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PreprintCited by 9Microbial Natural Products and BiosynthesisMicrobial Metabolism and ApplicationsEnzyme Production and Characterization

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Unravelling the functional diversity of type III polyketide synthases in fungi

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Sokolova, Nika, Denisov, Stepan S., Hackl, Thomas, Haslinger, Kristina·2024·Angewandte Chemie International Edition
DOI 10.1101/2024.11.25.625244PMC PMC12559476

Preprint — published as 10.1002/anie.202514786 · Paper page

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Abstract

Type III polyketide synthases (T3PKSs) are enzymes that produce diverse compounds of ecological and clinical importance. While well-studied in plants and bacteria, only a handful of T3PKSs from fungi have been characterised to date. Here, we developed a comprehensive workflow for kingdom-wide characterisation of T3PKSs. Using publicly available genomes, we mined more than 1000 putative enzymes and analysed their active site architecture and genomic neighbourhood. From there, we selected 37 representative PKS candidates for cell-free expression and prototyping with a diverse set of Coenzyme A activated substrates, revealing unique patterns in substrate and cyclisation specificity, as well as the preferred number of malonyl-Coenzyme A extensions. Using the 341 enzyme-substrate pairs generated in this study, we trained a machine learning model to predict T3PKS substrate specificity and experimentally validated it with an extended panel of non-natural substrates. We anticipate that the model will be useful for in silico screening of T3PKSs, while the insight into the product scope of these enzymes offers interesting starting points for further exploration.

