ArticleAngewandte Chemie (International ed. in English)2025
Unravelling the Functional Diversity of Type III Polyketide Synthases in Fungi.
Article in Angewandte Chemie (International ed. in English), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- The Applications of Machine Learning in Micro-Nano Materials Research: From High-Throughput Screening to Intelligent Design.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Enzymatic synthesis of bioactive quinolones and (thio)coumarins by fungal type III polyketide synthases.The FEBS journal · 2026Article
- Benchmarking methods for measuring biosynthetic gene cluster similarity and determination of gene cluster families.Bioinformatics (Oxford, England) · 2025Article
- Unravelling the Functional Diversity of Type III Polyketide Synthases in Fungi.Angewandte Chemie (International ed. in English) · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Type III polyketide synthases (T3PKSs) are enzymes that produce diverse compounds of ecological and clinical importance. While well-studied in plants, 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. In addition, we applied the model to identify two more promiscuous T3PKSs from fungi. We anticipate that the ML 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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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.