Brian Pierce

Associate Professor

Pierce Group

Contact

Email: pierce@umd.edu

Call: (240) 314-6271

Education

  • Ph.D., Bioinformatics, Boston University, 2008
  • B.S., Physics and Computer Science, Duke University, 2000

Profile

Dr. Brian Pierce’s laboratory develops and applies computer algorithms to better understand how the immune system recognizes pathogens and cancer, and his lab is particularly interested in antibodies, T cell receptors, and vaccine design. For more information on the Pierce Lab, visit the lab web site at: https://piercelab.ibbr.umd.edu.

CURRENT RESEARCH

Recent efforts have focused on studying the structure of the hepatitis C virus (HCV) to inform the design of novel vaccine candidates. This work includes the development of a pioneering epitope-based vaccine for HCV that elicits neutralizing antibodies. The Pierce lab is also modeling how HCV can escape antibody neutralization, and this can be used toward developing new vaccine candidates.

Crystallographic structure of a broadly neutralizing hepatitis C virus antibody in complex with a vaccine immunogen designed by the Pierce laboratory.

The Pierce group is working on understanding and predicting how antibodies recognize viruses and other pathogens. Through a collaboration with researchers at Stanford University, they now have an unprecedented view of the structure and key antibody recognition features of HCV. This provides a roadmap for improved HCV vaccine candidates, as well as insights into antibody recognition of viral envelope proteins in general.

Global mapping recognition determinants for a panel of 16 human monoclonal antibodies targeting hepatitis C virus.  Color representing binding level of mutants, and mutants are clustered based on binding profile similarity.

A longstanding area of focus in the lab has been understanding how T cells recognize specific antigens, and work in the Pierce lab includes modeling how T cell receptors (TCR) can be engineered to target cancer cells. The group has developed a therapeutic TCR that targets melanoma, and ongoing work includes developing algorithms to improve and optimize TCR structures for immunotherapeutic applications.  

The Pierce lab has developed a number of predictive protein modeling and design algorithms to carry out their research, including Rosetta, TCRFlexDock, RosettaTCR, ZRANK, and ZDOCK. The lab is a member of the RosettaCommons community, which is a global network of developers of the Rosetta modeling and design software. 

Structure of a T cell receptor for melanoma immune-therapy that was designed for 400-fold affinity improvement in binding to the tumor antigen.
Publications
2024
Exploring the potential of structure-based deep learning approaches for T cell receptor design.
TCR3d 2.0: expanding the T cell receptor structure database with new structures, tools and interactions.
Hepatitis C Virus E1E2 Structure, Diversity, and Implications for Vaccine Development.
Proscan: a structure-based proline design web server.
Combinatorially restricted computational design of protein-protein interfaces to produce IgG heterodimers.
2023
A single C-terminal residue controls SARS-CoV-2 spike trafficking and incorporation into VLPs.
Evaluation of AlphaFold Antibody-Antigen Modeling with Implications for Improving Predictive Accuracy.
Structural basis for T cell recognition of cancer neoantigens and implications for predicting neoepitope immunogenicity.
Impact of AlphaFold on structure prediction of protein complexes: The CASP15-CAPRI experiment.
SARS-CoV-2 infection establishes a stable and age-independent CD8+ T cell response against a dominant nucleocapsid epitope using restricted T cell receptors.
Structure of engineered hepatitis C virus E1E2 ectodomain in complex with neutralizing antibodies.
TCRmodel2: high-resolution modeling of T cell receptor recognition using deep learning.
Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor (TCR) diversity.
2022
Mucosal nanobody IgA as inhalable and affordable prophylactic and therapeutic treatment against SARS-CoV-2 and emerging variants.
Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants.
Structural Features of Antibody-Peptide Recognition.
Induction of broadly neutralizing antibodies using a secreted form of the hepatitis C virus E1E2 heterodimer as a vaccine candidate.
An extended motif in the SARS-CoV-2 spike modulates binding and release of host coatomer in retrograde trafficking.
T cell receptors (TCRs) employ diverse strategies to target a p53 cancer neoantigen.
Structural assessment of HLA-A2-restricted SARS-CoV-2 spike epitopes recognized by public and private T-cell receptors.