Phone: +46(0)31 772 8171
E-mail: martin.engqvist [at] chalmers.se
Office: Chemistry building, Room 2024
Our research focuses on bioinformatics and experimental high-throughput biochemical characterization of metabolic enzymes. We aim to understand how diverse protein sequences determine enzyme function.
Genome sequence data is growing at an explosive rate while experimental data relating to gene function is only growing slowly. The gap between what we know and what we do not know is increasing. The research community must greatly increase the body of experimental data to achieve accurate functional annotation of genes in current and future sequenced genomes, thereby unlocking their full value. A new generation of high-throughput experimental platforms are needed to provide this data.
Combining ideas and expertise from bioinformatics, biochemistry and directed evolution we operate a platform for high-throughput biochemical characterization of enzymes. It is a novel way of applying well-established methods towards a new purpose. We make use of these methods to densely sample sequence diversity in enzyme families. The resulting data is leveraged to gain insights into the sequence-function relationship in metabolic enzymes and forms a basis for improved functional gene annotation in sequenced genomes.
As of August 2018 (Google Scholar)
ϕ Indicates shared first-authorship as noted in publications .
“Correlating enzyme annotations with a large set of microbial growth temperatures reveals metabolic adaptations to growth at diverse temperatures” .
Peer-reviewed original articles
ϕ Indicates shared first-authorship as noted in publications
“Ribonucleotides incorporated by the yeast mitochondrial DNA polymerase are not repaired”
Wanrooij PH, Engqvist MKM, Forslund JME, Navarrete C, Nilsson AK, Sedman J, Wanrooij S, Clausen AR, Chabes A.
Proceedings of the National Academy of Sciences, Nov 7 (2017)
“Simultaneous Mapping and Quantitation of Ribonucleotides in Human Mitochondrial DNA”
Kreisel, K, Engqvist, MKM, Clausen, AR.
Journal of Visualized Experiments, e56551 (2017).
“Nucleotide pools dictate the identity and frequency of ribonucleotide incorporation in mitochondrial DNA”
Berglund AKϕ, Navarrete Cϕ, Engqvist MKM, Hoberg E, Szilagyi Z, Taylor RW, Gustafsson CM, Falkenberg M and Clausen AR.
PLOS Genetics, 13(2): e1006628 (2017).
“Highlighting the need for systems-level experimental characterization of plant metabolic enzymes”
Frontiers in Plant Science, 7: 1127 (2016).
“The influence of alternative pathways of respiration that utilize branched-chain amino acids following water shortage in Arabidopsis”
Pires MV, Júnior AA, Medeiros DB, Daloso DM, Pham PA, Barros KA, Engqvist MKM, Florian A, Krahnert I, Maurino VG, Araújo WL, Fernie AR.
Plant, Cell & Environment, 39 (6): 1304-19 (2016).
“ANT: software for generating and evaluating degenerate codons for natural and expanded genetic codes”
Engqvist MKM, Nielsen J.
ACS Synthetic Biology, 4 (8): 935–938 (2015).
“GLYCOLATE OXIDASE3, a Glycolate Oxidase Homolog of Yeast l-Lactate Cytochrome c Oxidoreductase, Supports l-Lactate Oxidation in Roots of Arabidopsis”
Engqvist MKMϕ, Schmitz Jϕ, Gertzmann Aϕ, Florian A, Jaspert N, Arif M, Balazadeh S, Mueller-Roeber B, Fernie AR, Maurino VG.
Plant Physiology, 169 (2): 1042-1061 (2015).
“Plants Possess a Cyclic Mitochondrial Metabolic Pathway similar to the Mammalian Metabolic Repair Mechanism Involving Malate Dehydrogenase and l-2-Hydroxyglutarate Dehydrogenase”
Hüdig M, Maier A, Scherrers I, Seidel L, Jansen EEW, Mettler-Altmann T, Engqvist MKM, Maurino VG.
Plant and Cell Physiology, 56 (9): 1820-1830 (2015).
“Adaptive mutations in sugar metabolism restore growth on glucose in a pyruvate decarboxylase negative yeast strain”
Zhang Y, Liu G, Engqvist MKM, Krivoruchko A, Hallström BM, Chen Y, Siewers V, Nielsen J.
Microbial cell factories, 14 (1): 116 (2015).
“Directed Evolution of a Far-Red Fluorescent Rhodopsin”
McIsaac RSϕ,** Engqvist MKM**ϕ, Wannier T, Rosenthal AZ, Herwig L, Flytzanis NC, Imasheva ES, Lanyi JK, Balashov SP, Gradinaru V and Arnold FH.
Proceedings of the National Academy of Sciences, 111 (36): 13034-13039 (2015).
“Archaerhodopsin variants with enhanced voltage-sensitive fluorescence in mammalian and Caenorhabditis elegans neurons”
Flytzanis NCϕ, Bedbrook CNϕ, Chiu H,** Engqvist MKM**, Xiao C, Chan KY, Sternberg PW, Arnold FH, Gradinaru V.
Nature Communications, 5(4894) (2014).
“Directed Evolution of Gloeobacter violaceus Rhodopsin Spectral Properties”
Engqvist MKM, McIsaac RS, Dollinger P, Flytzanis NC, Abrams M, Schor S and Arnold FH.
