid degradation pathway typically involves multiple oxidative hydroxylation steps, largely catalyzed by various P450 enzymes that participate in steroid catabolic pathways and play an important role in steroid degradation17,28. Leveraging the soluble expression and industrial application potential of bacterial P450, we developed a strategy for mining P450 hydroxylases from steroid-degrading bacterial genomes using a phylogeny-guided genome mining approach (Fig. 2a). While we previously identified a fungal C14α-hydroxylase (CYP14A) that efficiently catalyzes C14-hydroxylation29, its membrane-bound nature limits its broad application. To address this limitation, we initially aimed to identify soluble bacterial P450 capable of C14-hydroxylation, expanding the potential for industrial biocatalysis. Given that lanosterol demethylase initiates its catalytic cycle by hydroxylating the C14-methyl group30, we hypothesize that phylogenetically related bacterial P450s may adopt similar substrate-binding conformations and are capable of hydroxylating the C14 of sterols. To test this, we compiled a local database comprising the genomes of 265 putative steroid-degrading bacteria18. Using the C14-demethylase of Mycobacterium tuberculosis CYP51 (MTCYP51) as a probe30, we performed BLAST searches and constructed a phylogenetic tree with 1,000 homologous sequences (Supplementary Fig. 1, Supplementary Data 1). Eight candidates (P450-A1 to P450-A8) from clades near CYP51, varying in homology and origin, were selected for further study (Supplementary Fig. 1, Supplementary Table 1).

The corresponding genes were synthesized with codon optimization and expressed in Escherichia coli BL21(DE3) (Supplementary Fig. 2, Supplementary Data 2). To assess their activity, we tested five steroidal substrates (Fig. 2b)—PROG, CHO, 4-HBC, 4-androstenedione (4-AD), and lanosterol—using ferredoxin/ferredoxin reductase pairs (SpFdx/SpFdR from spinach31 and SeFdx1499/FdR0978 from Synechococcus elongatus PCC 794232, Supplementary Table 2) to support catalysis. Among the screened candidates, only P450-A8 exhibited clear activity in the presence of the Synechococcus redox system when CHO was used as the substrate (Supplementary Fig. 3). LC-MS analysis revealed a major product with a mass shift of −70 Da, along with three minor products, presumed to be mono- and di-hydroxylated derivatives, showing mass shifts of +16 Da and +32 Da, respectively (Supplementary Fig. 3). By scaling up the reaction, we isolated the major product, which was identified as PROG through NMR analysis. These findings demonstrate that P450-A8 unexpectedly catalyzes a sterol side-chain cleavage reaction.

We next evaluated whether P450-A8 could cleave the side chains of other sterols, including CHL, desmosterol, campesterol, and β-sitosterol, the latter two being major components of phytosterols (Fig. 2b). LC-MS analysis confirmed that P450-A8 catalyzes the side-chain cleavage of CHL, desmosterol, and β-sitosterol, producing PREG as the primary product (Supplementary Fig. 4), albeit with lower efficiency—particularly for desmosterol and β-sitosterol—compared to CHO. These results establish that P450-A8 has a much broader substrate specificity than the eukaryotic P450scc, mCYP11A19.

A comprehensive search of the NCBI genome database was conducted using P450-A8 as the query sequence to evaluate the distribution of this enzyme across bacterial species. This analysis identified 192 protein homologs exhibiting greater than 40% sequence identity with P450-A8. These sequences predominantly originate from two prokaryotic phyla: Pseudomonadota and Bacillota (Fig. 2c, Supplementary Data 1). Based on phylogenetic analysis, we selected nine homologous sequences from different clades, which were subsequently codon-optimized and synthesized for further study (Fig. 2c, Supplementary Table 3, Supplementary Data 2). According to cytochrome P450 nomenclature33,34, P450-A8 and the nine additional identified homologs were classified as members of the CYP204 family. Consequently, P450-A8 was renamed CYP204A2, while the remaining nine homologous proteins were designated CYP204A3, CYP204A4, CYP204A5, CYP204A6, CYP204B2, CYP204C1, CYP204D1, CYP204E1, and CYP204F1.

These nine proteins, except for CYP204C1 and CYP204D1, were successfully expressed in E. coli in soluble form (Supplementary Fig. 5). Activity assays of the soluble proteins were performed using CHO as the substrate. Notably, CYP204A3, CYP204A4, CYP204A5, CYP204A6, and CYP204B2 demonstrated the ability to catalyze their side-chain cleavage, producing PROG (Fig. 3a). Phylogenetic analysis demonstrates that bacterial P450scc, mammalian P450scc, and plant P450scc enzymes cluster into distinct evolutionary clades, indicating their independent evolutionary origins (Fig. 3b, Supplementary Table 4). This phylogenetic divergence underscores the convergent evolution of steroid side-chain cleavage functionality across diverse biological kingdoms. Given the lack of detailed investigation into the catalytic mechanisms of these bacterially derived P450scc enzymes, further mechanistic studies are necessary to fully elucidate their functional properties.

Determination of the hydroxylated intermediates produced by bacterial-derived P450scc
The bacterial P450scc enzymes exhibited variability in both protein expression levels and catalytic efficiency (Supplementary Figs. 5 and 6). Among them, CYP204A5 demonstrated superior catalytic efficiency (1.1-fold better than CYP204A2, 5.3-fold better than CYP204A3, 10.2-fold better than CYP204A4, 13.2-fold better than CYP204A6 and CYP204B2, Supplementary Fig. 6) and was selected as the primary candidate for further biochemical characterization and mechanistic investigation. Additionally, it showed higher catalytic activity with CHO than with CHL; Thus, CHO was chosen as the model substrate for mechanistic studies of CYP204A5.

LC-MS analysis of the CYP204A5-catalyzed reaction identified two monohydroxylated intermediates and one dihydroxylated intermediate, along with the primary product PROG, consistent with the observations for P450-A8 (CYP204A2) (Fig. 3a). According to the mechanism of mammalian P450scc, these hydroxylations are speculated to occur at C20 and C22. To characterize these intermediates, we chemically synthesized four monohydroxylated cholestenone standards—20S-hydroxycholestenone (20S-HCO), 20R-hydroxycholestenone (20R-HCO), 22S-hydroxycholestenone (22S-HCO), and 22R-hydroxycholestenone (22R-HCO) (Fig. 4a). Their structures and absolute configurations were confirmed by NMR or X-ray diffraction (Supplementary data 3). Comparing their retention times on LC-MS revealed that the two monohydroxylated compounds are indeed 20S-HCO and 22R-HCO, respectively (Supplementary Fig. 7).

To further determine whether they are true intermediates, 20S-HCO and 22R-HCO, along with the other two monohydroxylated compounds, 20R-HCO and 22S-HCO, were tested for cleavage activity by CYP204A5. The results showed that only 20S-HCO and 22R-HCO were further converted into PROG (Fig. 4b), whereas 20R-HCO and 22S-HCO were unreactive, supporting 20S-HCO and 22R-HCO as intermediates in the CYP204A5 catalytic process. Interestingly, the catalyzed reactions with CHO, 20S-HCO and 22R-HCO as substrates consistently produced a dihydroxy intermediate with the same retention time (Fig. 4b). Subsequent scale-up reactions, NMR characterization, and comparison with the C3-oxidized product of commercially available 20R, 22R-dihydroxycholesterol (20R,22R-DHC) generated by cholesterol oxidase (ChoM) identified the dihydroxy intermediate as 20R, 22R-dihydroxycholestenone (20R, 22R-DHCO) (Supplementary Fig. 7), in which the configuration of the hydroxyl groups at C20 and C22 are identical to those in 20S-HCO and 22R-HCO.

We further investigated whether CYP204A5 exhibits the same specificity toward cholesterol-derived substrates by testing chemically synthesized 20S-hydroxycholesterol (20S-HC) and 20R-hydroxycholesterol (20R-HC) (Fig. 4c), along with commercially sourced 22R-HC, 22S-hydroxycholesterol (22S-HC), and 20R, 22R-dihydroxycholesterol (20R,22R-DHC), for side-chain cleavage activity. Biochemical assays revealed that CYP204A5 can effectively converts 22R-HC, 20R,22R-DHC and 20S-HC into PREG, but exhibits no activity toward 20R-HC. Although 22S-HC can be converted into several hydroxylated products, their retention time didn’t align with 20R,22R-DHC, suggesting a nonspecific hydroxylation (Fig. 4d). These observations are consistent with the results from cholestenone-based substrate assays, establishing that bacterial and mammalian P450scc enzymes share a dihydroxylation-driven C–C bond cleavage mechanism but differ in their sequential hydroxylation steps and regioselectivity. Unlike mammalian P450scc (CYP11A1), which strictly follows a sequential C22→C20 hydroxylation pattern, bacterial P450scc enzymes (CYP204 family) exhibit a more flexible two-step hydroxylation mechanism, where the initial hydroxylation can occur at either the C-20 or C-22 position (Fig. 1c). This bidirectional catalytic activity could potentially enhance C–C bond cleavage efficiency compared to mCYP11A1. Notably, both hydroxylation steps, along with the subsequent C-C bond cleavage through a dihydroxylated intermediate, are likely mediated by Compound I (Cpd I) as the reactive oxygen species, analogous to the mechanism observed in mammalian P450scc9–12.

Structural characterization of P450scc
To elucidate the catalytic mechanism of bacterial P450scc, we first pursued structural characterization. Although crystallization of CYP204A5 was unsuccessful, we successfully determined the crystal structure of its homolog CYP204A3 at 2.1 Å resolution (PDB: 9WAT; Fig. 5a, Supplementary Table 5, Supplementary Fig. 8). Sequence alignment revealed 70.3% identity between CYP204A3 and CYP204A5. This high conservation corresponds to functional similarity: while exhibiting reduced activity, CYP204A3 processes identical substrates and generates the same intermediates as CYP204A5 (Fig. 3a and Supplementary Fig. 9), strongly suggesting a shared catalytic mechanism.

