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<!DOCTYPE HTML>
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<html>
<head>
<title>Tobias Hegelund Olsen's Personal Page</title>
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<div id="wrapper">
<header id="header" class="alt">
<!--<span class="logo"><img src="images/ab.png" alt="" /></span>-->
</header>
<!-- Nav -->
<nav id="nav">
<ul>
<li><a href="#intro" class="active">About me</a></li>
<li><a href="#experienceNeducation">Experience and Education</a></li>
<li><a href="#publications">Selected Publications</a></li>
<li><a href="#cta">Contact</a></li>
</ul>
</nav>
<!-- Main -->
<div id="main">
<!-- Introduction -->
<section id="intro" class="main">
<div class="spotlight">
<div class="content">
<header class="major">
<h2>Tobias Hegelund Olsen</h2>
</header>
<p>I specialise in applied research, focusing on developing
computational tools for the discovery and design of therapeutic
proteins, such as antibodies, T-cell receptors (TCRs),
and other novel modalities. For this, I make heavy use of
new deep learning techniques, but also other data science
approaches. The core objective of my work is to integrate
computational design pipelines with laboratory workflows,
creating a single, highly efficient system for rational
design of therapeutics.
</p>
<p>
I did my PhD in the Oxford Protein Informatics Group (OPIG),
University of Oxford, under supervision of Prof. Charlotte Deane,
MDE. I have extensive experience with developing new models for
immunogenicity, many different developability issues, paratope
prediction, structure prediction and affinity prediction. I also
have experience collating, processing and working with both
structural data and billions of amino acid sequences for both
antibodies and TCRs. Additionally, I have worked on predicting
useful bioactivities for peptides.
</p>
</div>
<span class="image"><img src="images/selfie.jpeg" alt="" /></span>
</div>
<p>
<h3><b>Published and available tools</b></h3>
<ul>
<li><b><a href="https://opig.stats.ox.ac.uk/webapps/ots">OTS:</a></b>
The Observed T cell receptor Space database enables paired-chain repertoire mining,
coherence analysis and language modelling
(<a href="https://www.biorxiv.org/content/10.1101/2024.05.20.594960v2">paper</a>).
</li>
<li><b><a href="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/TobiasHeOl/AbLang2">AbLang-2:</a></b>
Addressing the antibody germline bias and its effect on language models for improved antibody design
(<a href="https://www.biorxiv.org/content/10.1101/2024.02.02.578678v1">paper</a>).
</li>
<li><b><a href="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/oxpig/AntiFold">AntiFold:</a></b>
Improved antibody structure-based design using inverse folding
(<a href="https://doi.org/10.48550/arXiv.2405.03370">paper</a>).
</li>
<li><b><a href="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/TobiasHeOl/kasearch">KA-Search:</a></b>
A tool for rapid and exhaustive sequence search of billions of antibodies,
enabling exploration of the mutational space of antibodies
(<a href="https://doi.org/10.1101/2022.11.01.513855">paper</a>).
</li>
<li><b><a href="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/TobiasHeOl/AbLang">AbLang:</a></b>
An antibody specific language model for therapeutic antibody engineering
(<a href="https://doi.org/10.1093/bioadv/vbac046">paper</a>).
</li>
<li><b><a href="https://opig.stats.ox.ac.uk/webapps/oas/">OAS:</a></b>
A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences
(<a href="https://doi.org/10.1002/pro.4205">paper</a>).
</li>
<li><b><a href="https://services.healthtech.dtu.dk/service.php?AnOxPePred-1.0">AnOxPePred:</a></b>
Prediction of antioxidative properties of peptides using deep learning
(<a href="https://doi.org/10.1038/s41598-020-78319-w">paper</a>).
</li>
<li><b><a href="https://wenmr.science.uu.nl/proabc2/">proABC-2:</a></b>
PRediction of AntiBody contacts v2 and its application to information-driven docking.
