Biology subjects

Terlouw, B.

Publications and source records attributed to Terlouw, B..

3 recordsLinked to original sources

bioRxiv2026

Systematic engineering and machine learning analysis of intrinsic terminators reveal crucial nucleotides directly upstream of the terminator hairpin.

Transcriptional termination efficiency is considered an important parameter for finetuning bacterial gene expression. Still, the design principles that determine transcription termination efficiency remain poorly understood. In this study, we aimed to investigate the impact of the 3 untranslated region (3UTR) on gene expression in Escherichia coli and other bacteria. First, 3UTR variant sequences were generated, with randomized 30 bp sequences inserted between the STOP-codon and an intrinsic terminator, consisting of a GC-rich hairpin and a downstream poly(U)-tail. Using three reporter genes, it was found that different 3UTR sequences resulted in an up to five-fold difference in protein production, independent of the upstream coding sequence. The highest protein production was achieved when an adenosine was present directly upstream of the terminator hairpin. This was consolidated by systematic substitution of key nucleotides of the terminator and assessing their effect on mRNA and protein levels. Subsequently, we developed a predictive random forest machine learning model trained on the termination efficiency of different natural and synthetic terminator sequences, revealing an important role for the nucleotides directly upstream of the terminator hairpin. Altogether, this study showed that an additional adenosine nucleotide upstream of the terminator hairpin leads to improved protein production while reducing terminator read-through. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=74 SRC="FIGDIR/small/736697v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@d8a976org.highwire.dtl.DTLVardef@5d8269org.highwire.dtl.DTLVardef@11cc3e3org.highwire.dtl.DTLVardef@180a305_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2026

RetroMol: Parsing a shared encoding from natural products and their biosynthetic gene clusters

Natural products such as polyketides and nonribosomal peptides (NRPs) are important sources of bioactive compounds, including many antibiotics. Many of them are assembled by modular enzyme complexes and further modified and diversified by tailoring reactions encoded by biosynthetic gene clusters (BGCs). Although natural products and their coding BGCs describe different data modalities of the same biochemical process, a unified language to jointly describe their biochemistry is lacking. Here we introduce a sequence-based representation of the core biosynthesis of modular natural products, which we call primary sequences, that bridges chemical structures and BGCs. We also present RetroMol, an algorithm that parses either natural product structures or their encoding BGCs into their primary sequences of natural product building blocks. RetroMol allows for similarity scoring between natural products and BGCs, enabling the retrieval of compounds, BGCs, and a combination of the two, based on their biosynthetic similarity. This can, for instance, be used to retrieve biosynthetically similar but structurally dissimilar compounds, or link natural products to candidate coding BGCs in large experimental datasets. We demonstrate the latter by rediscovering the nocardichelin B BGC as a proof of principle. We also exemplify the utility of biosynthetic similarity by showing various pairs of biosynthetically similar compounds with low structural similarity. Together, these results establish primary sequences as a shared biosynthetic encoding for natural product comparison and BGC prioritization.

bioinformatics↗

bioRxiv2022

Revealing determinants of translation efficiency via whole-gene codon randomisation and machine learning

Codon usage refers to the occurrence of synonymous codons in protein-coding genes. It is known for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we developed a method to generate a large, synonymous codon library of the gene encoding the red fluorescent protein (RFP). After expression in Escherichia coli, 1459 clones of this library were selected of which we measured protein production levels and determined the full coding sequences. Using different machine learning approaches, this data was used to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model, which only relies on the sequence information for the first 8 codons. This study clearly demonstrated the key role of codons at the start of the coding sequence. As such, it provides not only important fundamental insights on the influence of codon usage on protein production but also relevant clues on optimising the design of efficiently translated synthetic genes.

molecular biology↗