The CYP204A3 structure adopts the characteristic trigonal-prism fold observed in other bacterial P450s (e.g., P450cam35, NasF505336, and PtmB37). Notably, AlphaFold3’s prediction for CYP204A3 showed remarkable accuracy (RMSD=0.488 Å) relative to our experimental structure (Supplementary Fig. 10), validating its application for modeling CYP204 family proteins. Notably, the F-G loop showed significant structural differences, likely attributable to its inherent flexibility. Despite extensive crystallization attempts, we were unable to obtain substrate-bound structures of CYP204A3. Therefore, we docked CHO and CHL into its active site, respectively (Fig. 5b and Supplementary Fig. 11), revealing different substrate-binding pocket architecture and interaction patterns compared to the mammalian CYP11A1-cholesterol complex (PDB: 3N9Y)9. Leveraging structural insights from P450scc and computational docking, we conducted targeted alanine scanning mutagenesis (Supplementary Fig. 12), where observed reductions in activity supported the predicted binding mode.

Comparative molecular dynamics (MD) simulations of the CYP204A3 and CYP11A1 complexes revealed greater fluctuations in the RMSD of both the ligand and the active sites in the CYP204A3 complex (Supplementary Fig. 13), indicating greater active site plasticity. This enhanced conformational flexibility may enable CYP2043 to have a broader range of substrates than mammalian P450sccs. MD simulations of the docked model of CYP204A3-CHO to showed that C20 and C22 remained consistently positioned near the iron-oxo center of compound I (Cpd I) throughout the simulation (Fig. 5c and Supplementary Fig. 14). The distances between the Cpd I and C20/C22 were measured at 4.04 ± 0.41 Å and 4.10 ± 0.45 Å, respectively, favoring hydroxylation and subsequent side-chain cleavage. Similar substrate positioning favoring hydroxylation was observed in the docked model of CHO with a CYP204A5 AlphaFold3-predicted structure (Supplementary Fig. 15). This spatial arrangement enables simultaneous hydroxylation of C20 and C22, distinguishing bacterial P450scc from the sequential hydroxylation mechanism of mammalian P450scc.

Analysis of the docked model reveal potential ligand-receptor interactions mediated by several key amino acid residues within 5 Å—specifically S80, M81, F84, Y93, S94, L253, W256, A257, and T261—which play a pivotal role in positioning the side chain of CHO above the heme group. (Fig. 5b). Interestingly, most residues within the 5 Å range of the pocket, including those listed above, are conserved in other bacterial P450scc enzymes, such as CYP204A5 (Fig. 5b and 5d, and Supplementary Fig. 16), suggesting that they are critical for substrate binding and maintaining catalytic conformation. To validate this, site-directed mutagenesis was performed on CYP204A5. As anticipated, most mutants exhibited significantly reduced or abolished production of PROG (Supplementary Fig. 17).

Identification of key residues for enhancing bacterial P450scc activity
With the aim of further investigating and improving the catalytic performance of bacterial P450scc enzymes, we conducted a comparative analysis of amino acid differences within the catalytic pocket across various bacterial P450scc enzymes. The analysis revealed that residues 322–325 in CYP204A5 significantly differ from the corresponding region (325Y–328S) in the less active CYP204A3 (Fig. 5b and 5d). Additionally, this motif is relatively conserved in other less active bacterial P450scc enzymes. Given the variation in enzyme activity over evolution, we speculated that these sites might be critical for higher P450scc activity.

This hypothesis was tested through the introduction of the CYP204A5 sequence (322F-323T-324M-325I) into the less active CYP204A3 background (325Y-326M-327L-328S). However, this substitution significantly reduced the catalytic activity of CYP204A3 (Fig. 5e). Further replacing the single residue of CYP204A3 and evaluating their effects on the catalytic activity revealed that only the Y325F mutation slightly enhanced catalytic activity, while other mutants significantly reduced it. Additionally, combinatorial two- and three-site swaps generally resulted in lower catalytic activity, except for the Y325F-L327M-S328I mutant, which retained activity comparable to the wild type (Fig. 5e). These results demonstrate that these regions are pivotal, and subtle changes significantly affect catalytic activity.

To further enhance the catalytic efficiency of CYP204A5, we implemented comprehensive site-saturation mutagenesis (SSM) targeting 22 residues located within the 5 Å substrate-binding pocket: S77, M78, F81, Y90, S91, S177, M180, L250, W253, A254, E257, T258, V320, A321, F322, T323, M324, I325, R326, A429, G430 and T431. A total of 418 single-site variants were generated and screened for their side-chain cleavage activity. Eight mutants (S91M, S91I, T323V, T323L, T323C, T323I, M324L, and M324I) exhibited significantly enhanced catalytic activity, producing PROG at levels approximately 3.5 to 6.5-fold higher than the wild type (Fig. 5f). These beneficial mutations were primarily located at three sites: S91, T323, and M324. Interestingly, T323 and M324 are part of the identified motif, reinforcing their crucial role in catalytic performance. However, combinatorial double mutants did not further enhance catalytic activity (Fig. 5f), suggesting that the identified single-site mutations already optimize the local structural environment for enhanced activity.

For the purpose of elucidating the molecular basis underlying the enhanced catalytic activity, we performed MD simulations on three representative mutants: CYP204A5-S91M, CYP204A5-T323L, and CYP204A5-M324I. For the S91M mutant, MD simulations indicated that the bulkier side chain introduced steric hindrance, which restricted the bending of the CHO-side chain (Fig. 6a). This restriction kept the C20 and C22 positions closer to Cpd I (Fig. 6b and Supplementary Fig. 18), thereby promoting more efficient catalytic conversion. Molecular dynamics trajectories indicated that, relative to the wild type, the T323L and M324I mutations also resulted in the C20 and C22 atoms of CHO adopting a closer proximity to the iron-oxo ligand of Cpd I (Fig. 6c–f; Supplementary Fig. 18). This altered positioning may contribute to more efficient hydroxylation at C20 and C22, suggesting a plausible mechanistic basis for the observed changes in catalytic activity. Structural analysis further showed that the T323L and M324I mutations introduced branched aliphatic side chains, enhancing hydrophobic interactions with the CHO. These interactions stabilized the substrate in a catalytically favorable conformation, thereby increasing overall activity.

In contrast to the wild-type enzyme, where all monohydroxylated intermediates (20S-HCO and 22R-HCO) and the dihydroxylated intermediate (20R, 22R-DHCO) were detected, the mutant strains exhibited significant conversion of hydroxylated intermediates (Supplementary Fig. 19), consistent with the improved catalytic efficiency observed for CHO cleavage. MD simulations of the S91M and T323I mutants revealed that the monohydroxylated substrates 20S-HCO and 22R-HCO remained in closer proximity to Cpd I throughout the simulation compared to the wild type (Supplementary Figs. 20 and 21). This finding is consistent with the observed enhancement in enzymatic activity. This suggests that the primary effect of the mutation on activity is related to altering the distance between the side chain and Cpd I. Based on the MD simulations and mutagenesis results described above, we speculate that the relatively lower activity of CYP204 enzymes towards cholesterol and phytosterols, compared to cholestenone, may be attributed to hydrogen-bonding interactions influenced by the C3-hydroxyl group, as well as steric effects caused by differences in their side chains. These factors likely hinder the optimal positioning of C20 and C22 near the Cpd I for efficient catalysis. Overall, these findings demonstrate that modifying steric hindrance and enhancing hydrophobic interactions in the catalytic pocket of bacterial P450scc enzymes effectively improves side-chain cleavage activity. This structure-guided engineering approach offers a promising strategy for optimizing bacterial P450scc enzymes for biocatalytic applications.

Methods
Methods for mining bacterial P450 for steroid modification
The selection of 265 microorganisms for this investigation was systematically conducted based on comprehensive literature reviews documenting steroid-degrading microorganisms18. Their genomic sequences were retrieved from the NCBI RefSeq database38. A robust local genomic database comprising 265 microorganisms was constructed utilizing BioEdit2 software39. To identify cytochrome P450 sequences, we conducted a BLAST search against the established local database, employing a known bacterial P450 sequence (NCBI accession number: WP_003898577.1 from Mycobacterium tuberculosis EAS054) as the reference30. This analysis yielded 1000 candidate sequences, which were subsequently subjected to multiple sequence alignment using MUSCLE with default parameters. For phylogenetic reconstruction, the identified P450 protein sequences were analyzed alongside known CYP51 sequences through the Maximum Likelihood (ML) method implemented in MEGA1140. The resulting phylogenetic relationships were visualized and annotated using the Interactive Tree of Life (iTOL) platform41, as presented in Supplementary Fig. 1 and Supplementary Data 4.

Molecular cloning and mutagenesis
The molecular cloning was performed using E. coli DH5α cells according to standard protocols. The genes encoding P450s (CYP51-A1~A8, CYP204A3-A6, CYP204B2, CYP204C1, CYP204D1, CYP204E1 and CYP204F1) in the study were synthesized and cloned into the pET28a(+) vector with a N-terminal His6-tag by ATANTARES (Suzhou, China) (Supplementary Data 2). Mutational gene construction was achieved by site-directed mutagenesis via rolling circle amplification. Primer synthesis and DNA sequencing were performed at Tsingke Biotechnology Co., Ltd. All the primers used in this study are all listed in Supplementary Data 5. Strains and plasmids used in this study are listed in Supplementary Table 6.

