Biology subjects

Beekwilder, J.

Publications and source records attributed to Beekwilder, J..

3 recordsLinked to original sources

bioRxiv2025

Rhodo-Box: a Synthetic Biology Toolbox to Facilitate Metabolic Engineering of Rhodobacter sphaeroides

Rhodobacter sphaeroides is a purple non-sulphur alphaproteobacterium with a highly versatile metabolism. This microorganism holds promise as a chassis for sustainable biomanufacturing of numerous chemicals. Yet, its potential is constrained by a lack of standardized, well-characterized genetic elements to tune gene expression such as transcriptional promoters and ribosome binding sites (RBSs). In this study, we present Rhodo-Box, a comprehensive toolkit for R. sphaeroides created by adapting and extending the Zymo-Parts modular cloning framework. Using Rhodo-Box we built and characterized: (a) three broad-host origins of replication (pBBR1, RK2 and RSF1010), (b) a set of 13 promoters, (c) four inducible expression systems (NahR-PsalTTC, LacI-PlacT7A1_O3O4, VanR-PvanCC, and XylS-Pm), (d) 11 RBSs, and (e) four transcriptional terminators. Furthermore, we present a semi-automated, user-friendly cloning approach which enables rapid construction of R. sphaeroides strains. The Rhodo-Box toolkit equips R. sphaeroides with a standardized, automation-compatible collection of parts and workflows essential for efficient design-build-test-learn cycles and advanced metabolic engineering. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=106 SRC="FIGDIR/small/685836v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@fcb172org.highwire.dtl.DTLVardef@1e5eb44org.highwire.dtl.DTLVardef@1b8df28org.highwire.dtl.DTLVardef@42eb2a_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

bioRxiv2020

Integrating structure-based machine learning and co-evolution to investigate specificity in plant sesquiterpene synthases

Sesquiterpene synthases (STSs) catalyze the formation of a large class of plant volatiles called sesquiterpenes. While thousands of putative STS sequences from diverse plant species are available, only a small number of them have been functionally characterized. Sequence identity-based screening for desired enzymes, often used in biotechnological applications, is difficult to apply here as STS sequence similarity is strongly affected by species. This calls for more sophisticated computational methods for functionality prediction. We investigate the specificity of precursor cation formation in these elusive enzymes. By inspecting multi-product STSs, we demonstrate that STSs have a strong selectivity towards one precursor cation. We use a machine learning approach combining sequence and structure information to accurately predict precursor cation specificity for STSs across all plant species. We combine this with a co-evolutionary analysis on the wealth of uncharacterized putative STS sequences, to pinpoint residues and distant functional contacts influencing cation formation and reaction pathway selection. These structural factors can be used to predict and engineer enzymes with specific functions, as we demonstrate by predicting and characterizing two novel STSs from Citrus bergamia. Author summaryPredicting enzyme function is a popular problem in the bioinformatics field that grows more pressing with the increase in protein sequences, and more attainable with the increase in experimentally characterized enzymes. Terpenes and terpenoids form the largest classes of natural products and find use in many drugs, flavouring agents, and perfumes. Terpene synthases catalyze the biosynthesis of terpenes via multiple cyclizations and carbocation rearrangements, generating a vast array of product skeletons. In this work, we present a three-pronged computational approach to predict carbocation specificity in sesquiterpene synthases, a subset of terpene synthases with one of the highest diversities of products. Using homology modelling, machine learning and co-evolutionary analysis, our approach combines sparse structural data, large amounts of uncharacterized sequence data, and the current set of experimentally characterized enzymes to provide insight into residues and structural regions that likely play a role in determining product specifcity. Similar techniques can be repurposed for function prediction and enzyme engineering in many other classes of enzymes.

bioinformatics↗

bioRxiv2018

An Analysis of Characterized Plant Sesquiterpene Synthases

Plants exhibit a vast array of sesquiterpenes, C15 hydrocarbons which often function as herbivore-repellents or pollinator-attractants. These in turn are produced by a diverse range of sesquiterpene synthases. A comprehensive analysis of these enzymes in terms of product specificity has been hampered by the lack of a centralized resource of sufficient functionally annotated sequence data. To address this, we have gathered 262 plant sesquiterpene synthase sequences with experimentally characterized products. The annotated enzyme sequences allowed for an analysis of terpene synthase motifs, leading to the extension of one motif and recognition of a variant of another. In addition, putative terpene synthase sequences were obtained from various resources and compared with the annotated sesquiterpene synthases. This analysis indicated regions of terpene synthase sequence space which so far are unexplored experimentally. Finally, we present a case describing mutational studies on residues altering product specificity, for which we analyzed conservation in our database. This demonstrates an application of our database in choosing likely-functional residues for mutagenesis studies aimed at understanding or changing sesquiterpene synthase product specificity.

bioinformatics↗