Generative model design, as well as protein binder design targeting peptide–MHCs for CAR-T therapies, snake toxins, as well as design of enzyme inhibitors for bioindustrial and biomedical applications, supported by automated closed-loop experimental validation.
People
Center Leader
Timothy Patrick Jenkins Associate Professor Department of Biotechnology and Biomedicine tpaje@dtu.dk
Alexander Kai Büll Professor Department of Biotechnology and Biomedicine alebu@dtu.dk
Designing binders against intrinsically disordered peptides implicated in neurodegenerative diseases such as Alzheimer’s and Parkinson’s. His group will use generative models to create stabilizing binders that modulate aggregation pathways, opening new avenues for therapeutic intervention in protein misfolding disorders.
Birgitte Zeuner Associate Professor Department of Biotechnology and Biomedicine bzeu@dtu.dk
Engineering carbohydrate-active enzymes (CAZymes) for industrial applications, requiring GPU-driven modelling of large enzyme families and their substrate interactions to enable rational and AI-guided engineering.
Carlos G. Acevedo-Rocha Senior Researcher Biotechnology Research Institute for the Green Transition cargac@dtu.dk
De novo enzyme design and metabolic engineering, applying generative protein models to create enzymes with multiple properties including novel catalytic activities for the sustainable biosynthesis of industrial chemicals, biomaterials and natural products.
Carolina Barra Quaglia Associate Professor Department of Health Technology carolet@dtu.dk
Developing attention-based machine-learning and computer-vision methods for computational immunology. The group integrates protein sequence, structure, immunopeptidomics, and image-derived data to predict antigen presentation, immune recognition, protein immunogenicity, and allergenicity, with applications in allergy, autoimmunity, and protein safety assessment.
Chiara Francavilla Associate Professor Department of Biotechnology and Biomedicine chiafra@dtu.dk
Studying Receptor Tyrosine Kinases (RTKs) signalling by quantitative omics in bulk and single cells using epithelial cells (healthy and from cancer) and designing isoform-specific mini binders against RTKs to probe molecular switches in RTK signaling and investigating therapeutic resistance mechanisms.
Eli Nathan Weinstein Assistant Professor Department of Chemistry enawe@kemi.dtu.dk
Fundamental machine learning methodology for molecules, including generative and probabilistic techniques for steering chemical synthesis and high-throughput assays, and for learning from laboratory and clinical data.
Erwin Schoof Professor Department of Biotechnology and Biomedicine erws@dtu.dk
Building on his recent Science paper integrating single-cell proteomics and transcriptomics, protein design unlocks a new frontier in biomarkers.
Esperanza Rivera de Torre Associate Professor erdto@dtu.dk
Allergy immunotherapy and diagnostics, developing de novo designed binders to allergens and immune receptors for precision diagnostics, control of allergens in production lines, and safer therapeutic strategies.
Janine Sophie Kemming Assistant Professor Department of Health Technology jsoke@dtu.dk
De novo binders to inhibit inflammatory enzymes and promote wound healing, identification of pathogenic T cells in autoimmunity to enable targeted de novo binder-based therapies.
Jes Frellsen Associate Professor Department of Applied Mathematics and Computer Science jefr@dtu.dk
Generative and probabilistic machine learning methods and their applications to protein design and biological sequence modelling. Current work includes antibody design, protein stability and isoform analysis, as well as AI-based approaches for sequence annotation and the safety of protein therapeutics.
Katrine Qvortrup Professor Department of Chemistry kaqvo@kemi.dtu.dk
De novo design of binders to facilitate blood-brain-barrier crossing of biologics.
Konstantinos Kalogeropoulos Assistant Professor Department of Biotechnology and Biomedicine konka@dtu.dk
Development of de novo designed selective inhibitors against matrix metalloproteinases (MMPs), bona fide targets in inflammatory diseases and cancer. Additional proteases such as coagulation factors and kallikreins for coagulopathies and skin diseases. Combining computational design with experimental degradomics pipelines to generate and validate highly specific inhibitors. Design of novel proteases with improved catalytic efficiency and stability for translational biomedical and industrial applications, and probes for visualizing protease activity in living tissues.
Kristoffer Haurum Johansen Assistant Professor Department of Health Technology krisha@dtu.dk
Advancing synthetic immunology by combining functional genomics with de novo protein design to develop novel precision cancer immunotherapies. This research integrates CRISPR/Cas9 pooled functional screens with the generative design of binders to improve T cell efficacy for immunomodulation.
Morten Nielsen Groupleader, Professor Department of Health Technology morni@dtu.dk
Novel pattern recognition algorithms aimed at learning the rules to define and allow for prediction of the cognate target (epitopes) and specificity of central immune receptor molecules including MHC, and T cell and B cell receptors. This to support the development of next generation vaccines and immunotherapies.
Sine Reker Hadrup Head of Section, Professor Department of Health Technology sirha@dtu.dk
T-cell immunology and immunogenomics, where GPU-intensive pipelines are required for large-scale single-cell immune profiling and binder prediction to support next-generation cancer immunotherapies.