Protein expression and purification
For the expression of P450s and their mutants, the plasmid carrying the gene encoding P450 was transferred into E. coli BL21(DE3), the resulting strains were grown overnight at 37°C with shaking at 220 rpm, which were used as seed cultures to inoculate LB medium at a 1:100 ratio. Cells were grown at 37°C until the optical density at 600 nm (OD600) reached 0.8−1.0 and then induced by the addition of isopropyl-β-D-1-thiogalactopyranoside (IPTG) (0.1 mM), δ-amino-levulinic acid (0.4 mM), and ferrous sulfate (0.2 mM). The induced cultures were further incubated at 18°C for 20 h with shaking at 200 rpm. The cells were harvested by centrifugation and resuspended in Buffer A (25 mM Hepes, 300 mM NaCl, 10% glycerol, pH 7.5, the pH can be adjusted according to the characteristics of different proteins). The cells were broken by ultra-sonication, and the insoluble debris was removed by centrifugation at 13,500 xg for 1 h at 4°C. The protein supernatant was then incubated with 1 mL Ni-NTA sepharose for 1.5 hours with slow, constant rotation at 4°C. Subsequently the protein resin mixtures were loaded into a gravity flow column, and proteins were eluted with increasing concentrations of imidazole (25 mM, 50 mM, 100 mM, 300 mM) in Buffer A. Purified proteins were collected according to SDS-PAGE and then loaded into PD-10 desalting columns to desalt using Buffer B (50 mM Hepes, 100 mM NaCl, 10% glycerol, pH 7.5, the pH can be adjusted according to the characteristics of different proteins). The final purified proteins were concentrated by centrifugation using an Amicon Ultra-4 (GE Healthcare) and stored at −80°C for later use.

The expression and purification of redox partner SpFdx/SpFdR, SeFdx1499/ SeFdR0978, and glucose dehydrogenase (GDH) were conducted as described above, except that only IPTG was added when OD600 reached 0.8−1.0.

Biochemical assay
The enzymatic reactions were performed in a 100 μL reaction mixture containing 50 mM TES buffer (N-(Tris(hydroxymethyl)methyl)−2-aminoethanesulfonic acid sodium salt, pH 7.5), 10 µM P450scc, 40 µM Fdx, 10 µM FdR, 500 µM substrate, and an NADPH regeneration system comprised of 10 mM glucose, 1 mM GDH, and 1 mM NADPH. Reactions with boiled enzymes were performed as controls. Then all reactions were incubated at 18°C for 12 h and subsequently quenched with the addition of equal methanol (100 μL). Protein was removed by centrifugation at 13,500 xg for 10 min, the 5 μL supernatants were subjected to UPLC (LC-30A, Shimadzu Corporation, Kyoto, Japan) or UPLC-MS (LCMS-2020, Shimadzu Corporation, Kyoto, Japan) system using a Shimadzu Shim-pack GIST C18 column (2 μm, 2.1 × 100 mm) at a flow rate of 0.2 mL∙min–1 using a mobile phase of (A) H2O containing 0.1% fomic acid and (B) CH3CN. The elution procedure was a 25 min gradient program as follows: t=0 min, 30% B; t=8 min, 100% B; t=20 min, 100% B; t=21 min, 30% B; t=25 min, 30% B.

Mass spectrometer parameters were as follows: positive/negative ionization, SIM event time 0.3 s, Detector voltage 1.5 kV, Interface voltage –4.5 kV, DL voltage 0 V, Interface temperature 350°C, DL temperature 250°C, Nebulizing gas flow 1.5 L/min, Heat unit temperature 200°C, Drying gas flow rate 15 L/min, Ionization mode DUIS (ESI and APCI). The m/z range was 100–850.

Protein structure prediction and molecular docking
The predicted structure of CYP204 enzymes was constructed using AlphaFold342, the CYP204A3 protein sequence (UniProt Accession ID: A0A258V3A8) was submitted to the AlphaFold Server on July 27, 2024 (https://alphafoldserver.com/). The top-ranked model was used for comparison with the experimentally determined crystal structure. The pLDDT scores were color-coded and overlaid from two viewing angles, and the predicted aligned error (PAE) plot was also generated and included, which are provided in Supplementary Fig. 10. The CYP204A5 (NCBI Accession ID: WP_058802412.1) protein sequence was submitted to the AlphaFold Server on January 8, 2025 (https://alphafoldserver.com/). The top-ranked model was used for molecular docking. The visualizations of pLDDT scores and the PAE plot were provided in Supplementary Fig. 15.

Molecular docking studies were conducted using the SwissDock web-based platform43. The center position and dimensions of the box have been adjusted to position it correctly above the P450 heme. All other settings use default values. The selected orientations did not always correspond to the lowest-energy states, as we focused on poses with the steroid side chains positioned close to the ferric heme center. The parameters and details are listed in the Supplementary Table 7, and Protein-Ligand docking structures generated in this study are presented in Supplementary Data 6.

MD Simulations
The optimal binding pose of the substrate in the P450 active site was selected from molecular docking results. The Compound I (Cpd I) fragment, consisting of the heme cofactor (Fe–protoporphyrin IX) with the oxo radical and its proximal cysteine ligand, together with the substrate, was subjected to geometry optimization using Gaussian1644 at the B3LYP functional level with the LANL2DZ pseudopotential basis set for FE and 6-31G(d) basis set for C, H, O, N and S atoms. To account for solvent effects, the IEFPCM implicit solvation model was applied throughout the optimization45,46. The spin multiplicity was set to 4, in agreement with previously published studies47. The optimized wavefunction files were processed with Multiwfn48 to derive RESP charges. Bonded and non-bonded parameters for Cpd I and the substrate were generated using Sobtop49 based on the RESP charges.

Molecular dynamics (MD) simulations were performed using GROMACS 2022.450. The AMBER14SB force field51 was used for the protein, GAFF52 for the substrate, and TIP3P53 for water molecules. Four sodium ions were added to neutralize the total charge of the system. The protein complex was solvated in a rectangular box, with periodic boundary conditions applied in all directions. The system setup, including simulation box dimensions, total number of atoms, number of water molecules, and salt concentration, was presented in Supplementary Data 7. The force field parameters contain atom types, charges, and Lennard-Jones parameters (σ, ε) of CpdI and substrate are provided in Supplementary Data 8.

The optimized wavefunction files were processed with Multiwfn48 to derive RESP charges. Specifically, a two-step charge fitting is performed. In the first stage, a weak hyperbolic restraint (a = 0.0005) is imposed on the atomic charges to allow the greatest degree of freedom for the charges on polar atoms to fit the ESP. In the second stage, a restraint coefficient of a = 0.0010 was applied. Based on the first-step results, the fitting is restricted to the charges of sp3 hybridized carbons, methylene carbons (excluding the terminal capping groups), and their attached hydrogen atoms, while enforcing charge equivalency for chemically equivalent hydrogens. The detailed input and output files for the RESP fitting procedure are provided in a figshare repository (see Data Availability).

Protonation states of titratable residues were assigned at pH 7.4 using the default settings of pdb2gmx. Energy minimization was carried out in two steps: 10,000 steps of steepest descent followed by 5,000 steps of conjugate gradient minimization. The minimized structure was equilibrated with 500 ps of NVT and 500 ps of NPT simulations, during which positional restraints were applied. Three independent production MD runs were performed for 100 ns each with a 2 fs integration step. The temperature was maintained at 291.15 K using the V-rescale54 thermostat, and the pressure at 1 bar using the Parrinello–Rahman55 barostat. Long-range electrostatic interactions were treated with the particle mesh Ewald (PME)56 method, with a real-space cutoff of 1.2 nm. The van der Waals (vdW) cutoff was also set to 1.2 nm. Hydrogen bonds were constrained with the LINCS algorithm. The input files for energy minimization and equilibration and the files for production runs are provided in provided in a figshare repository (see Data Availability).

