Abstract: Specialized metabolites play key roles in regulating physiological processes in organisms related to growth and development and serve as communicators between organisms. Their encoded messages include cries for help, embrace yourself, as well as deadly kisses. However, only a few percent of specialized metabolites is structurally characterized, and even less are connected to their genetic machinery in the organisms producing them. Whilst technological advances made analytical chemical analysis more sensitive than ever before, solving metabolite structures from analytical data remains very difficult and was coined as one of the “Grand Challenges” in the metabolomics field.In this seminar, I will highlight recent advances in computational metabolomics made by my group and others that start to use machine learning to solve this grand challenge. I will describe the motivations and concepts of a number of metabolomics mining and annotation tools to better understand the complex metabolite mixtures that specialized metabolites are typically part of. As an early example of using machine learning in untargeted metabolomics, I will present the tandem of MS2LDA and MotifDB (www.ms2lda.org) for substructure discovery and annotation in metabolomics data. More recently, my group proposed Spec2Vec and MS2DeepScore, novel machine learning-based mass spectral similarity scores that improve library matching and analogue searching and that formed the basis for the also machine learning-based MS2Query analogue search tool.
I will finish off with my perspective on integrating  genome and metabolome mining workflows to accelerate specialized metabolite discovery and their structural and functional characterization, as well as a call to action: sharing is caring! I expect that the presented methodological developments will advance our understanding of the role of metabolites and their complex molecular interactions that underpin growth, development, and health.

Speaker: Justin J.J. van der Hooft is an Assistant Professor in Computational Metabolomics in the Bioinformatics Group at Wageningen University, NL, and an author of over 80 peer-reviewed articles in the metabolomics field.  Justin is very fascinated by the ingenuity of nature in creating  marvelous  chemical structures and their diverse roles in ecosystems that include inter-kingdom communication and this a main driver of his research. He obtained his MSc (2007) in Molecular Sciences (Wageningen University, NL) and his PhD (2012) at the Biochemistry and Bioscience groups in Wageningen (Wageningen University & Research, NL). After a postdoctoral period in Glasgow, UK, studying both analytical and computational aspects of metabolite structure annotation, he returned to Wageningen in 2017 to work on linking metabolome and genome mining workflows. Since he started his own group in 2020, his team has continued to develop computational metabolomics methodologies to decompose the mass spectral data of complex metabolite mixtures into structure and substructure information. By linking genome and metabolome mining, his team studies plant, food, and microbiome-associated metabolites to find novel bioactive metabolites and infer their source and function. Since 2022, he is also affiliated with the University of Johannesburg, SA, as a visiting professor. Got interested? Find out more and meet the team here: https://vdhooftcompmet.github.io.

Place of Seminar:  Otaniemi, T4 (Zoom link)