Skip to content
DraconiticResearch
Search Sources API
182,469 studies31 sourcesUpdated 56 min ago
Peer-reviewedCC BY-NC-ND 4.0Cell Reports Methods

Deep mapping of structural perturbations to energetics enables precise TCR design

  • Shifu Luo1,2,3
  • Songming Zhang2
  • Ying Shi2,4,5
  • Junjie Li2
  • Jiaxin Cai2
  • Ning-Yi Shao1
  • Yi Pan2
  • Jinyan Li2
5 affiliations
  1. 1Faculty of Health Sciences, University of Macau, Taipa, Macao SAR 999078, China
  2. 2Faculty of Computer Science and Artificial Intelligence, Shenzhen University of Advanced Technology, Shenzhen, Guangdong, China
  3. 3Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
  4. 4School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi, China
  5. 5College of Computer Science and Technology, Taiyuan Normal University, Jinzhong 030619, China
Read full textDOICite

Abstract

Affinity optimization and cross-reactivity profiling are pivotal for T cell receptor (TCR) engineering but remain hindered by repertoire diversity and limited structural insights. We present mpTCRai, a deep learning framework that predicts residue-level structural interactions by simulating the sequential mechanics of antigen presentation and T cell recognition. Through a hotspot-based scoring mechanism, mpTCRai explicitly maps structural perturbations to energetic changes, revealing that these perturbations strongly correlate with experimental affinities (r = −0.88). The model captures the structural dependencies of key molecular switches and computationally screens against cross-reactive mutations like Y5W to evaluate structural off-target risks. Guided by these insights, we computationally prioritized four A6-TCR variants for adult T cell leukemia. Overall, this work establishes a unified computational platform integrating structural and energetic constraints to accelerate the downstream experimental validation and rational design of engineered TCRs.

Related

  1. Mapping structural-immune crosstalk in asthma.
    Lin Chen, Yu-Xuan Jin, Jin-Lyu Sun
    Life medicinePeer-reviewedCC BYFull textPhysiologyEurope PMCPMC
  2. Evaluation of BCR::ABL1 Positivity in Peripheral Blood Neutrophils Enables the Diagnosis of De Novo Blast Phase Chronic Myeloid Leukemia with Additional Structural Abnormalities of the Philadelphia Chromosome
    Hirotaka Sakai, Ikuo Miura, Ayako Arai
    Internal MedicinePeer-reviewedCC BY-NC-NDFull textGeneral medicineJ-STAGE
  3. Mapping Country-Level Structural Capacity Constraints in Brachytherapy Infrastructure for Locally Advanced Cervical Cancer in the Americas: A cross-sectional study
    David Viveros-Carreño, Nathalia Mora-Soto, Isabel Beshar +7
    International journal of gynecological cancer : official journal of the International Gynecological Cancer SocietyPeer-reviewedLicence not statedFull textMedical imaging & radiologyPMC
  4. Spatially mapping the mechanical and structural properties of the seedling uterine fibroid-myometrium interface
    Daniella M. Fodera, Alara Sutcu, Arielle S. Joasil +8
    Npj Women's HealthPeer-reviewedCC BY-NC-ND 4.0Full textObstetrics, gynaecology & paediatricsPMC
  5. Mapping intronic mutational hotspots by in silico mutagenesis enables single antisense oligonucleotide correction of multiple variants
    H. Saei, B. Ardin, N. Kaiser +20
    medRxivPreprintCC BYFull textHuman geneticsmedRxiv

Cite

BibTeXRIS
BibTeX
@article{luo2026deep,
  title = {{Deep mapping of structural perturbations to energetics enables precise TCR design}},
  author = {Shifu Luo and Songming Zhang and Ying Shi and Junjie Li and Jiaxin Cai and Ning-Yi Shao and Yi Pan and Jinyan Li},
  journal = {Cell Reports Methods},
  year = {2026},
  month = aug,
  doi = {10.1016/j.crmeth.2026.101569},
  url = {https://doi.org/10.1016/j.crmeth.2026.101569},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - Deep mapping of structural perturbations to energetics enables precise TCR design
AU  - Shifu Luo
AU  - Songming Zhang
AU  - Ying Shi
AU  - Junjie Li
AU  - Jiaxin Cai
AU  - Ning-Yi Shao
AU  - Yi Pan
AU  - Jinyan Li
PY  - 2026
DA  - 2026/08/20
JO  - Cell Reports Methods
DO  - 10.1016/j.crmeth.2026.101569
UR  - https://doi.org/10.1016/j.crmeth.2026.101569
AB  - Affinity optimization and cross-reactivity profiling are pivotal for T cell receptor (TCR) engineering but remain hindered by repertoire diversity and limited structural insights. We present mpTCRai, a deep learning framework that predicts residue-level structural interactions by simulating the sequential mechanics of antigen presentation and T cell recognition. Through a hotspot-based scoring mechanism, mpTCRai explicitly maps structural perturbations to energetic changes, revealing that these perturbations strongly correlate with experimental affinities (r = −0.88). The model captures the structural dependencies of key molecular switches and computationally screens against cross-reactive mutations like Y5W to evaluate structural off-target risks. Guided by these insights, we computationally prioritized four A6-TCR variants for adult T cell leukemia. Overall, this work establishes a unified computational platform integrating structural and energetic constraints to accelerate the downstream experimental validation and rational design of engineered TCRs.
SN  - 2667-2375
ER  -