Biology subjects

Calounova, T.

Publications and source records attributed to Calounova, T..

3 recordsLinked to original sources

bioRxiv2025

Ovo, an Open-Source Ecosystem for De Novo Protein Design

The protein design field is rapidly advancing, with frequent emergence of new models and pipelines for designing de novo proteins with tailored properties and functions not found in nature. However, the current tool landscape is fragmented, tools are hard to install and deploy, and require significant computational expertise to integrate into end-to-end, scalable pipelines. A particular challenge is managing many sequences, structures, and metrics for downstream testing and retrospective analysis of input parameters. To address this need, we introduce Ovo, an open-source de novo protein design ecosystem that consolidates models, workflows, data management, and interactive visualization into a scalable, infrastructure-agnostic platform. Ovo features Nextflow-based workflow orchestration, a storage layer, and both command-line and graphical interfaces that democratize scaffold design, binder design and diversification, and validation workflows. Ovos novel ProteinQC module computes comprehensive sequence and structure descriptors, contextualizing designs against reference sets. Ovo plugins let the community add new workflows and user interfaces to accelerate adoption of emerging methods and facilitate community-driven benchmarking. Ovo lowers engineering barriers and demystifies the design process, allowing experts and non-technical users to design proteins at scale. With community-driven development, Ovo can accelerate de novo protein design and advance discovery in therapeutics and biotechnology.

bioinformatics↗

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

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↗