Tobias Hegelund Olsen

I am currently doing a PhD in Immunoinformatics in the Oxford Protein Informatics Group (OPIG), University of Oxford. I am funded via the EPSRC Sustainable Approaches to Biomedical Science Centre for Doctoral Training: Responsible and Reproducible Research (SABS:R3) program.

My research focuses on designing and building in silico tools for engineering immunological proteins (i.e. antibodies, T-Cell Receptors, MHCs, etc.). For this, I make use of deep learning and other data science approaches. I have experience working with humanization, antibody paratope prediction, TCR:p:MHC structure prediction and affinity prediction. I have collated, processed and worked with both structural data as well as billions of amino acid sequences. Additionally, I have worked on predicting useful bioactivities for peptides. My current work focuses on improving the developability of therapeutic antibodies by utilising large antibody sequence datasets and language models.

Published and available tools

  • KA-Search: A tool for rapid and exhaustive sequence search of billions of antibodies, enabling exploration of the mutational space of antibodies (paper).
  • AbLang: An antibody specific language model for therapeutic antibody engineering (paper).
  • OAS: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences (paper).
  • AnOxPePred: Prediction of antioxidative properties of peptides using deep learning (paper).
  • proABC-2: PRediction of AntiBody contacts v2 and its application to information-driven docking. (paper).
  • TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes (paper).
    • Media

      I can be contacted on LinkedIn and Twitter @HegelundOlsen.

    • GitHub

      Any code I write, and which is public, I will try and make available on my Github.

Experience and Education

  • PhD candidate in Immunoinformatics • University of Oxford, Oct. 2019 - Present

    Investigating B-cell repertoire data using deep learning approaches to aid in the development of antibody therapeutics.
  • Research Assistant • Technical University of Denmark, Sep. 2018 - Jun. 2019

    Designing in silico tools for prediction of peptide functions, a deep learning approach for predicting the antibody paratope and de-immunogenization of proteins. Supervised by Professor Paolo Marcatili.
  • M.Sc. Eng. in Pharmaceutical Design and Engineering • Technical University of Denmark, 2016 - 2018

    Thesis: Combining deep learning and structural modelling to predict T cell receptor specificity. Supervised by Professor Paolo Marcatili and Postdoc Kamilla Kjærgaard Jensen at the Department of Bio and Health Informatics, DTU.
  • Student Assistant • Technical University of Denmark, Mar. 2018 - Aug. 2018

    Designing in silico tools for prediction of peptide functions. Supervised by Professor Paolo Marcatili and Professor Egon Bech Hansen.
  • Exchange Program • Seoul National University, South Korea, Sep. 2017 - Dec. 2017

  • B.Sc. in Biotechnology • Technical University of Denmark, 2013 - 2016

    Thesis: Investigation of the Substrate Specificity Determinants of Barley Limit Dextrinase. Supervised by Professor Birte Svensson and Ph.D. Susan Andersen at the Enzyme and Protein Chemistry Group, DTU.
  • Exchange Program • Renssalaer Polytechnic Institute, USA, Sep. 2015 - Dec. 2015

Selected Publications

Contact

Contact me if you have any questions about the tools I have been involved in creating or any other enquiries. The best way is by email or LinkedIn.

Details

Address
24-29 St Giles' • Department of Statistics, University of Oxford • Oxford OX1 3LB • United Kingdom
Phone
(000) 000-0000 x 0000
Email
tobias.olsen@stats.ox.ac.uk