Crystallization of protein and compound
CYP204A3 crystals were grown at 20°C using the sitting drop vapor diffusion method, and crystals of the compounds were produced at 4°C using the slow volatilization method (details in Supplementary Methods).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study by Tian et al. present an impressive effort in identifying biotechnologically bacterial cytochrome P450 enzymes that catalyse steroid side-chain cleavage by employing a plethora of biochemical, bioinformatic & biophysical methods. However, the manuscript lacks some important information, and has presentational issues and draws conclusions from incomplete analyses. Upon revision, the points given below should be addressed. Summary: The study aims at identifying bacterial C14-alpha hydroxylase Cytochrome P450 (CYP) enzymes for biotechnological applications but unexpectedly revealed a bacterial enzyme catalysing steroid side-chain cleavage. These bacterial CYPs bear the advantage of solubility when compared to their membrane-bound mammalian counterparts. To achieve this, the authors applied an impressive, holistic approach encompassing identification of candidate enzymes by bioinformatic- & biochemical screening, investigation of substrate-specificity & enzymatic mechanism by structural biology, molecular dynamics simulations and biochemical assays & enhancement of enzymatic activity through mutagenesis experiments. The remarkably wide scope of methods & characterization approaches presented in this manuscript necessitates a wide range of areas of expertise for an appropriate review. This review will, thus, mostly focus on the computational- (docking & MD simulation) and structural sections of the manuscript. Initially, candidate enzymes were identified from bioinformatic screening and were subjected to experimental screening on different Steroid substrates. Only one catalytically active enzyme: P450-A8 was found to transform cholestenone to Progesterone while also working on a wider substrate range (different from eukaryotic equivalents). This sequence was used to identify 8 further homologous bacterial enzymes for further testing which revealed CYP204A5 to have the highest activity. To solve the enzymatic mechanism, mono- & dihydroxylated intermediates were identified through comparative LC-MS & chemical synthesis indicating a 2-step hydroxylation mechanism. This was further supported by structural studies on CYP204A3 (A5 was not crystallizable) & associated docking and molecular dynamics simulations. From the latter and site-saturation mutagenesis experiments, residues facilitating substrate positioning & enzymatic activity were identified yielding novel mutants with increased activity. Further MD studies on 3 representative mutants demonstrated that these mutations place the C20 & C22 atoms closer to the catalytically active Compound 1 species. Major concerns: -It is unclear if CYP204A3, whose structure was solved crystallographically, differs from the most efficient candidate identified in this study, CYP204A5, in substrate specificity. The authors only remark on its lower activity, but a thorough comparison of enzyme specificity would be necessary to assess how good of a template this structure is for CYP204A5. Likewise, some measures like sequence identity and sequence alignment would should be provided to assist assessment of the authors’ choice to crystallize this enzyme. -From their comparison of the crystallographic structure of CYP204A3 to mammalian CYP11A1 the authors conclude that the former is less specific due to a looser fit of the docked substrate. This comparison seems very challenging as they compare between a crystallographic holo- and an apo-structure, thus, potentially missing out on induced-fit effects taking place upon substrate binding. It might be necessary to compare to the converged MD-structure of the docked protein-substrate complex, compare quantities like ligand RMSDs or even try to co-crystallize a steroid bound complex to substantiate this point. -The manuscript lacks any thorough validation of the docking poses of cholestenone to CYP204A3 : what were the docking scores? How do the poses e.g. compare to alignments with CYP11A1:Cholesterole? Likewise, it would be good to give a proper interaction plot, not only the Surface-fit presented in Fig S8 and to provide the docked structure as supplementary material. -Likewise, any critical validation of the performed MD simulation is missing. Without any measure of convergence besides the C20, C22-Fe distances, it is impossible to assess if the simulation-lengths & choice to only simulate one replica per system are sufficient. This is very important as some of these distance-plots e.g. in Fig. 6 B & D as well as S11 B & S12 B display increases/instabilities just before the simulations were terminated indicating some structural rearrangements. Such measures could include e.g. protein & ligand RMSDs over all trajectories. -In the sentence ‘Closer examination of the substrate-binding pocket further identified several key amino acid residues […]’ (p. 9) it is unclear how this examination was performed. Was this based on quantitative measurements such as contact analyses or on visual inspection? This information is necessary to assess the validity of all subsequent experiments. -The ‘Methods’ & ‘Supplementary methods’ sections lack essential information on the simulation-based assays: -Which parameters were used in SwissDock ? -Which implicit solvent model was used in DFT? -Which spin-state was chosen to represent CPD1? How high was the associated spin-contamination? High spin-contamination can over-delocalize charges over the molecule, thus, affecting RESP calculations. -How were the RESP charges computed in Multiwfn? Which fitting algorithm etc. was utilized? How were the charges made compatible with AMBER? —> why did the authors choose to use Multiwfn for RESP charge computations instead of the native AMBER program Antechamber which can also compute RESP charges from ESP calculations with Gaussian. Likewise, why was the ‘sobtob’ tool utilized instead of Antechamber --> How was the system protonated and at what pH? -> References for the AMBER14SB-PARMBSC1 and GAFF force fields and the TIP3P water model should be given. -How many counter-ions were added to the simulation systems, i.e. at which concentration? -No information on electrostatics, treatment of periodic boundary condition etc. are given for the energy minimizations -The ‘Methods’ section of the manuscript essentially only describes experimental methods. Despite majorly contributing to the manuscript, almost all bioinformatics- and simulation-related methods have to be searched for in the Supplementary materials. Especially in the light of our concerns about critical information lacking there, it is unclear to us why the methodology was fragmented like this. Minor concerns: -Language: some incomprehensible sentences, some repetitions/tautologies (e.g. ‘[…] plant CYP87A family P450scc enzymes […] have recently been identified in several plants’); many titles of Supplementary materials erroneous -The authors’ initial screening approach seems to undervalue the importance of local vs. global similarity. The way we understand their initial bioinformatic approach, it was based essentially on just selecting one template enzyme and a global alignment to identify initial hits. Since CYPs are very well known & relatively conserved, it might have been more successful if the authors would have instead focused on local alignments in the known substrate-coordinating regions of steroid-metabolizing CYPs. This point should be elaborated on in the introduction and/or discussion -The manuscript's clarity would greatly profit from using proper abbreviations instead of numbers to refer to different compounds e.g. PROG/PREG/…, particularly as this gets very confusing when reaction steps are discussed. -The text in Fig. 2 is too small and not readable -Fig. 4 E: the presented mechanism does not seem to make sense: where does the third oxygen in ‘Cycle 3’ come from? Is it derived from a water molecule as in Cycle 3, CPD1 still carries an oxygen? -Actual means & standard deviations for the distances measured over the MD simulation should be reported instead of discussing ‘approximate values’ in paragraph 2 on p. 9 -The title of table S2 is entirely unclear to us (‘Information of redox used in this study’) -In the Supplementary methods, the authors refer to AF3 models as homology models which is wrong. -Why did the authors choose the AMBER14SB-PARMBSC1 force field?", "reviewer_2": "The manuscript by Tian et al. describes the discovery of bacterial P450scc enzymes in steroid-degrading microorganisms. The authors reveal that these bacterial enzymes proceed via a different mechanism compared to the mechanism of the comparable enzymes found in mammals and plants. The authors also perform engineering work based on one obtained bacterial P450scc enzyme structure. Overall this is an excellent paper, providing new insights into bacterial metabolism and also highighting a new biocatalytic tool for production of progesterone-derived compounds. Below are minor commenst: 1) The stereochemistry of the compounds in Fig. 1 is neither complete (1, 2 ,3, and 6), nor correct (Fig. 1C, intermediate 6). 2) The intermediates in Fig. 1. are numbered wrongly. 3) Please make Fig. 3B more reader friendly since some annotations are upside-down. 4) Please explain why the peaks for 5 in Fig. 4D are not consistent by retention time. 5) Figs. 4B and 4D: By comparing the retention time, pregnenolone (1) seems to be eluted earlier than progesterone (2), which is supposed not to be such result using C18 column. 6) Lines 110, 112, 167 and 185: supporting information is not specified. 7) CYP204 catalyzes cholesterol and other phytosterols with lower efficiency compared to cholestenone. Please explain why. Can docking of cholesterol and other phytosterols into the active site give some evidence? 8) Cholesterol is much more wild-spread than cholestenone in organisms. Is it possible that cholesterol is not the direct substrate of CYP204 but catalyzed by another enzyme first to form cholestenone? 9) Please unify the format of cited references.", "reviewer_3": "This article by Tian et al. details the discovery and characterization of a series of bacterial P450scc enzymes that facilitate the side-chain cleavage of steroids in steroid catabolism. By conducting a comparative analysis of the phylogenetic origins of bacterial and mammalian P450scc enzymes, the authors highlight the convergent evolution of these isoforms. This study demonstrates the potential of bacterial P450scc enzymes as versatile and engineerable biocatalytic platforms for the synthesis of pregnenolone and progesterone, key precursors in steroid drug production. Through targeted mutagenesis, the authors show how specific amino acid substitutions influence substrate positioning and alter product profiles and catalytic activity, underscoring the complexity of enzyme dynamics that govern activity towards particular substrates. They further compare the mechanism and activity of the bacterial variants to their mammalian counterparts. Throughout this work, the authors utilize a comprehensive suite of techniques, including bioinformatics, biochemical assays, X-ray crystallography, and molecular simulations to support their claims. Overall, the manuscript is thorough and well-structured. The findings are well-justified, informative, and suitable for publication in Nature Communications after several minor revisions. There are two minor points that need to be addressed prior to acceptance for publication of the manuscript. 1) Figure 4E: The arrow pushing needs to be fixed and it is suggested that the proposed mechanism is shown in more complete detail. The arrow-pushing mechanism in Figure 4E is incorrect: The half arrows drawn from the porphyrin radical to the double bond of the Fe(IV)=O are in the wrong direction. Upon initiation of hydroxylation by Cpd-I, the porphyrin is reduced to neutral and Cpd-II, an Fe(IV)OH, is formed. In other words, one of the double bonds is homolytically cleaved with one electron going to the porphyrin and one electron going to form a bond with the substrate hydrogen that is abstracted during rebound. Additionally, the mechanistic scheme is somewhat confusing to look at since the outcome of the rebound mechanism is not drawn but instead cycle 3 with Cpd-I again is shown directly after the arrow. It would be helpful to see a complete version of what the authors are proposing. 