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Figure 1 Scope of this study. a) Typical reactions catalysed by T3PKSs. Iterative decarboxylation of extender units (usually malonyl‐CoA) and condensation with the starter unit (variable). Cyclisation of the linear polyketide yielding pyrone (lactone), resorcinol (aldol), or phloroglucinol (Claisen) product. b) Workflow for kingdom‐wide characterisation of T3PKS. Dotted arrows represent optional steps.
Figure 1 Scope of this study. a) Typical reactions catalysed by T3PKSs. Iterative decarboxylation of extender units (usually malonyl‐CoA) and condensation with the starter unit (variable). Cyclisation of the linear polyketide yielding pyrone (lactone), resorcinol (aldol), or phloroglucinol (Claisen) product. b) Workflow for kingdom‐wide characterisation of T3PKS. Dotted arrows represent optional steps.CC BY · PubMed Central
Figure 2 Clustering and active site residue analysis of fungal T3PKSs. a) Sequence similarity network of the mined enzyme candidates at 80% sequence identity cutoff. Each circle is a representative node grouping protein sequences with >95% sequence identity. Diamond‐shaped nodes represent enzymes with literature precedent. Clusters from which sequences were selected for experimental characterisation are numbered. Clusters composed exclusively of sequences from Aspergillus are marked with an asterisk. The only major cluster where the catalytic triad is not conserved is marked with # (amino acid substitutions: H303D and N336S). b) Comparison of the active site residues between plant T3PKSs (184 reviewed sequences from InterPro) and the mined fungal T3PKSs. The numbering of the residues corresponds to that in MsCHS (model chalcone synthase from Medicago sativa). The active site residue logo was generated using the R package ggseqlogo.[ 29 ]
Figure 2 Clustering and active site residue analysis of fungal T3PKSs. a) Sequence similarity network of the mined enzyme candidates at 80% sequence identity cutoff. Each circle is a representative node grouping protein sequences with >95% sequence identity. Diamond‐shaped nodes represent enzymes with literature precedent. Clusters from which sequences were selected for experimental characterisation are numbered. Clusters composed exclusively of sequences from Aspergillus are marked with an asterisk. The only major cluster where the catalytic triad is not conserved is marked with # (amino acid substitutions: H303D and N336S). b) Comparison of the active site residues between plant T3PKSs (184 reviewed sequences from InterPro) and the mined fungal T3PKSs. The numbering of the residues corresponds to that in MsCHS (model chalcone synthase from Medicago sativa). The active site residue logo was generated using the R package ggseqlogo.[ 29 ]CC BY · PubMed Central
Figure 3 Activity profiling of the cell‐free expressed T3PKSs. a) Panel of CoA thioesters 1–12 used as substrates for activity profiling. b) Maximum‐likelihood phylogenetic tree based on the multiple sequence alignment of 37 fungal T3PKSs (outgroup: PhCHS) combined with a heatmap of enzyme concentrations in the cell‐free lysate and a binary representation of enzyme activity in cell‐free lysates supplemented with substrates 1–12 and malonyl‐CoA. The “SSN” column specifies the corresponding cluster in the sequence similarity network. Enzyme concentrations [E] (0 to 30 µM) were determined with a split‐GFP complementation assay.
Figure 3 Activity profiling of the cell‐free expressed T3PKSs. a) Panel of CoA thioesters 1–12 used as substrates for activity profiling. b) Maximum‐likelihood phylogenetic tree based on the multiple sequence alignment of 37 fungal T3PKSs (outgroup: PhCHS) combined with a heatmap of enzyme concentrations in the cell‐free lysate and a binary representation of enzyme activity in cell‐free lysates supplemented with substrates 1–12 and malonyl‐CoA. The “SSN” column specifies the corresponding cluster in the sequence similarity network. Enzyme concentrations [E] (0 to 30 µM) were determined with a split‐GFP complementation assay.CC BY · PubMed Central
Figure 4 Machine learning model for T3PKS substrate specificity prediction. a) Workflow used to train the predictive machine learning model in this study. b) Performance metrics of the Multilayer Perceptron model for different datasets. The model was trained on the cell‐free activity profiling dataset with ProtTrans‐X5/MACCS Keys feature vectors; error bars indicate standard deviation. c) Heatmap of the predicted versus published substrate specificity for 11 previously characterised T3PKSs from fungi (dataset “Published fungal enzymes”). The activity data were curated from literature (Table S2). The structures of substrates 25–38 is depicted in Figure S6. Substrates that were present in the training dataset of our model are highlighted. d) Heatmap of the predicted versus observed substrate specificity for 10 fungal T3PKSs with non‐natural substrates 13–24 (dataset “Validation extra substrates”). e) Structures of substrates 13–24, 27 and 31 used for experimental validation of the model. f) The observed substrate specificity of 5 new fungal T3PKSs selected using the ML model. All prediction values are averaged from 100 model runs with a random seed. Pink: substrate accepted, white: not accepted.
Figure 4 Machine learning model for T3PKS substrate specificity prediction. a) Workflow used to train the predictive machine learning model in this study. b) Performance metrics of the Multilayer Perceptron model for different datasets. The model was trained on the cell‐free activity profiling dataset with ProtTrans‐X5/MACCS Keys feature vectors; error bars indicate standard deviation. c) Heatmap of the predicted versus published substrate specificity for 11 previously characterised T3PKSs from fungi (dataset “Published fungal enzymes”). The activity data were curated from literature (Table S2). The structures of substrates 25–38 is depicted in Figure S6. Substrates that were present in the training dataset of our model are highlighted. d) Heatmap of the predicted versus observed substrate specificity for 10 fungal T3PKSs with non‐natural substrates 13–24 (dataset “Validation extra substrates”). e) Structures of substrates 13–24, 27 and 31 used for experimental validation of the model. f) The observed substrate specificity of 5 new fungal T3PKSs selected using the ML model. All prediction values are averaged from 100 model runs with a random seed. Pink: substrate accepted, white: not accepted.CC BY · PubMed Central
Figure 5 Product scope of the 37 fungal T3PKSs. a) Product profile of the reactions of cell‐free lysates with substrates 1–12. Ketide number: 2n – diketide; 3n – triketide; 4n – tetraketide; 5n – pentaketide; 6n – hexaketide. b) Extracted ion chromatograms of the reaction products of DhelPKS and AserPKS2 with substrate 9 compared to the “no enzyme” control. Putative tri‐ and tetraketide pyrone products are marked with pink triangle and rectangle, respectively. Putative tetra‐ and pentaketide resorcinol products are marked with blue rectangle and pentagon, respectively. c)–f) HR‐MS/MS spectra and the proposed fragmentation patterns of the above‐mentioned reaction products. The precursor ion is indicated with a dashed grey line labelled with its m/z value. g) Proposed[ 39 ] scheme of diketide quinolone formation from 3. h) HPLC‐based identification of reaction products of purified T3PKSs with 3 at 273 nm. Dotted line represents the authentic standard of 4‐hydroxy‐1‐methyl‐2‐quinolone.
Figure 5 Product scope of the 37 fungal T3PKSs. a) Product profile of the reactions of cell‐free lysates with substrates 1–12. Ketide number: 2n – diketide; 3n – triketide; 4n – tetraketide; 5n – pentaketide; 6n – hexaketide. b) Extracted ion chromatograms of the reaction products of DhelPKS and AserPKS2 with substrate 9 compared to the “no enzyme” control. Putative tri‐ and tetraketide pyrone products are marked with pink triangle and rectangle, respectively. Putative tetra‐ and pentaketide resorcinol products are marked with blue rectangle and pentagon, respectively. c)–f) HR‐MS/MS spectra and the proposed fragmentation patterns of the above‐mentioned reaction products. The precursor ion is indicated with a dashed grey line labelled with its m/z value. g) Proposed[ 39 ] scheme of diketide quinolone formation from 3. h) HPLC‐based identification of reaction products of purified T3PKSs with 3 at 273 nm. Dotted line represents the authentic standard of 4‐hydroxy‐1‐methyl‐2‐quinolone.CC BY · PubMed Central
Figure 6 Genomic neighbourhood of the fungal T3PKSs. a) Comparison of the scirpilin biosynthetic gene cluster in Fusarium scirpi (GenBank: OR551244) and the genomic neighbourhood of the XylPKS‐encoding gene in Xylariaceae sp. FL1272. b) Comparison of soppiline biosynthetic gene cluster in Penicillium soppii (MIBiG[ 56 ]: BGC0003032) and the genomic neighbourhood of the AlupPKS2‐encoding gene in Aspergillus luppii. c) Comparison of the communesin biosynthetic gene cluster in Penicillium expansum (MIBiG: BGC0001205) and the genomic neighbourhood of the AlupPKS1‐encoding gene in Aspergillus luppii. The gene clusters and pairwise amino acid sequence identities between the encoded proteins were visualised using gggenomes.[ 57 ]
Figure 6 Genomic neighbourhood of the fungal T3PKSs. a) Comparison of the scirpilin biosynthetic gene cluster in Fusarium scirpi (GenBank: OR551244) and the genomic neighbourhood of the XylPKS‐encoding gene in Xylariaceae sp. FL1272. b) Comparison of soppiline biosynthetic gene cluster in Penicillium soppii (MIBiG[ 56 ]: BGC0003032) and the genomic neighbourhood of the AlupPKS2‐encoding gene in Aspergillus luppii. c) Comparison of the communesin biosynthetic gene cluster in Penicillium expansum (MIBiG: BGC0001205) and the genomic neighbourhood of the AlupPKS1‐encoding gene in Aspergillus luppii. The gene clusters and pairwise amino acid sequence identities between the encoded proteins were visualised using gggenomes.[ 57 ]CC BY · PubMed Central

