"The key challenge in bioinformatics today is not the development of new algorithms, but truly understanding the available data." - Bjoern Peters, Ph.D.
Bjoern Peters is an Associate Professor in the Vaccine Discovery Division. Dr.
Peters' research focus is on the analysis of immunological information
using statistical and computational methods, with a particular interest
in modeling the recognition of immune epitopes. In 2000, Dr. Peters received his Diploma from the University of Hamburg in Germany, for a thesis in Laser Physics and Quantum Optics. Dr. Peters then undertook graduate work at the Humboldt University in Berlin,
where he became interested in applying quantitative methods commonly
used in physics to immunological questions. He earned his PhD in
Theoretical Biophysics in 2003 with summa cum laude, writing his thesis
on modeling the MHC class I antigen processing and presentation pathway. In 2004, Dr. Peters came to LIAI for
a postdoctoral training position in Dr. Sette's lab. From the start, he
was heavily involved in the Immune Epitope Database project (http://www.immuneepitope.org),
and became its Co-PI in charge of bioinformatics in 2005. Between 2006
and 2007 he was a Research Scientist, before being appointed Assistant Professor at LIAI at the start of 2008.
Peters and his team are developing tools to analyze and predict what
parts of a pathogen or allergen are targeted by immune responses.
Several of the molecular mechanisms involved in these processes have
been well characterized experimentally. By analyzing patterns in the
experimental data, it is possible to create predictive computational
models. These models can be applied to scan allergens or pathogens in silico
likely immune response targets. Identifying these targets aids in the
rational development of treatments and diagnostics. The resulting
computational tools are made freely available as part of the Immune
Epitope Database Analysis Resource (http://tools.immuneepitope.org
Immune Epitope Database itself catalogs and organizes immune epitope
data, which requires transforming free text information from journal
publications into a structured format. To make optimal use of the
stored information, it is desirable to connect it with information
stored elsewhere. For example, one could ask what the variability of an
immune response target in different strains of a pathogen is. This
requires connecting the IEDB data to other resources storing genomic
information. Doing this efficiently requires a community consensus on
knowledge representation standards. Dr. Peters team is contributing to
such consensus building and standardization efforts through active work
on scientific community initiatives: The Ontology of Biomedical
Investigations (OBI, http://obi-ontology.org/
), and the NIAID data interoperability working group.
The immune epitope database (IEDB) 3.0. Nucleic Acid Res. 2015
Transcriptional profile of tuberculosis antigen-specific T cells reveals novel multifunctional features. J. Immunol. 2014
Epigenomic analysis of primary human T cells reveals enhancers associated with TH2 memory cell differentiation and asthma susceptibility. Nat. Immunol. 2014
Using a combined computational-experimental approach to predict antibody-specific B cell epitopes. Structure. 2014
Properties of MHC class I presented peptides that enhance immunogenicity. PLoS Comput. Biol. 2013
Previously undescribed grass pollen antigens are the major inducers of T helper 2 cytokine-producing T cells in allergic individuals. PNAS
Predicting cell types and genetic variations contributing to disease by combining GWAS and epigenetic data. PLoS One 2013
Positional bias of MHC class I restricted T-cell epitopes in viral antigens is likely due to a bias in conservation. PLoS Comput Biol 2013
Drug hypersensitivity caused by alteration of the MHC-presented self-peptide repertoire. Proc Natl Acad Sci U S A. 2012
Pre-existing immunity against swine-origin H1N1 influenza viruses in the general human population. Proc Natl Acad Sci U S A. 2009
A consensus epitope prediction approach
identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol. 2006
A community resource benchmarking predictions of
peptide binding to MHC-I molecules. PLoS Comput Biol. 2006
Examining the independent binding assumption for
binding of peptide epitopes to MHC-I molecules. Bioinformatics. 2003
Identifying MHC class I epitopes by predicting
the TAP transport efficiency of epitope precursors. J Immunol. 2003
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