2) The electron density for 9UIG should be reexamined. While the structure of CYP203A3 (9UIG) is generally well-determined and the statistics are quite good, the electron density needs to be re-examined. There is electron density in several places in the map, but in particular in the F/G loop that is unattributed (i.e. there is significant residual positive Fo-Fc density) that has clear secondary structure and may represent the unmodeled components of the CYP204A3 structure. The authors made a reasonable choice to leave some parts of the protein unmodeled in the absence of certainty about how to connect the chains. However, examination of the electron density suggests that the model could be further improved especially near residues 181 – 192. Minor suggestions: 1.Throughout the results section, the authors start many paragraphs with infinitives in a very repetitive fashion that could be changed to improve readability and flow of the manuscript: Line 129: To evaluate Line 176: To further determine Line 188: In order to further investigate Line 211: To elucidate the Line 221: To investigate this Line 245: To test this hypothesis, Line 266: To further enhance Line 293: For the purpose to elucidate the 2. In the sentence on Line 178: “The results showed that only 13 and 16 were further converted into 2 (Fig. 4B), whereas 14 and 15 were unreactive, confirming 13 and 16 as intermediates in the CYP204A5 catalytic process.” The word “confirming” should be changed to “supporting” as this experiment reasonably supports the involvement of 13 and 16, but there are several alternative hypotheses that one could propose to achieve these results (e.g. changes in substrate binding/ reorientation). The findings from this experiment are well-justified, but “confirming” a mechanism or intermediates is too strong of language and would require much stronger (and challenging to obtain) evidence. 3. In Figure 2 Line 120: “Maxingmum Likelihood method.” Should be Maximum 4. In Figure 6: Only 1 of the porphyrin nitrogens is bonded to the Fe. All 4 nitrogens should be bonded (or none of them as in Figure 5). 5. In Figure 6: It would be more informative to plot B, D, and F as histograms to get a sense of the distributions and differences in distances between variants. 6. The molecular structures of 13, 16, 17 come after they are referenced in Figure 2. While they are introduced in the text, it would be helpful to introduce the structures prior to referencing them in a figure. 7. In Figure 5 Lines 258-259: “The distances between iron-oxo species of CYP204A3 and C20/22 of cholestenone are indicated with yellow dash lines”. There is no iron-oxo in this figure. The distances appear to be between the Fe and carbons directly.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "We sincerely appreciate your insightful and constructive feedback on our work, particularly the valuable comments and suggestions regarding the MD simulations and molecular docking analyses. These comments have significantly strengthened the rigor and interpretation of our computational findings. In response, we have implemented the following key revisions: (1) Revised all MD and docking protocols with enhanced parameters and validation steps.; (2) Conducted three independent MD replicates to ensure reproducibility; (3) Added quantitative comparisons (e.g., RMSD); (4) Performed complementary mutational biochemical assays to corroborate computational predictions. On the other hand, despite extensive efforts, we were unable to obtain co-crystal structures of the protein with its steroid substrates. Given CYP204-mediated multi-step catalytic mechanism, molecular docking and MD simulations alone cannot fully elucidate the process. We therefore interpret the MD simulation results as a possible mechanism that may contribute to the observed activity enhancement. Our core conclusions regarding the novel activity and mechanism of CYP204A remain robust. The revised manuscript now provides a more balanced interpretation of these computational results while maintaining the strength of our experimental findings. The point-by-point response is below. Upon revision, the points given below should be addressed. Major concerns: -It is unclear if CYP204A3, whose structure was solved crystallographically, differs from the most efficient candidate identified in this study, CYP204A5, in substrate specificity. The authors only remark on its lower activity, but a thorough comparison of enzyme specificity would be necessary to assess how good of a template this structure is for CYP204A5. Likewise, some measures like sequence identity and sequence alignment would should be provided to assist assessment of the authors’ choice to crystallize this enzyme. Response: We thank the reviewer for this important point. We supplemented these following data demonstrate that despite CYP204A3's lower catalytic activity it shares key functional characteristics with CYP204A5: (1) 70.3% sequence identity and conserved pocket residues (Supplementary Fig. S15). (2) Identical substrate promiscuity profiles and intermediate production (Fig. 3A and Supplementary Fig. S8). The reliability of our structural modeling approach is validated by the exceptional agreement between AlphaFold3's CYP204A3 prediction and our experimental structure (RMSD = 0.488 Å; Supplementary Fig. S9), demonstrating AlphaFold3's reliability for modeling the CYP204 family. While we extensively attempted CYP204A5 crystallization, its AlphaFold3 model proved reliable for our mechanistic studies. These data collectively support CYP204A3 as a valid structural proxy for CYP204A5.\" We add these results now included in the revised manuscript Text-line237-246- clean version. -From their comparison of the crystallographic structure of CYP204A3 to mammalian CYP11A1 the authors conclude that the former is less specific due to a looser fit of the docked substrate. This comparison seems very challenging as they compare between a crystallographic holo- and an apo-structure, thus, potentially missing out on induced-fit effects taking place upon substrate binding. It might be necessary to compare to the converged MD-structure of the docked protein-substrate complex, compare quantities like ligand RMSDs or even try to co-crystallize a steroid bound complex to substantiate this point. Response: We thank the reviewer for raising this important point regarding the comparisons between CYP204A3 and CYP11A1. We performed comparative MD simulations of substrate-bound CYP204A3 and the mammalian CYP11A1-cholesterol complex (PDB: 3N9Y). Quantitative analysis revealed higher ligand RMSD values in CYP204A3 (relative to protein backbone atoms; Supplementary Fig. S12), indicating greater conformational flexibility of the bound substrate. This enhanced active site plasticity may contribute to CYP204A3's broader substrate specificity compared to the more constrained CYP11A1. In addition, we also systematically attempted to obtain co-crystal structures with multiple sterols, only the apo form of CYP204A3 was successfully determined. Given the complex nature of P450-substrate interactions, we interpret these findings cautiously and present them as one potential factor among several that may influence substrate range. We add these results and discussion included in the revised manuscript Text-line254-258-clean version. -The manuscript lacks any thorough validation of the docking poses of cholestenone to CYP204A3: what were the docking scores? Response: We thank the reviewer for this valuable comment. The docking score for CYP204A3-cholestenone is predicted to be -8.976 kcal/mol. Notably, the highest-scoring conformation was not selected—instead the second. We prioritized: (1) Steric feasibility for C20/C22 of the side chain in close proximity to the heme center.; (2) refer to mammalian P450scc structural and mechanism analysis data. To further validate the docking poses, we supplemented experimental evidence from alanine-scanning mutagenesis in CYP204A3 (Supplementary Fig. S11), which confirmed key active-site residues; Furthermore, motif-swapping experiments involving CYP204A3 and CYP204A5 provide strong support for the reliability of our chosen conformations (Fig. 5E). We added related results and discussion included in the revised manuscript, Text-line 250-253- clean version. How do the poses e.g. compare to alignments with CYP11A1:Cholesterole? Likewise, it would be good to give a proper interaction plot, not only the Surface-fit presented in Fig S8 and to provide the docked structure as supplementary material. Response: We thank the reviewer for these constructive suggestions. We have now included a detailed structural alignment comparison between our CYP204A3-cholesterol docked complex and the CYP11A1-cholesterol crystal structure in Supplementary Fig. S10; The docked structure files have been provided as supplementary Data: MD-simulation-Data Dataset 3. -Likewise, any critical validation of the performed MD simulation is missing. Without any measure of convergence besides the C20, C22-Fe distances, it is impossible to assess if the simulation-lengths & choice to only simulate one replica per system are sufficient. This is very important as some of these distance-plots e.g. in Fig. 6 B & D as well as S11 B & S12 B display increases/instabilities just before the simulations were terminated indicating some structural rearrangements. Such measures could include e.g. protein & ligand RMSDs over all trajectories. Response: We appreciate the reviewer’s insightful comment regarding the validation of simulation convergence. To rigorously address this concern, we have now performed three independent replicas for each MD simulation and analyzed the backbone RMSD of all trajectories to ensure system stability (Supplementary Fig. S12, S13, S17, S19 and S20). -In the sentence ‘Closer examination of the substrate-binding pocket further identified several key amino acid residues […]’ (p. 9) it is unclear how this examination was performed. Was this based on quantitative measurements such as contact analyses or on visual inspection? This information is necessary to assess the validity of all subsequent experiments. Response: We thank the reviewer for this important clarification. The key residues were identified through analysis of docking models using PyMOL (interaction distance < 5 Å). We have revised the text to state: \" Analysis of the docked model reveals potential ligand –receptor interactions mediated by several key amino acid residues within 5 Å—specifically S80, M81, F84, Y93, S94, L253, W256, A257, and T261—which may play a pivotal role in positioning the side chain of CHO above the heme group\" in Text-line 260-263-clean version to clearly reflect the analytical approach. -The ‘Methods’ & ‘Supplementary methods’ sections lack essential information on the simulation-based assays: -Which parameters were used in SwissDock ? Response: We adjusted the position and size of the box to align it above the heme of P450 and meet the online computational limitation. Other system's default parameters for the rest. The relevant parameters are added in Supplementary Table S6. -Which implicit solvent model was used in DFT? Response: The solvent effects were modeled using the integral equation formalism variant of the polarizable continuum model (IEFPCM). We have now explicitly stated this in the revised Methods section: “To account for solvent effects, the IEFPCM implicit solvation model was applied throughout the optimization [ Chemical Physics 55.1 (1981): 117-129. & Chemical reviews 105.8 (2005): 2999-3094.].”. -Which spin-state was chosen to represent CPD1? How high was the associated spin-contamination? High spin-contamination can over-delocalize charges over the molecule, thus, affecting RESP calculations. Response: A quartet spin-state (spin multiplicity = 4) was used for our model of Compound I (CPD1). This choice is well-precedented and consistent with previous DFT studies on the heme Compound I intermediate (e.g., J. Comput. Chem. 2012, 33 (2), 119). To assess the degree of spin contamination, we analyzed the expectation value of the spin-squared operator, <S²>. The calculated <S²> value before annihilation was 3.7880. This value is very close to the ideal theoretical value of 3.75 for a pure quartet state (S=1.5). The deviation is only ~1.01% from the ideal value, indicating a very low and acceptable level of spin contamination. Furthermore, the <S²> value after the annihilation of the first spin contaminant is 3.7507, which is nearly identical to the ideal value, further confirming the appropriateness of the chosen wavefunction. We agree with the reviewer that high spin-contamination can be a concern for charge distribution. However, given the minimal spin contamination observed in our system, we are confident that its effect on charge delocalization and the reliability of the subsequent RESP calculations is negligible. We have added the following clarification to the Methods section: “The spin multiplicity was set to 4, in agreement with previously published studies [ J. Comput. Chem. 2012, 33 (2), 119–133].”