Valentin Junet | Senior Data Scientist
Engimmune Therapeutics AG
Valentin Junet has 6+ years of experience in developing data-driven solutions to solve complex biotech problems. After a master’s degree in applied mathematics at ETH Zurich, he received his Ph.D. in Bioinformatics at the Autonomous University of Barcelona as part of a collaborative project funded by the European Union’s H2020 program. During this period, he was working between the academy and the industry to develop application-oriented tools in multiple fields, namely Immunoinformatics with peptide-MHC class II binding prediction, Pharmacology with in-silico clinical trials and retrospective cohort study and Hematology with the study of B-cells’ mutational patterns in monoclonal lymphomas. His work was published in various top-tier peer-reviewed journals like Bioinformatics or Briefings in Bioinformatics. In 2022, he joined Engimmune Therapeutics as a Data scientist where he focuses on the development of ML models for the engineering of soluble TCRs and on implementing data-driven platforms with distinct applications such as target/epitope discovery or synthetic library design.
Appearances:
Festival of Biologics Day 2 @ 17:30
Machine Learning–Guided Protein Engineering of Picomolar Affinity Soluble T Cell Receptors
- Soluble TCR engagers enable high-affinity targeting of disease-relevant antigens, thus representing a promising therapeutic modality for oncology, autoimmunity, and infectious disease. However, affinity maturation of soluble TCRs (typically requiring a million-fold enhancement) is complicated by specificity challenges.
- Here we describe a pipeline that utilizes data from diverse DMS libraries, which include both on-target and off-target selections, to train several machine learning models. We then use their predictions to guide the design of improved TCR variants.
- Our machine learning–guided protein engineering approach enables the rapid identification of picomolar affinity TCRs with high levels of specificity.
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