Dick de Ridder

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Dick de Ridder

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Science: A way of finding things out and then making them work. Science explains what is happening around us the whole time. So does Religion, but Science is better because it comes up with more understandable excuses when it's wrong. There is a lot more Science than you think.

From 'A Scientific Encyclopedia for the Enquiring Young Nome'
by Angalo de Haberdasheri ('Wings', Terry Pratchett)

I was a postdoc, assistant and associate professor in the Delft Bioinformatics Lab in 2003-2013. As of November 1, 2013, I have moved to Wageningen University & Research centre. With a background is in pattern recognition / machine learning, my research goal is to develop adaptive algorithms and models for molecular biology, primarily based on high-throughput measurement data and available prior knowledge.


For an overview of software developed recently in our group, please see the software page.

During my PhD, I maintained a C-library for neural networks and pattern recognition algorithms, SPRLIB/ANNLIB. I was also involved in the maintenance of a Matlab pattern recognition toolbox, PRTools, developed by Bob Duin. The latter was used throughout the book Classification, parameter estimation and state estimation: an engineering approach using Matlab (F. van der Heijden, R.P.W. Duin, D. de Ridder and D.M.J. Tax, John Wiley & Sons, 2004).

Research Highlights

Laboratory evolution yields novel lactate transporters and aneuploidy

Laboratory evolution is a powerful approach in applied and fundamental yeast research, but complete elucidation of the molecular basis of evolved phenotypes remains a challenge. In this study, DNA microarray-based transcriptome analysis and whole-genome resequencing were used to investigate evolution of novel lactate transporters in Saccharomyces cerevisiae that can replace Jen1p, the only documented S. cerevisiae lactate transporter. To this end, a jen1Δ mutant was evolved for growth on lactate in serial batch cultures. Single-nucleotide changes were found in the acetate transporter gene ADY2, which were confirmed to mutate ADY2 into an efficient lactate transporter. Due to the strong selective advantage of having more copies of this novel lactate transporter, its gene became triplicated by formation of a novel isochromome III, carrying two additional ADY2 copies.

The genome sequence of a yeast for modern industrial biotechnology

Saccharomyces cerevisiae CEN.PK 113-7D is widely used for metabolic engineering and systems biology research in industry and academia. We sequenced, assembled, annotated and analyzed its genome. Single-nucleotide variations (SNV), insertions/deletions (indels) and differences in genome organization compared to the reference strain S. cerevisiae S288C were analyzed. In addition to a few large deletions and duplications, nearly 3000 indels were identified in the CEN.PK113-7D genome relative to S288C. These differences were overrepresented in genes whose functions are related to transcriptional regulation and chromatin remodelling. Some of these variations were caused by unstable tandem repeats, suggesting an innate evolvability of the corresponding genes. Besides a previously characterized mutation in adenylate cyclase, the CEN.PK113-7D genome sequence revealed a significant enrichment of non-synonymous mutations in genes encoding for components of the cAMP signalling pathway. Some phenotypic characteristics of the CEN.PK113-7D strains were explained by the presence of additional specific metabolic genes relative to S288C. In particular, the presence of the BIO1 and BIO6 genes correlated with a biotin prototrophy of CEN.PK113-7D. Furthermore, the copy number, chromosomal location and sequences of the MAL loci were resolved. The assembled sequence reveals that CEN.PK113-7D has a mosaic genome that combines characteristics of laboratory strains and wild-industrial strains.

Predicting protein secretion success

The cell-factory Aspergillus niger is widely used for industrial enzyme production. Selecting enzymes for large-scale production requires costly lab work to test for successful high-level secretion of the over-expressed enzyme. To reduce the amount of lab work, we developed a sequence-based classifier that predicts successful high-level secretion of homologous proteins. This enables the selection of a subset of potential enzymes out of a large set of enzymes. A dataset of 638 proteins was used to train and validate a classifier, using a 10-fold cross-validation protocol. Using a linear discriminant classifier, an average accuracy of 0.85 was achieved, which in practice could lead to half the amount of lab work. Feature selection results indicate what features are mostly defining for successful protein production, which could be an interesting lead to couple sequence characteristics to biological processes involved in protein production and secretion.

MAIA: Integrating genome assemblies

De novo assembly of a eukaryotic genome with next-generation sequencing data is still a challenging task. Over the past few years several assemblers have been developed, often suitable for one specific type of sequencing data. The number of known genomes is expanding rapidly, therefore it becomes possible to use multiple reference genomes for assembly projects. We introduce an assembly integrator that makes use of all available data, i.e. multiple de novo assemblies and mappings against multiple related genomes, by optimizing a weighted combination of criteria. The developed algorithm was applied on the de novo sequencing of the Saccharomyces cerevisiae CEN.PK 113-7D strain. Using Solexa and 454 read data, two de novo and three comparative assemblies were constructed and subsequently integrated, yielding 29 contigs, covering more than 12 Mbp; a drastic improvement compared with the single assemblies.

Modeling enzyme aspecificity

Many enzymes are aspecific, or even promiscuous: they can catalyze transformations of more metabolites than the traditional ones as listed in e.g. KEGG. This information is currently only available in databases, such as the BRENDA enzyme activity database. We have developed a method to model enzyme aspecificity, by predicting whether an input compound is likely to be transformed by a certain enzyme. This system, called MaRIboES (metabolite and reaction inference based on enzyme specificities), has many applications, for example to complete reconstructed metabolic networks, to aid in metabolic engineering or to help identify unknown peaks in mass spectra. MaRIboES employs structural and stereochemistry similarity measures and molecular fingerprints to generalise enzymatic reactions based on data available in BRENDA. Leave-one-out cross-validation shows that 80% of known reactions are predicted well. Application to the yeast glycolytic and pentose phosphate pathways predicts a large number of known and new reactions, often leading to the formation of novel compounds, as well as a number of interesting bypasses and cross-links.

People involved

Dick de Ridder