# High Resolution Modeling and Design of T-Cell Receptors

> **NIH NIH R01** · UNIV OF MARYLAND, COLLEGE PARK · 2021 · $321,398

## Abstract

Accurate modeling of the structure and recognition of adaptive immune receptors is a major challenge in
computational biology. Despite a shared immunoglobulin structural framework, highly variable antigen binding
loop sequences and structures, with intrinsic dynamics and binding conformational changes, are often not
accurately represented or correctly modeled using current algorithms. There is an even greater need to
address this challenge due to the rapidly growing field of immune sequencing, which often results in thousands
of sequences of antigen-specific immune receptors from the repertoire of a single individual per experiment. In
the absence of reliable modeling tools, the observed shared sequence motifs and areas of divergence lack a
structural and mechanistic explanation, given that experimental structural characterization is not practical or
feasible for more than a handful of molecules. The focus of this application is on T cell receptors (TCRs), which
recognize antigenic peptides by the major histocompatibility complex (MHC), leading to the cellular immune
response. We will develop advanced modeling and design algorithms to address the challenges of flexible loop
modeling through informatics and knowledge-based developments to help unravel their recognition code. This
will entail the development of algorithms to reliably model TCR structures from sequence (Aim 1), model TCR
recognition of peptide-MHCs through docking (Aim 2), and design TCR recognition through loop engineering
(Aim 3). These Aims will be accomplished through validation against existing experimental structural and
affinity data, as well as close partnership with experimental laboratories that will provide sequence, structural,
dynamic, and binding measurements of TCRs, and validate affinity and structure of designed receptors.
Collectively, these developments will allow the illumination of the mechanistics underpinning recognition by
specific and repertoire-level TCRs from sequence, improved loop modeling and docking algorithms, and the
capability to effectively control and engineer TCR recognition through structure-based design.

## Key facts

- **NIH application ID:** 10168567
- **Project number:** 5R01GM126299-04
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** Brian G. Pierce
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $321,398
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10168567

## Citation

> US National Institutes of Health, RePORTER application 10168567, High Resolution Modeling and Design of T-Cell Receptors (5R01GM126299-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10168567. Licensed CC0.

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