. -How were the RESP charges computed in Multiwfn? Which fitting algorithm etc. was utilized? How were the charges made compatible with AMBER? Response: Similar to Antechamber, Multiwfn also applies the standard RESP method for deriving atomic charges, as originally described by Kollman, P. A. et al. (J. Phys. Chem. 1993, 97(40), 10269). That is, a two-step charge fitting is performed. In the first stage, a weak hyperbolic restraint is imposed on the atomic charges to allow the greatest degree of freedom for the charges on polar atoms to fit the ESP. In the second stage, the fitting is restricted to the charges of sp3 hybridized carbons, methylene carbons, and their attached hydrogen atoms, while enforcing charge equivalency for chemically equivalent hydrogens. Further details of this procedure are provided in section 3.9.16 of the Multiwfn software manual. The application of Multiwfn for generating RESP charges is well-established and has been reported in numerous publications (e.g., Nat. Commun., 2024, 15 (1), 6249). why did the authors choose to use Multiwfn for RESP charge computations instead of the native AMBER program Antechamber which can also compute RESP charges from ESP calculations with Gaussian. Likewise, why was the ‘sobtob’ tool utilized instead of Antechamber Response: The parameterization of the iron-containing complex presented a challenge, as the standard Antechamber protocol lacks support for RESP charge derivation involving iron centers. To circumvent this limitation, we employed Multiwfn for the charge calculation. Furthermore, while AmberTools is integral to the Amber simulation package, its direct application here was impractical due to inherent format incompatibilities with GROMACS, our chosen MD engine. We therefore leveraged the Sobtop program to construct the molecular topology. Sobtop was uniquely suited for this task as it systematically assigns GAFF atom types where applicable, while seamlessly integrating parameters from the Universal Force Field (UFF) for atoms not covered by GAFF, such as the central iron ion. Sobtop generates GROMACS-compatible topology files that maintain consistency with the overarching Amber force field, providing a robust and streamlined workflow for our simulations. How was the system protonated and at what pH? Response: We performed protonation at pH 7.4 using gmx pdb2gmx (GROMACS) with the force-field default protonation rules. Asp/Glu were set deprotonated, Lys/Arg protonated, and termini in their zwitterionic forms. Histidines were assigned as neutral HID/HIE tautomers according to local hydrogen-bonding suggested by pdb2gmx. We have added these details to the Methods section. References for the AMBER14SB-PARMBSC1 and GAFF force fields and the TIP3P water model should be given. Response: Related references have been added to the supplementary method. AMBERFF14SB：Maier, J.; Martinez, C.; Kasavajhala, K.; Wickstrom, L.; Hauser, K.; Simmerling, C., ff14SB: Improving the Accuracy of Protein Side Chain and Backbone Parameters from ff99SB J. Chem. Theory Comput. 2015, 11, 3696-3713. GAFF force fields：Wang, J., Wolf, R.M., Caldwell, J.W., Kollman, P.A. and Case, D.A. (2004), Development and testing of a general amber force field. J. Comput. Chem., 25: 1157-1174. https://doi.org/10.1002/jcc.20035 TIP3 water model：William L. Jorgensen, Jayaraman Chandrasekhar, Jeffry D. Madura, Roger W. Impey, Michael L. Klein; Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 15 July 1983; 79 (2): 926 – 935. https://doi.org/10.1063/1.445869 -How many counter-ions were added to the simulation systems, i.e. at which concentration? Response: We have clarified this in the Methods section with the following addition: \" Four sodium ions were added to neutralize the total charge of the system\"; no additional ions were included.\" -No information on electrostatics, treatment of periodic boundary condition etc. are given for the energy minimizations Response: - We are grateful to the reviewer for their diligent feedback, which has been instrumental in helping us identify and address the descriptive shortcomings within the Methods section. We have now specified the initial periodic boundary conditions for the system in the Methods section: “The protein complex was solvated in a rectangular box, with periodic boundary conditions applied in all directions. The exact dimensions for each simulation system are provided in checklist-4a.xlsx.” Furthermore, the description of the electrostatic interactions has been revised to clarify its application throughout all simulation stages, including energy minimization: “Throughout the energy minimization and subsequent simulation steps, long-range electrostatic interactions were treated with the particle mesh Ewald (PME) method using a 1.2 nm cutoff for the Coulomb interactions. Van der Waals (vdW) interactions cutoff was also set to 1.2 nm” -The ‘Methods’ section of the manuscript essentially only describes experimental methods. Despite majorly contributing to the manuscript, almost all bioinformatics- and simulation-related methods have to be searched for in the Supplementary materials. Especially in the light of our concerns about critical information lacking there, it is unclear to us why the methodology was fragmented like this. Response: We appreciate the reviewer's valid concern regarding the organization of methodological details. To improve clarity and accessibility, we have restructured the Methods section to integrate all computational and bioinformatics approaches alongside experimental procedures in the main manuscript. Minor concerns: -Language: some incomprehensible sentences, some repetitions/tautologies (e.g. ‘[…] plant CYP87A family P450scc enzymes […] have recently been identified in several plants’); many titles of Supplementary materials erroneous Response: We have revised ambiguous sentences, removed tautologies (e.g., simplified CYP87A description), and corrected all supplementary material titles. -The authors’ initial screening approach seems to undervalue the importance of local vs. global similarity. The way we understand their initial bioinformatic approach, it was based essentially on just selecting one template enzyme and a global alignment to identify initial hits. Since CYPs are very well known & relatively conserved, it might have been more successful if the authors would have instead focused on local alignments in the known substrate-coordinating regions of steroid-metabolizing CYPs. This point should be elaborated on in the introduction and/or discussion Response: We sincerely appreciate the reviewer's insightful suggestion regarding our bioinformatic approach. Our primary objective was to identify potential bacterial steroid P450 hydroxylases based on phylogenetic analysis with known steroid-metabolizing CYPs, as described in the initial mining section (Text-line 107-109). We recognize that detailed analysis of substrate-coordinating regions would provide a more precise method for identifying steroid-specific P450s. We expanded the discussion section in Text-line 369-375-clean version to highlight the potential of future large-scale analyses examining sequence-structure-substrate relationships of known steroid-metabolizing CYPs. This structural bioinformatics approach could enable more targeted mining of steroid-metabolizing enzymes in future studies. -The manuscript's clarity would greatly profit from using proper abbreviations instead of numbers to refer to different compounds e.g. PROG/PREG/…, particularly as this gets very confusing when reaction steps are discussed. Response: We appreciate this excellent suggestion. In the revised manuscript, we have implemented the following changes: Systematic abbreviation use: Introduced standard steroid and intermediates abbreviations (e.g., PROG for progesterone, PREG for pregnenolone) at first mention. -The text in Fig. 2 is too small and not readable Response: The text in Figure 2 has been enlarged and reformatted for clarity. -Fig. 4 E: the presented mechanism does not seem to make sense: where does the third oxygen in ‘Cycle 3’ come from? Is it derived from a water molecule as in Cycle 3, CPD1 still carries an oxygen? Response: The third oxygen in Cycle 3 originates from molecular oxygen (O₂). As depicted in the figure, the catalytic side-chain cleavage of P450scc is a stepwise oxidation reaction. Each oxidation step represents a new cycle of P450 activity. This process uses the classical pathway of P450 to form CpdI. In accordance with Reviewer 3's suggestion and to enhance clarity, we have revised Figure 4E. -Actual means & standard deviations for the distances measured over the MD simulation should be reported instead of discussing ‘approximate values’ in paragraph 2 on p. 9 Response: We have revised in text as exact mean ± SD distances (4.04±0.41 Å and 4.10±0.45 Å, respectively) are now reported. -The title of table S2 is entirely unclear to us (‘Information of redox used in this study’) Response: The title has been revised to: Redox protein systems utilized for P450 activity in this study. -In the Supplementary methods, the authors refer to AF3 models as homology models which is wrong. Response: We corrected \"homology models\" to \" The predicted structures\" in Supplementary Methods. -Why did the authors choose the AMBER14SB-PARMBSC1 force field? Response: We acknowledge the potential for confusion and have removed the reference to PARMBSC1, which is a force field specifically parameterized for DNA and is not relevant to our work. Our study exclusively employed the AMBER14SB force field, a choice motivated by its demonstrated robustness and accuracy in protein simulations. The suitability of this force field is further substantiated by its successful application in recent, comparable studies (e.g., JACS Au 2024, 4 (4), 1591 – 1604). Reviewer #2 (Remarks to the Author): The manuscript by Tian et al. describes the discovery of bacterial P450scc enzymes in steroid-degrading microorganisms. The authors reveal that these bacterial enzymes proceed via a different mechanism compared to the mechanism of the comparable enzymes found in mammals and plants. The authors also perform engineering work based on one obtained bacterial P450scc enzyme structure. Overall this is an excellent paper, providing new insights into bacterial metabolism and also highighting a new biocatalytic tool for production of progesterone-derived compounds. Response： We thank the reviewer for high appreciation of this work. Below are minor commenst: 1) The stereochemistry of the compounds in Fig. 1 is neither complete (1, 2 ,3, and 6), nor correct (Fig. 1C, intermediate 6). Response： The stereochemistry of compounds has been corrected and completed in the revised Fig.1. 2) The intermediates in Fig. 1. are numbered wrongly. Response：The numbering in Fig. 1 has been corrected 3) Please make Fig. 3B more reader friendly since some annotations are upside-down. Response：Annotations have been reoriented for clarity in the revised figure 4) Please explain why the peaks for 5 in Fig. 4D are not consistent by retention time. Response： As described in the main text (in Text-line 212-213 clean version), these peaks with MW 419 in Fig 4D CYP204A5+ 20S-HC represent unidentified dihydroxylation CHL products, which are not 5 (20R,22R-DHC). To avoid confusion, they have been annotated in the newly revised legend and figure. 5) Figs. 4B and 4D: By comparing the retention time, pregnenolone (1) seems to be eluted earlier than progesterone (2), which is supposed not to be such result using C18 column. Response：Thanks for your careful examination. This deviation is primarily due to the fact that Figure 4B presents an EIC from UPLC-MS analysis, in which the retention times exhibit a slight delay compared to the corresponding UPLC chromatogram. The ion chromatograms of PREG and PROG are provided in the figure below to more clearly illustrate the difference in retention times between the two compounds. In fact, these two compounds have very similar retention times in our analysis. 6) Lines 110, 112, 167 and 185: supporting information is not specified. Response：Lines 110, 112, 167, and 185 refer to NMR structural data. To improve clarity and maintain consistent numbering of figures between the main text and the supplementary material, we have retitled this section in main text as “Supplementary spectral Data.” The supporting information file includes complete spectral details for each relevant compound. 