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“The challenge in bioinformatics for immunology is not the development of new algorithms, but to accurately translate immunological questions into a computable form. ”
Bjoern Peters, Ph.D.
Associate Professor
Center for Infectious Disease; Division of Vaccine Discovery

cell-bullet1.jpg"The challenge in bioinformatics for immunology is not to develop faster algorithms, but to accurately translate immunological questions into a computable form." - Bjoern Peters, Ph.D.

Bjoern Peters is an Associate Professor in the Vaccine Discovery Division. Dr. Peters' research focuses 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 degree in Physics at the University of Hamburg with a thesis on quantum noise-driven laser oscillations. In 2003, he earned his PhD in Theoretical Biophysics (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 postdoctoral training in Dr. Alessandro Sette's lab, focusing on the initiation and development of the Immune Epitope Database project (http://www.iedb.org). In contrast to his original plans, he has been trapped in San Diego ever since. There are worse places to be trapped. He was appointed Assistant Professor at LIAI in 2008, and promoted to Associate Professor with tenure in 2014.

research focus
cell-bullet2.jpgResearch in the Peters lab is focused in three areas, all relating to the development of computational tools to address fundamental questions in immunology.

Starting as a PhD student in 2000, Dr. Peters has worked on the development and validation of tools to analyze and predict which parts of a pathogen, allergen or cancer cell are targeted by immune responses. Identifying these specific molecular targets of immune responses, called epitopes, recognized by diseased individuals opens a path towards the development of diagnostics, vaccines and therapeutics for that particular disease.  The tools the Peters lab develops aim to reduce the experimental effort required to identify these targets; computer-based predictions allow researchers to focus on the components most likely to be recognized rather than screening thousands of molecules.  

The second research area of the lab is the identification of differences between immune cells in individuals with opposite disease outcomes. Powerful experimental tools have been developed to detect differences in how cells utilize the diverse parts of the genome. The Peters lab is using these tools to characterize how immune cells from diseased individuals differ from healthy individuals. These cells are isolated using disease-specific epitopes (or reagents based on them), so our epitope-identifying algorithms directly aid our disease-focused work. This research helps us understand how the disease develops and identifies potential targets in the genome for interventions to treat or prevent the disease.

Finally, the Peters lab is deeply involved in the development of community standards for knowledge representation to promote interoperability and re-use of data. The Peters and Sette lab maintain the Immune Epitope Database , which catalogs all published experiments on immune epitope recognition.This requires transforming free text information from journal publications into a structured format, and to make the information optimally useful, connecting it with information stored elsewhere. Doing this efficiently requires a community consensus on knowledge representation. Dr. Peters’ team is contributing to such consensus building and standardization efforts through active work on scientific community initiatives such as the Ontology of Biomedical Investigations (OBI).
selected publications

cell-bullet3.jpg 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. Proc Natl Acad Sci U S A. 2013

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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