Journal of Molecular Biology, 427 (1): 205-220 (2014).
“D-2-hydroxyglutarate metabolism is linked to photorespiration in the shm1-1 mutant”
Kuhn Aϕ,** Engqvist MKM**ϕ, Jansen EEW, Jakobs C, Weber APM and Maurino VG.
Plant Biology, 15(4): 776-84 (2013).
“Transgenic Introduction of a Glycolate Oxidative Cycle into A. thaliana Chloroplasts Leads to Growth Improvement”
Maier A, Fahnenstich H, von Caemmerer S,** Engqvist MKM**, Weber APM, Flügge UI and Maurino VG.
Frontiers in Plant Science, 3(February): 1-12 (2012).
“D-Lactate dehydrogenase as a marker gene allows positive selection of transgenic plants”
Wienstroer Jϕ, Engqvist MKMϕ, Kunz HHϕ, Flügge UI and Maurino VG.
FEBS Letters, 586(1): 36-40 (2012).
“Plant D-2-hydroxyglutarate dehydrogenase participates in the catabolism of lysine especially during senescence”
Engqvist MKMϕ, Kuhn Aϕ, Wienstroer J, Weber K, Jansen EE, Jakobs C, Weber APM and Maurino VG.
Journal of Biological Chemistry, 286(13): 11382-90 (2011).
“Two D-2-hydroxyacid dehydrogenases in Arabidopsis thaliana with catalytic capacities to participate in the last reactions of the methylglyoxal and β-oxidation pathways”
Engqvist M, Drincovich MF, Flügge UI and Maurino VG.
Journal of Biological Chemistry, 284(37): 25026-37 (2009).
“HAG2/MYB76 and HAG3/MYB29 exert a specific and coordinated control on the regulation of aliphatic glucosinolate biosynthesis in Arabidopsis thaliana”
Gigolashvili Tϕ, Engqvist Mϕ, Yatusevich R, Müller C, Flügge UI.
New Phytologist 177 (3), 627-642 160 (2008)
“Effect of poly (ethylene glycol) on enzymatic hydrolysis and adsorption of cellulase enzymes to pretreated lignocellulose”
Börjesson J, Engqvist M, Sipos B, Tjerneld F.
Enzyme and Microbial Technology 41 (1), 186-195 (2007)
Peer-reviewed research review articles
“2-Hydroxy Acids in Plant Metabolism”
Maurino VG, Engqvist MKM.
In The Arabidopsis Book, Sep 4, 13:e0182 (2015).
“Mitochondrial 2-hydroxyglutarate metabolism”
Engqvist MKM, Eßer C, Maier A, Lercher MJ and Maurino VG.
Mitochondrion. 19: 275-281 (2014).
“Metabolic engineering of photorespiration - bypass approaches”
Engqvist MKM, Maurino VG.
In Methods in Molecular Biology: Photorespiration (eds. Fernie AR, Weber A, Bauwe H) 1653:137-155 (2017)
Ambiguous Nucleotide Tool (ANT)
Degenerate codons are used to represent DNA positions that have multiple possible nucleotide alternatives. This is useful for protein engineering and directed evolution, where primers specified with degenerate codons are used as a basis for generating libraries of protein sequences. ANT provides Python code for generating and evaluating degenerate codons for natural and expanded genetic codes.
Manually editing a large number of plasmid files, to annotate them with mutations discovered in the lab or other features, is tedious and error-prone. DNApy provides Python code for DNA and plasmid editing. It allows for batch in silico cloning of an unlimited number of sequences from FASTA files as well as the batch introduction and annotation of mutations.
Making accurate color scales for communicating data in visualizations is critical. In many presentations and publications unbalanced scales are used. A common error is to use color scales which does not have smooth and constant change in lightness, leading to difficulty in interpreting the visualized data. colcol provides Python code to deal with color conversions as well as with generating publication-quality color scales.
Many good plotting libraries exist for Python, but sometimes it is necessary to generate custom vector art visualizations of data. wsvg is a light Python wrapper around the svg (scalable vector graphics) format, enabling scripting of svg files.
Computational analysis of data in a research setting is often difficult to reproduce due to poor documentation of steps taken and software used. Determining which version of what script was used to process a raw data file to generate publishable results can be challenging. As ever-increasing amounts of data is being generated in biological research it is critical that this problem is addressed and that computational analysis becomes as reproducible as laboratory experiments. The reproducible research repository provides a folder structure and conventions aimed at achieving such reproducibility. Note: This repository was originally created by others and made public here. The Engqvist Lab version has been modified from this source to fit our own research needs.
Scientific writing should not be bound to any given proprietary software or file format, especially one which is ill-suited for handling complex texts and large figures. Unfortunately many scientific publishers have built their publication pipelines around such software. An alternative in many journals is to submit LaTeX documents, by this is intimidating to most users. Pandoc offers a great alternative. By writing your documents using markdown Pandoc can convert that text into a whole range of common file formats, including LaTeX, pdf, doc, and others. The reproducible reports repository sets up a folder structure and utilities for using Pandoc to write reports.