7) CYP204 catalyzes cholesterol and other phytosterols with lower efficiency compared to cholestenone. Please explain why. Can docking of cholesterol and other phytosterols into the active site give some evidence? Response：Docking results of CYP204A3 with cholesterol (A) and other phytosterols (B) are provided below. Combined with MD simulation analysis for cholestenone (CHO), we propose that the lower catalytic efficiency of CYP204 toward cholesterol and phytosterols stems from differences in substrate-enzyme interactions: (1) The 3-keto group in cholestenone forms distinct hydrogen bonds with active site residues, unlike the 3-hydroxyl group in cholesterol; (2) Phytosterols experience steric hindrance due to their varied alkyl side chains, in addition to the 3-hydroxyl group, which disrupts optimal positioning of C20/C22 near CpdI. We add related discussion in Text-line 249-254 clean version. 8) Cholesterol is much more wild-spread than cholestenone in organisms. Is it possible that cholesterol is not the direct substrate of CYP204 but catalyzed by another enzyme first to form cholestenone? Response：This is indeed plausible, as 3β-hydroxysteroid dehydrogenases (3β-HSDs) are widely present in steroid-metabolizing bacteria （Microorganisms 2021, 9, 469. https://doi.org/10.3390/microorganisms9030469）and could catalyze the oxidation of cholesterol's 3β-hydroxyl group to form cholestenone prior to CYP204-mediated modification. 9) Please unify the format of cited references. Response：we have unified the format of cited references. Reviewer #3 (Remarks to the Author): This article by Tian et al. details the discovery and characterization of a series of bacterial P450scc enzymes that facilitate the side-chain cleavage of steroids in steroid catabolism. By conducting a comparative analysis of the phylogenetic origins of bacterial and mammalian P450scc enzymes, the authors highlight the convergent evolution of these isoforms. This study demonstrates the potential of bacterial P450scc enzymes as versatile and engineerable biocatalytic platforms for the synthesis of pregnenolone and progesterone, key precursors in steroid drug production. Through targeted mutagenesis, the authors show how specific amino acid substitutions influence substrate positioning and alter product profiles and catalytic activity, underscoring the complexity of enzyme dynamics that govern activity towards particular substrates. They further compare the mechanism and activity of the bacterial variants to their mammalian counterparts. Throughout this work, the authors utilize a comprehensive suite of techniques, including bioinformatics, biochemical assays, X-ray crystallography, and molecular simulations to support their claims. Overall, the manuscript is thorough and well-structured. The findings are well-justified, informative, and suitable for publication in Nature Communications after several minor revisions. Response: We are sincerely grateful to the Reviewer for their generous appreciation and positive assessment of our study. There are two minor points that need to be addressed prior to acceptance for publication of the manuscript. 1) Figure 4E: The arrow pushing needs to be fixed and it is suggested that the proposed mechanism is shown in more complete detail. The arrow-pushing mechanism in Figure 4E is incorrect: The half arrows drawn from the porphyrin radical to the double bond of the Fe(IV)=O are in the wrong direction. Upon initiation of hydroxylation by Cpd-I, the porphyrin is reduced to neutral and Cpd-II, an Fe(IV)OH, is formed. In other words, one of the double bonds is homolytically cleaved with one electron going to the porphyrin and one electron going to form a bond with the substrate hydrogen that is abstracted during rebound. Additionally, the mechanistic scheme is somewhat confusing to look at since the outcome of the rebound mechanism is not drawn but instead cycle 3 with Cpd-I again is shown directly after the arrow. It would be helpful to see a complete version of what the authors are proposing. Response：Thanks for your suggestions, the Figure 4E have been revised for clarify its mechanism. 2) The electron density for 9UIG should be reexamined. While the structure of CYP203A3 (9UIG) is generally well-determined and the statistics are quite good, the electron density needs to be re-examined. There is electron density in several places in the map, but in particular in the F/G loop that is unattributed (i.e. there is significant residual positive Fo-Fc density) that has clear secondary structure and may represent the unmodeled components of the CYP204A3 structure. The authors made a reasonable choice to leave some parts of the protein unmodeled in the absence of certainty about how to connect the chains. However, examination of the electron density suggests that the model could be further improved especially near residues 181 – 192. Response： We thank the reviewer for highlighting the need to re-examine the electron density in the CYP203A3 (9UIG) structure, particularly in the F/G loop region (residues 181-192). In response, we have carefully reanalyzed the electron density maps and successfully modeled all previously unassigned residues in this region. The updated model shows good agreement with the electron density, though some flexibility remains evident in parts of the loop. We have addressed the residual Fo-Fc density by properly incorporating these residues into the refined structure. To support our revisions, we provide the updated PDB (9WAT) and MTZ files along with a figure showing the 2Fo-Fc electron density (contoured at 1.0 σ) for residues 182-192, demonstrating the improved model quality. These modifications have enhanced both the completeness and accuracy of the structural model. We add revised file in Supplementary attachment: CYP204A3 structure data for reviewers Dataset 4. Minor suggestions: 1.Throughout the results section, the authors start many paragraphs with infinitives in a very repetitive fashion that could be changed to improve readability and flow of the manuscript: Line 129: To evaluate Line 176: To further determine Line 188: In order to further investigate Line 211: To elucidate the Line 221: To investigate this Line 245: To test this hypothesis, Line 266: To further enhance Line 293: For the purpose to elucidate the Response：We revised the manuscript to improve readability by varying sentence structure throughout the Results section, eliminating the repetitive use of infinitives to begin paragraphs. 2. In the sentence on Line 178: “The results showed that only 13 and 16 were further converted into 2 (Fig. 4B), whereas 14 and 15 were unreactive, confirming 13 and 16 as intermediates in the CYP204A5 catalytic process.”The word “confirming” should be changed to “supporting” as this experiment reasonably supports the involvement of 13 and 16, but there are several alternative hypotheses that one could propose to achieve these results (e.g. changes in substrate binding/ reorientation). The findings from this experiment are well-justified, but “confirming” a mechanism or intermediates is too strong of language and would require much stronger (and challenging to obtain) evidence. Response：We changed \"confirming\" to \"supporting\" to more accurately reflect the level of evidence provided by these experimental results regarding intermediates. 3. In Figure 2 Line 120: “Maxingmum Likelihood method.” Should be Maximum Response：The misspelling \"Maxingmum\" has been corrected to \"Maximum\" in Figure 2. 4. In Figure 6: Only 1 of the porphyrin nitrogens is bonded to the Fe. All 4 nitrogens should be bonded (or none of them as in Figure 5). Response：We have corrected the iron-porphyrin coordination in Figure 6 to properly show all four nitrogen atoms bonded to the central iron. 5. In Figure 6: It would be more informative to plot B, D, and F as histograms to get a sense of the distributions and differences in distances between variants. Response：We appreciate this excellent suggestion. The distance distribution data in Figure 6 have been revised to better visualize the statistical distributions and differences between variants. 6. The molecular structures of 13, 16, 17 come after they are referenced in Figure 2. While they are introduced in the text, it would be helpful to introduce the structures prior to referencing them in a figure. Response：We have replaced numerical designations with standardized abbreviations for compounds and intermediates, as suggested by the reviewers 1, to enhance clarity and differentiation throughout the text. 7. In Figure 5 Lines 258-259: “The distances between iron-oxo species of CYP204A3 and C20/22 of cholestenone are indicated with yellow dash lines”. There is no iron-oxo in this figure. The distances appear to be between the Fe and carbons directly. Response：We have revised the description of Figure 5 to accurately reflect that the measured distances are between the iron center and substrate carbons (C20/22), not an iron-oxo species. Reviewer #4 (Remarks to the Author): I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts. Response: We sincerely appreciate the constructive comments and the time dedicated to evaluating our work. Reviewer #5 (Remarks to the Author): I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts. Response: We sincerely appreciate the constructive comments and the time dedicated to evaluating our work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have put in substantial effort into revising their manuscript, addressing most of our concerns, in part through conducting additional experiments and simulations. However, several critical points remain to be revised before we can recommend this article for publication. Note that this review is mainly focused on the molecular modeling and simulation components of the manuscript. The most important ones are: -While the authors have now included some raw simulation data (input/output pdbs, some box sizes and some non-bonded force field parameters), the data currently provided would not be sufficient to reproduce their results. Full MD-trajectories (.xtc/.trr files with GROMACS) are likely too big to be uploaded but it would be necessary to include all other files (.mdp, .top, .gro, .log, .ndx,...) in order to understand and recreate their results. -Likewise, all docking poses should be included. -The Gaussian input & output files are also missing. -The newly added comparison between the Alphafold model of CYP204A3 & their experimental structure (Supplementary Fig. S9), would profit from a close-up of the binding-site residues (side-chain representations). There is a notable deviation in the variable F-G loop region which could affect ligand binding and this should be stated in the main text where the alphafold model is mentioned. -In addition to the ligand-RMSDs reported for the new MD-simulations of CYP204A3:substrate vs. CYP11A1-Cholesterol, the claim that CYP204A3 displays higher active site plasticity cannot be assessed merely by ligand RMSD. Additional RMSD measurements of the active-site residues would be necessary to substantiate this claim. -In the response to our question about the docking scores, it is not clear if -8.976 kcal/mol is the score of the best or second best pose (used for modeling). Please also include all docking scores in a Supplementary table, along with other criteria that were used to select the relevant pose. -In the same section, the authors reference the newly added Supplementary Fig. S11. From the caption of this figure, it is not stated which kind of data is presented. The caption should be revised to specify the kind of spectra. -In their response to our questions concerning the SwissDock parameters, the authors refer to the newly added Supplementary Table S6. This table does not include any information on SwissDock. However, it reports the usage of Autodock Vina and another docking method named ‘AC’. Neither Autodock Vina nor the other program is mentioned anywhere in the main text. Which docking program did the authors use? If Vina was used, it should be described and referenced. Also, it is unclear to us what ‘AC’ refers to. We are not familiar with any program of this name. It should be discussed & referenced correctly. If ‘AC’ is an abbreviation, the full name should be given. Table S6 also lacks information on units. -In their response to our concerns on spin-contamination, the authors correctly conclude that their reported <S^2> value is low enough to merit a very accurate DFT calculation. However, they only report this value in their response to our comments. Please include it in the Supplementary material so that future readers also know that these computations are reliable. Likewise, the imposed spin-multiplicity should be included. Both of these concerns can be addressed by making the Gaussian input and output files available. -The authors should report the precise procedure utilized to derive RESP with Multiwfn charges in the Supplementary Material. The program offers many options during this procedure e.g. in the inclusion of constraints, the choice of fitting points and many more. -Figure 4E is still very hard to read. It is not immediately clear that each reaction step involves an entire catalytic cycle of the P450 and