(<a href="https://doi.org/10.1093/bioinformatics/btaa644">paper</a>).
</li>
<li><b><a href="https://services.healthtech.dtu.dk/service.php?TCRpMHCmodels-1.0">TCRpMHCmodels:</a></b>
Structural modelling of TCR-pMHC class I complexes
(<a href="https://doi.org/10.1038/s41598-019-50932-4">paper</a>).
</li>
</p>
<ul class="features">
<li>
<span class="alt"><img src="images/socialmedialogo.png" alt="" height="100" /></span>
<!--<span class="icon major style3 fa-copy"></span>-->
<h3>Media</h3>
<p>I can be contacted on <a href="https://linkedin.com/in/HegelundOlsen">LinkedIn</a> and
Twitter @HegelundOlsen.</p>
</li>
<li>
<span class="alt"><img src="images/GitHub-Mark.png" alt="" height="100" /></span>
<!--<span class="icon brands fa-github logo"></span>-->
<h3>GitHub</h3>
<p>Any code I write, and which is public, I will try and make available on my
<a href="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/TobiasHeOl">Github</a>.
</p>
</li>
</ul>
</section>
<!-- Background -->
<section id="experienceNeducation" class="main">
<div class="spotlight">
<div class="content">
<header class="major">
<h2>Experience and Education</h2>
</header>
<ul class="alt">
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/gsk.jpeg" height="110" class="center">
</div>
<div class="col-10 col-12-medium">
<h4><b>Investigator, Protein Design and Informatics</b> • GSK, Oct.
2023 - Present</h4>
Applied research for developing computational tools for discovery and design of therapeutic biologics.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/oxlogo.png" height="110" class="center">
</div>
<div class="col-10 col-12-medium">
<h4><b>PhD candidate in Immunoinformatics</b> • University of Oxford, Oct.
2019 - Sep. 2023</h4>
Investigating B-cell repertoire data using deep learning approaches to aid in
the development of antibody therapeutics.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium" class="center">
<img src="images/dtulogo.png" height="150" class="center">
</div>
<div class="col-10 col-12-medium">
<h4><b>Research Assistant</b> • Technical University of Denmark, Sep. 2018
- Jun. 2019</h4>
Designing <i>in silico</i> 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.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/dtulogo.png" height="150" class="center">
</div>
<div class="col-10 col-12-medium">
<h4><b>M.Sc. Eng. in Pharmaceutical Design and Engineering</b> • Technical
University of Denmark, 2016 - 2018</h4>
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.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/dtulogo.png" height="150">
</div>
<div class="col-10 col-12-medium">
<h4><b>Student Assistant</b> • Technical University of Denmark, Mar. 2018 -
Aug. 2018</h4>
Designing <i>in silico</i> tools for prediction of peptide functions. Supervised
by Professor Paolo Marcatili and Professor Egon Bech Hansen.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<div class="center">
<img src="images/snulogo.png" height="100">
</div>
</div>
<div class="col-10 col-12-medium">
<h4><b>Exchange Program</b> • Seoul National University, South Korea, Sep.
2017 - Dec. 2017</h4>
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/dtulogo.png" height="150">
</div>
<div class="col-10 col-12-medium">
<h4><b>B.Sc. in Biotechnology</b> • Technical University of Denmark, 2013 -
2016</h4>
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.
</div>
</li>
<li>
<div class="row">
<div class="col-2 col-12-medium">
<img src="images/rpilogo.png" height="100">
</div>
<div class="col-10 col-12-medium">
<h4><b>Exchange Program</b> • Renssalaer Polytechnic Institute, USA, Sep.
