# High resolution modeling and design of immune recognition

> **NIH NIH R35** · UNIV OF MARYLAND, COLLEGE PARK · 2022 · $208,699

## Abstract

Project Summary:
Accurate modeling of immune receptors and their recognition is a major challenge in computational biology, of
direct relevance to many diseases and therapeutics. While they share common heterodimeric immunoglobulin
folds, the immense sequence diversities of T cell receptors (TCRs) and antibodies lead to an astounding range
of antigen binding modes and specificities. Current docking approaches are largely incapable of producing
near-native models of these complexes in the set of top-ranked predictions, and conformational flexibility of
TCR and antibody loops pose a major barrier to predictive algorithms. My laboratory has had a longstanding
interest in developing and applying algorithms to better model and design TCRs and antibodies. We recently
developed an algorithm and web server to model TCRs from sequence (TCRmodel), a database of TCR
structures and sequences (TCR3d), and we have assembled an updated docking benchmark, which is being
used to develop improvements to our TCR docking algorithm. We have also recently developed an updated
antibody-antigen docking and affinity benchmark, which more than doubles the size of the previous benchmark
release; we have performed docking and affinity prediction assessment on these cases, giving us a rich
dataset of models and scores. During the next five years, we plan to expand and capitalize on these datasets
to develop advanced knowledge-based tools and algorithms, including geometric deep learning methods, to
address major challenges in this area: reliable modeling of CDR3 loop structures, accurate predictive antibody-
antigen and TCR-peptide-MHC docking, and design of TCR and antibody targeting. This will result in the ability
to model TCR and antibody interaction structures from sequence, precise control of TCR and antibody affinity
and specificity, and the design of new interactions to target antigens of interest. We will release our methods
and results to the community as web servers, databases, and code. This work will be enhanced by
collaborations with leading laboratories, through which we will have access to new experimental structural,
dynamic, and affinity data which will be used to develop, apply, and validate our algorithms.

## Key facts

- **NIH application ID:** 10330807
- **Project number:** 1R35GM144083-01
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Brian G. Pierce
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $208,699
- **Award type:** 1
- **Project period:** 2022-01-01 → 2026-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10330807

## Citation

> US National Institutes of Health, RePORTER application 10330807, High resolution modeling and design of immune recognition (1R35GM144083-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10330807. Licensed CC0.

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