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Metadata

Authors
Sokolova, N., Denisov, S. S., Hackl, T., & Haslinger, K.
Affiliations
  • University of Groningen (NL)
  • University of Vienna (AT)
Corresponding author
Haslinger, Kristina
ORCID
Venue
Angewandte Chemie International Edition
Publisher
openRxiv
Published
2024-11-25
Volume
64
Issue
44
Pages
e202514786-e202514786
ISSN
1433-7851 (ISSN-L)
DOI
10.1101/2024.11.25.625244
PMC
PMC12559476
OpenAlex
W4413995573
Cited by
9
References
1
Citations by year
2026: 5 · 2025: 3 · 2024: 1
FWCI
4.52
Type
journal article
Language
en
Open access
Licence
Locations
  • Angewandte Chemie International Edition (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • European Commission (HORIZON2020, 893122)
  • Rijksuniversiteit Groningen
  • Universitair Medisch Centrum Groningen
  • HORIZON EUROPE Marie Sklodowska-Curie Actions (101152560)
Topics
  • Life Sciences › Immunology and Microbiology › Applied Microbiology and Biotechnology › Microbial Natural Products and Biosynthesis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Microbial Metabolism and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biotechnology › Enzyme Production and Characterization
Keywords
type III polyketide synthase, fungal secondary metabolites, substrate specificity prediction, machine learning, active site structure, non-natural substrates, promiscuous enzymes, cell-free expression
MeSH terms
Machine Learning, Fungi, Substrate Specificity, Catalytic Domain, Polyketide Synthases

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Sokolova, N., Denisov, S. S., Hackl, T., & Haslinger, K. (2024). Unravelling the functional diversity of type III polyketide synthases in fungi. Angewandte Chemie International Edition. https://doi.org/10.1101/2024.11.25.625244

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