the inclusion of the ferric, water-bound resting state at the bottom of each figure part makes it even more confusing. As the reactions are also depicted in Fig. 1(C), it might be easier to entirely remove this panel (4E) and explicitly mention that the enzymes catalyze two reactions which both involve Cpd1 as the active species. -In Figure 6, histograms are given of distances analysed for the wild-type and mutant CYP 204A5. The authors need to provide information on what the curves are in this figure and the corresponding figures in the SI. If they are Gaussian fits, are these justified as they do not always fit the data well, especially for distance d2? A better treatment might omit the fit lines and only consider the position and height of the highest histogram bars. This would still support the trends towards shorter distances in the mutants.", "reviewer_2": "All of my comments have been addressed comprehensively. I recommend publication in Nature Communications.", "reviewer_3": "The authors have made a significant effort to address my (and other reviewers') concerns and sufficiently addressed a majority of the comments raised.", "reviewer_4": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We sincerely thank the reviewer for this important comment concerning reproducibility. In response, we have now provided all essential GROMACS input and output files, docking poses and Gaussian input & output files in supplementary database. For GROMACS input and output files, the .tpr files were generated using GROMACS version 2022.4, and they contain complete information on the system topology, force field parameters, and simulation settings. These files can be directly executed with the command gmx mdrun -s filename.tpr, allowing other researchers to fully reproduce the reported simulations. Due to the large size of the .xtc (trajectory) and .log (run log) files —over several gigabytes in total across all replicates—these files were not uploaded. However, the provided files include all necessary information for verification and reproduction of the simulations, ensuring transparency and reproducibility of our computational results. These data can be found in MD-simulation-Data Dataset 3. -The newly added comparison between the Alphafold model of CYP204A3 & their experimental structure (Supplementary Fig. S9), would profit from a close-up of the binding-site residues (side-chain representations). There is a notable deviation in the variable F-G loop region which could affect ligand binding and this should be stated in the main text where the alphafold model is mentioned. Response: We thank the reviewer for this suggestion. As requested, we have added the binding-site residues to Supplementary Fig. S9B. We highlighted the deviation in the F-G loop region in the main text where the AlphaFold model is mentioned (line 233-234 in main text). -In addition to the ligand-RMSDs reported for the new MD-simulations of CYP204A3: substrate vs. CYP11A1-Cholesterol, the claim that CYP204A3 displays higher active site plasticity cannot be assessed merely by ligand RMSD. Additional RMSD measurements of the active-site residues would be necessary to substantiate this claim. Response: We appreciate your valuable suggestion. Following this advice, we calculated the RMSD values of the active-site residues for both CYP204A3 and CYP11A1 during the MD simulations. As shown in the newly added Supplementary Fig. S12C and D, the active-site residues of CYP204A3 exhibit higher RMSD values and broader fluctuations than those of CYP11A1, indicating greater conformational flexibility. Revisions to the main text are also included in line 243-244 in main text. -In the response to our question about the docking scores, it is not clear if -8.976 kcal/mol is the score of the best or second best pose (used for modeling). Please also include all docking scores in a Supplementary table, along with other criteria that were used to select the relevant pose. Response: Thank you for this important clarification. The docking score of −8.976 kcal/mol corresponds to the second-best pose, which was selected for further modeling based on its favorable binding conformation and interaction profile. For transparency, we have included all docking scores in Supplementary Table S6, along with detailed criteria used for pose selection. -In the same section, the authors reference the newly added Supplementary Fig. S11. From the caption of this figure, it is not stated which kind of data is presented. The caption should be revised to specify the kind of spectra. Response: We appreciate the reviewer's careful attention to detail. We have revised the caption of Supplementary Fig. S11 to explicitly state that the figure presents. “UPLC profiles of CHO conversion by wild-type CYP204A13 and alanine mutants monitored at 254 nm.” -In their response to our questions concerning the SwissDock parameters, the authors refer to the newly added Supplementary Table S6. This table does not include any information on SwissDock. However, it reports the usage of Autodock Vina and another docking method named ‘AC’. Neither Autodock Vina nor the other program is mentioned anywhere in the main text. Which docking program did the authors use? If Vina was used, it should be described and referenced. Also, it is unclear to us what ‘AC’ refers to. We are not familiar with any program of this name. It should be discussed & referenced correctly. If ‘AC’ is an abbreviation, the full name should be given. Table S6 also lacks information on units. Response: We appreciate the reviewer's insightful comments. Molecular docking was performed using the SwissDock web service (https://www.swissdock.ch/), which integrates two docking engines: Attracting Cavities (AC) and AutoDock Vina. Due to compatibility issues with the AlphaFold3-generated CYP204A5 structure in AutoDock Vina, the Attracting Cavities (AC) engine was employed. The selected docking pose from AC met all predefined evaluation criteria. The revised manuscript now provides comprehensive methodological details in Supplementary Table S6, including parameter specifications and literature citations for both docking engines. -In their response to our concerns on spin-contamination, the authors correctly conclude that their reported <S^2> value is low enough to merit a very accurate DFT calculation. However, they only report this value in their response to our comments. Please include it in the Supplementary material so that future readers also know that these computations are reliable. Likewise, the imposed spin-multiplicity should be included. Both of these concerns can be addressed by making the Gaussian input and output files available. Response: We thank the reviewer for this important observation. The Gaussian input and output files including S2 value are now presented in MD-simulation-Data Dataset 3. We add this information in revised MD simulation method section. Calculated < S2> values for CpdI at B3LYP levels of theory Spin <S2> Method Basis Set <S2> Ideal Multiplicity Calculated B3LYP 6-31G(d)/Lanl2DZ 4 3.7880 3.75 -The authors should report the precise procedure utilized to derive RESP with Multiwfn charges in the Supplementary Material. The program offers many options during this procedure e.g. in the inclusion of constraints, the choice of fitting points and many more. Response: Since the system is incompatible with Multiwfn's automated RESP fitting procedure (due to unsupported capping groups in the second step), we performed manual two-step ESP fitting following RESP principles. In the first step, we conducted initial ESP fitting with a weighting factor (a) of 0.0005 while constraining the total charges of formyl and amide groups to zero. The second step (a=0.0010) refined the charges by only permitting adjustments for sp3 hybridized carbons, hydrogens (excluding capping groups), and two =CH groups, with additional equivalence 2 constraints applied to hydrogens in non-capping -CH , -CH -, and =CH moieties. 3 2 2 The procedure is relatively complex; therefore, all files used for the RESP fitting are provided in the “HEME_4_4_RESP” directory. Detailed steps are described in step.txt, and the final charge results are given in HEME_4_4.chg. The files are added in MD-simulation-Data Dataset 3 -Figure 4E is still very hard to read. It is not immediately clear that each reaction step involves an entire catalytic cycle of the P450 and the inclusion of the ferric, water-bound resting state at the bottom of each figure part makes it even more confusing. As the reactions are also depicted in Fig. 1(C), it might be easier to entirely remove this panel (4E) and explicitly mention that the enzymes catalyze two reactions which both involve Cpd1 as the active species. Response: We thank the reviewer for this valuable suggestion. Upon careful consideration, we have removed the Figure 4E to improve clarity. Instead, we have clarified in the text that both enzymatic reactions utilize Cpd1 as the active species in line 205-210 in main text. -In Figure 6, histograms are given of distances analysed for the wild-type and mutant CYP 204A5. The authors need to provide information on what the curves are in this figure and the corresponding figures in the SI. If they are Gaussian fits, are these justified as they do not always fit the data well, especially for distance d2? A better treatment might omit the fit lines and only consider the position and height of the highest histogram bars. This would still support the trends towards shorter distances in the mutants. Response: We thank the reviewer for this suggestion. Following the recommendation, we have removed the Gaussian fit lines from Figure 6 and corresponding SI figures. The trends toward shorter distances in the mutants remain clearly evident from the histogram peak positions and heights."}, {"round": 3, "reviewer_comments": {"reviewer_1": "While the authors have addressed all our comments on the previous versions in their rebuttal, the accompanying edits to the manuscript are unfortunately not complete and require revision as follows: -GROMACS files: While the authors provide files for production runs, the input files for energy minimization and equilibration still appear to be missing. -RESP: While the files used seem to be present in the supplementary dataset, their usage is not really clear from reading step.txt and, as the procedure used is not standard, the description given in the responses helps to clarify what was done and should be incorporated in the manuscript. -Docking: The methods used have been clarified. However, Table S6 only includes selected docking scores rather than all docking scores. E.g. the score of -8.976 kcal/mol is for the second best score (according to the response) but the value for the best score is not given. In addition, its not clear from the table that this is the second best score and what the ranking of the other scores is. As docking scores always have to be considered in context, the complete output pose and scoring information should be given. The additional criteria in the footnote are qualitative: what are the quantitative criteria for accepting a pose?", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts."}, "author_response": "Response: We have now included the input files for energy minimization and equilibration in the updated Supplementary Data 10. (Figshare DOI: 10.6084/m9.figshare.30657758) -RESP: While the files used seem to be present in the supplementary dataset, their usage is not really clear from reading step.txt and, as the procedure used is not standard, the description given in the responses helps to clarify what was done and should be incorporated in the manuscript. Response: We have expanded the methodological description in the manuscript, incorporating the details provided in updated Supplementary Data 9. (Figshare DOI: 10.6084/m9.figshare.30657758) -Docking: The methods used have been clarified. However, Table S6 only includes selected docking scores rather than all docking scores. E.g. the score of -8.976 kcal/mol is for the second best score (according to the response) but the value for the best score is not given. In addition, its not clear from the table that this is the second best score and what the ranking of the other scores is. As docking scores always have to be considered in context, the complete output pose and scoring information should be given. The additional criteria in the footnote are qualitative: what are the quantitative criteria for accepting a pose? Response: We have included the output information for all docking pose results in the updated Supplementary Data 6. The selection criteria prioritize the option with the highest score that satisfies the first two criteria. This information has been added to the revised Supplementary Table 7."}]

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