Biology subjects

Kloosterman, A. M.

Publications and source records attributed to Kloosterman, A. M..

4 recordsLinked to original sources

bioRxiv2026

A combinatorial DNA origami platform for biologically replicable, thermostable data storage and molecular authentication

DNA origami is becoming an attractive platform for data storage, yet current approaches rely on the limited stability of DNA hybridization, preventing them from fully utilizing the stability and cost-effective copying inherent to classical sequence-based storage. Here we introduce DNA Origami for Combinatorial data Storage (DOCS), where we encode information into the scaffold molecule using a combinatorial enzymatic approach. This enables text encoding that is biologically cloneable, stable at high temperatures, and randomly accessible. We further demonstrate the DOCS platforms combinatorial power by creating a stochastic molecular authentication system. Finally, we show using simulations that expanding the information capacity of data carriers allows for the storage and recovery of large files up to several hundred kilobytes in size. DOCS provides a robust, scalable strategy for molecular data storage and security that bridges the gap between classical DNA data storage strategies and DNA nanostructure-based methods.

bioengineering↗

bioRxiv2023

An error correction strategy for image reconstruction by DNA sequencing microscopy

By pairing adjacent molecules in-situ and then mapping these pairs, DNA microscopy could significantly reduce workload in spatial omics methods by directly inferring geometry from sequencing data alone. However, experimental artefacts can lead to errors in the adjacency data which distort the spatial reconstruction. Here, we describe a method to correct two such errors: spurious crosslinks formed between any two nodes, and fused nodes that are formed out of multiple molecules. We build on the principle that spatially close molecules should be connected and show that these errors violate this principle, allowing for their detection and correction. Our method corrects errors in simulated data, even in the presence of up to 20% errors, and proves to be more efficient at removing errors from experimental data than a read count filter. Integrating this method in DNA microscopy will significantly improve the accuracy of spatial reconstructions with lower data loss.

bioinformatics↗

bioRxiv2020

Integration of machine learning and pan-genomics expands the biosynthetic landscape of RiPP natural products

Most clinical drugs are based on microbial natural products, with compound classes including polyketides (PKS), non-ribosomal peptides (NRPS), fluoroquinones and ribosomally synthesized and post-translationally modified peptides (RiPPs). While variants of biosynthetic gene clusters (BGCs) for known classes of natural products are easy to identify in genome sequences, BGCs for new compound classes escape attention. In particular, evidence is accumulating that for RiPPs, subclasses known thus far may only represent the tip of an iceberg. Here, we present decRiPPter (Data-driven Exploratory Class-independent RiPP TrackER), a RiPP genome mining algorithm aimed at the discovery of novel RiPP classes. DecRiPPter combines a Support Vector Machine (SVM) that identifies candidate RiPP precursors with pan-genomic analyses to identify which of these are encoded within operon-like structures that are part of the accessory genome of a genus. Subsequently, it prioritizes such regions based on the presence of new enzymology and based on patterns of gene cluster and precursor peptide conservation across species. We then applied decRiPPter to mine 1,295 Streptomyces genomes, which led to the identification of 42 new candidate RiPP families that could not be found by existing programs. One of these was studied further and elucidated as a novel subfamily of lanthipeptides, designated Class V. Two previously unidentified modifying enzymes are proposed to create the hallmark lanthionine bridges. Taken together, our work highlights how novel natural product families can be discovered by methods going beyond sequence similarity searches to integrate multiple pathway discovery criteria. Code and data availabilityThe source code of DecRiPPter is freely available online at https://github.com/Alexamk/decRiPPter. Results of the data analysis are available online at http://www.bioinformatics.nl/~medem005/decRiPPter_strict/index.html and http://www.bioinformatics.nl/~medem005/decRiPPter_mild/index.html (for the strict and mild filters, respectively). All training data and code used to generate these, as well as outputs of the data analyses, are available on Zenodo at doi:10.5281/zenodo.3834818.

microbiology↗

bioRxiv2020

RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery

Nearly half of the classes of natural products known as ribosomally synthesized and post-translationally modified peptides (RiPPs) are reliant on a protein domain called the RiPP recognition element (RRE) for peptide maturation. The RRE binds specifically to a linear precursor peptide and directs the post-translational modification enzymes to their substrate. Given its prevalence across various types of RiPP biosynthetic gene clusters (BGCs), the RRE could theoretically be used as a bioinformatic handle to identify novel classes of RiPPs. In addition, due to the high affinity and specificity of most RRE:precursor peptide complexes, a thorough understanding of the RRE domain could be exploited for biotechnological applications. However, sequence divergence of the RRE domain across RiPP classes has precluded automated identification of RREs based solely on sequence similarity. Here, we introduce RRE-Finder, a novel tool for identifying RRE domains with high sensitivity. RRE-Finder can be used in "precision" mode to confidently identify RREs in a class-specific manner or in "exploratory" mode, which was designed to assist in the discovery of novel RiPP classes. RRE-Finder operating in precision mode on the UniProtKB protein database retrieved over 30,000 high-confidence RREs spanning all characterized RRE-dependent RiPP classes, as well as several yet-uncharacterized RiPP, putatively novel gene cluster architectures that will require future experimental work. Finally, RRE-Finder was used in precision mode to explore a possible evolutionary origin of the RRE domain. Altogether, RRE-Finder provides a powerful new method to probe RiPP biosynthetic diversity and delivers a rich dataset of RRE sequences that will provide a foundation for deeper biochemical studies into this intriguing and versatile protein domain.

bioinformatics↗