Biology subjects

Perkovic, M.

Publications and source records attributed to Perkovic, M..

3 recordsLinked to original sources

bioRxiv2025

MARTS-DB: A Database of Mechanisms And Reactions of Terpene Synthases

BackgroundTerpene synthases (TPSs) are enzymes that catalyze some of the most complex reactions in nature - the cyclizations of terpenes, which form the carbon backbones to the largest group of natural products, the terpenoids. On average, more than half of the carbon atoms in a terpene scaffold undergo a change in connectivity or configuration during these enzymatic cascades. Understanding TPS reaction mechanisms remains challenging, often requiring intricate computational modeling and isotopic labelling studies. Moreover, the relationship between TPS sequence and catalytic function is difficult to decipher, while data-driven approaches remain limited due to a lack of comprehensive, high-quality data sources. MainWe introduce the Mechanisms And Reactions of Terpene Synthases DataBase (MARTS-DB) - a manually curated, structured, and searchable database that integrates TPS enzymes, the terpenes they produce, and their detailed reaction mechanisms. MARTS-DB includes over 2,600 reactions catalyzed by 1,334 annotated enzymes from across all domains of life, with reaction mechanisms mapped as stepwise cascades for more than 400 terpenes. Accessible at https://www.marts-db.org, the database provides advanced search functionality and supports full dataset downloads in machine-readable formats. It also encourages community contributions to promote continuous growth. ConclusionUser-friendly and comprehensive, MARTS-DB enables the systematic exploration of TPS catalysis, opening new avenues for computational analysis and machine learning, as recently demonstrated in the prediction of novel TPSs.

bioinformatics↗

bioRxiv2024

Remarkable diversity of alkaloid scaffolds in Piper fimbriulatum

Plant specialized metabolites play key roles in diverse physiological processes and ecological interactions. Identifying structurally novel metabolites, as well as discovering known compounds in new species, is often crucial for answering broader biological questions. The Piper genus (Piperaceae family) is known for its special phytochemistry and has been extensively studied over the past decades. Here, we investigated the alkaloid diversity of Piper fimbriulatum, a myrmecophytic plant native to Central America, using a metabolomics workflow that combines untargeted LC-MS/MS analysis with a range of recently-developed computational tools. Specifically, we leverage open MS/MS spectral libraries and metabolomics data repositories for metabolite annotation, guiding isolation efforts towards structurally-new compounds (i.e., dereplication). As a result, we identified several alkaloids belonging to 5 different classes and isolated one novel seco-benzylisoquinoline alkaloid featuring a linear quaternary amine moiety that we named fimbriulatumine. Notably, many of the identified compounds were never reported in Piperaceae plants. Our findings expand the known alkaloid diversity of this family, and demonstrate the value of revisiting well-studied plant families using state-of-the-art computational metabolomics workflows to uncover previously overlooked chemodiversity. To contextualize our findings into a broader biological context, we employed a workflow for automated mining of literature reports of the identified alkaloid scaffolds and mapped the results onto the angiosperm tree of life. By doing so, we highlight the remarkable alkaloid diversity within the Piper genus and provide a framework for generating hypotheses on the biosynthetic evolution of these specialized metabolites. Many of the computational tools and data resources used in this study remain underutilized within the plant science community. This manuscript demonstrates their potential through a practical application and aims to promote broader accessibility to untargeted metabolomics approaches. Significance StatementWe combine untargeted metabolomics with a range of recently developed computational tools to uncover a previously overlooked diversity of alkaloid scaffolds in Piper fimbriulatum. Our findings demonstrate the potential of revisiting well-studied plant families using state-of-the-art computational metabolomics workflows to uncover previously overlooked chemodiversity.

plant biology↗

bioRxiv2024

Discovery and Characterization of Terpene Synthases Powered by Machine Learning

The exponential growth of uncharacterized enzyme sequences in genomic repositories demands novel tools for functional annotation. Here, we combined alignment-driven structural domain analysis with protein language models to create EnzymeExplorer, a machine-learning pipeline for enzyme function prediction. We applied this approach to terpene synthases (TPSs), which present an ideal model case because they catalyze complex carbocationic rearrangements with unpredictable product outcomes. We detected new structural domains and achieved significantly higher average precision than existing methods for function prediction. By analyzing the UniRef90 database, we identified TPSs overlooked by existing computational methods. Remarkably, we discovered and experimentally confirmed three archaeal TPSs, expanding the known taxonomic distribution of TPS catalysis to a new domain of life. Further in silico screening of archaeal proteomes revealed that terpene biosynthesis is widespread across Archaea. Our approach offers a powerful framework for characterizing enzyme "dark matter" in the rapidly expanding genomic and metagenomic datasets.

biochemistry↗