公开(公告)号：US20230349914A1

公开(公告)日：2023-11-02

申请号：US18029395

申请日：2021-09-30

Applicant: THE BOARD OF REGENTS OF THE UNIVERSITY OF TEXAS SYSTEM

Inventor： Tianshi LU ,  Tao WANG

IPC: G01N33/68 ,  G06N3/08 ,  G16B40/20 ,  G16B15/30

CPC classification number: G01N33/6845 ,  G06N3/08 ,  G06N3/0455 ,  G16B40/20 ,  G16B15/30

Abstract: Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). To help determine the TCRs that interact with particular neoantigens, prediction models that predict TCR-binding specificities of neoantigens presented by different classes of major histocompatibility complex (MHCs) were developed. To confirm the applicability of the model to clinical settings, the prediction models were comprehensively validated by a series of analyses. The validated prediction models used a flexible transfer learning approach and differential learning schema to achieve highly accurate prediction of TCR binding specificity only using TCR sequence data, antigen sequence data, and MHC alleles.