Brian G. Pierce

This study determines the activation pathway of the human voltage-gated proton channel hHV1 using computational methods, revealing voltage-driven structural changes and lipid effects validated by experiments.

This paper develops a nanoparticle vaccine platform incorporating native HCV E1E2 to enhance immunogenicity, showing improved neutralizing antibody responses compared to subunit vaccines.

This study benchmarks deep learning models' ability to predict mutation-induced fold switching in proteins using an NMR-characterized dataset of fold-switching mutants.

This study benchmarks AlphaFold and related methods for modeling antibody and TCR antigen recognition, showing improvements with AlphaFold3 and increased sampling but varying success across complex types.

This study shows that GSDMA undergoes S-acylation, which promotes pyroptosis by enhancing membrane anchoring and oligomerization, distinct from GSDMD's palmitoylation mechanism.

IMMREP25 evaluates TCR:pMHC binding prediction for unseen peptides, showing improved performance with structural modeling but still below random guessing.

This study improves HIV broadly neutralizing antibodies through multistate engineering, resulting in enhanced breadth and potency against diverse Env variants.

This paper develops a computational tool to identify conserved, immunogenic T cell epitopes across Plasmodium falciparum strains and HLA alleles, improving malaria vaccine design.

This study evaluates AlphaFold's ability to model nanobody-peptide interactions, finding moderate success in some cases but limitations when translating to complex contexts.

This study engineers an HIV broadly neutralizing antibody to improve its potency, antiviral activity, and manufacturability while maintaining safety and efficacy.

The paper determines crystal structures of a TCR complex with NRAS neoantigens and uses AlphaFold to model these complexes, showing varying success across different implementations.

This study evaluates two deep learning methods for designing T cell receptors by comparing their performance to physics-based approaches in binding target antigens.

This study evaluates AlphaFold's performance in modeling antibody-antigen complexes, finding improved accuracy with a 30% success rate and identifying factors that enhance modeling success.

This study benchmarks AlphaFold for predicting protein complex structures, finding high accuracy for heterodimers but challenges for antibody-antigen complexes.