2015 - Dec. 2015</h4>
</div>
</li>
</ul>
</div>
</div>
</section>
<!-- Second Section -->
<section id="publications" class="main special">
<header class="major">
<h2>Selected Publications</h2>
<ul>
<li><a href="https://doi.org/10.1101/2022.11.01.513855">
<h4><u><b>KA-Search: Rapid and exhaustive sequence
identity search of known antibodies.</b></u> </h4>
</a>
<b><u>Olsen, T. H.</b></u>; Abanades, B.; Moal, I. H.; & Deane, C. M., BioRxiv, (2022).
</li>
<li><a href="https://doi.org/10.1093/bioadv/vbac046">
<h4><u><b>AbLang: An antibody language model for
completing antibody sequences.</b></u> </h4>
</a>
<b><u>Olsen, T. H.</b></u>; Moal, I. H.; & Deane, C. M., Bioinformatics Advances 2-1,
(2022).
</li>
<li><a href="https://doi.org/10.1002/pro.4205">
<h4><u><b>Observed Antibody Space: A diverse database of
cleaned, annotated, and translated unpaired and paired antibody
sequences.</b></u> </h4>
</a>
<b><u>Olsen, T. H.</b></u>; Boyles, F.; & Deane, C. M., Protein Science 1-6, (2021).
</li>
<li><a href="https://doi.org/10.1038/s41598-020-78319-w">
<h4><u><b>AnOxPePred: using deep learning for the prediction of antioxidative properties
of peptides.</b></u> </h4>
</a>
<b><u>Olsen, T. H.</b></u>; Yesiltas, B.; Marin, F. I.; Pertseva, M.; García-Moreno, P. J.;
Gregersen, S.;
Overgaard, M. T.; Jacobsen, C.; Lund, O.; Hansen, E. B. & Marcatili, P., Scientific
Reports 10, 2045-2322
(2020).
</li>
<li><a href="https://doi.org/10.1093/bioinformatics/btaa644">
<h4><u><b>proABC-2: PRediction of AntiBody contacts v2 and its application to
information-driven docking.</b></u> </h4>
</a>
Ambrosetti, F.; <b><u>Olsen, T. H.</u></b>; Olimpieri, P. P.; Jiménez-García, B.; Milanetti,
E.; Marcatilli, P.
& Bonvin, A. M. J. J., Bioinformatics 36, 5107–5108 (2020).
</li>
<li><a href="https://doi.org/10.1038/s41598-019-57229-6">
<h4><u><b>Identification of emulsifier potato peptides by bioinformatics: application to
omega-3 delivery emulsions and release from potato industry side
streams.</b></u> </h4>
</a>
García-Moreno, P. J.; Gregersen, S.; Nedamani, E. R.; <b><u>Olsen, T. H.</u></b>; Marcatili,
P.; Overgaard, M. T.; Andersen, M. L.; Hansen, E. B. & Jacobsen, C., Scientific Reports
10, 2045-2322 (2020).
</li>
<li><a href="http:https://dx.doi.org/10.1038/s41598-019-50932-4">
<h4><u><b>TCRpMHCmodels: Structural modelling of TCR-pMHC class I complexes.</b></u>
</h4>
</a>
Jensen, K. K.; Rantos, V.; Jappe, E. C.; <b><u>Olsen, T. H.</u></b>; Jespersen, M. C.;
Jurtz, V.; Jessen, L. E.; Lanzarotti, E.; Mahajan, S.; Peters, B.; Nielsen, M. &
Marcatili, P., Scientific Reports 9, 1–12 (2019).
</li>
</ul>
</section>
<!-- Get Started -->
<section id="cta" class="main">
<div class="spotlight">
<div class="content">
<div class="row">
<div class="col-6 col-12-medium">
<h2>Contact</h2>
<p>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 LinkedIn.</p>
</div>
<div class="col-6 col-12-medium">
<h2>Details</h2>
<dl class="alt">
<dt>Address</dt>
<dd></dd>
<dt>Phone</dt>
<dd>(000) 000-0000 x 0000</dd>
<dt>Email</dt>
<dd><a href="#"></a></dd>
</dl>
</div>
</div>
</div>
</section>
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