对抗原特异性 CDR3 结构域的熵分析确定了具有不同抗原特异性的 CDR3 之间共享的基本结合基序。
Entropic analysis of antigen-specific CDR3 domains identifies essential binding motifs shared by CDR3s with different antigen specificities.
机构信息
Institute for Systems Biology, Seattle, WA 98109, USA; Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
Institute for Systems Biology, Seattle, WA 98109, USA; Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 91125, USA.
出版信息
Cell Syst. 2023 Apr 19;14(4):273-284.e5. doi: 10.1016/j.cels.2023.03.001. Epub 2023 Mar 30.
Antigen-specific T cell receptor (TCR) sequences can have prognostic, predictive, and therapeutic value, but decoding the specificity of TCR recognition remains challenging. Unlike DNA strands that base pair, TCRs bind to their targets with different orientations and different lengths, which complicates comparisons. We present scanning parametrized by normalized TCR length (SPAN-TCR) to analyze antigen-specific TCR CDR3 sequences and identify patterns driving TCR-pMHC specificity. Using entropic analysis, SPAN-TCR identifies 2-mer motifs that decrease the diversity (entropy) of CDR3s. These motifs are the most common patterns that can predict CDR3 composition, and we identify "essential" motifs that decrease entropy in the same CDR3 α or β chain containing the 2-mer, and "super-essential" motifs that decrease entropy in both chains. Molecular dynamics analysis further suggests that these motifs may play important roles in binding. We then employ SPAN-TCR to resolve similarities in TCR repertoires against different antigens using public databases of TCR sequences.
抗原特异性 T 细胞受体 (TCR) 序列具有预后、预测和治疗价值，但解码 TCR 识别的特异性仍然具有挑战性。与碱基配对的 DNA 链不同，TCR 以不同的方向和不同的长度与它们的靶标结合，这增加了比较的复杂性。我们提出了一种基于 TCR 长度标准化的扫描参数 (SPAN-TCR)，用于分析抗原特异性 TCR CDR3 序列并确定驱动 TCR-pMHC 特异性的模式。通过熵分析，SPAN-TCR 确定了降低 CDR3 多样性（熵）的 2 -mer 基序。这些基序是最常见的可预测 CDR3 组成的模式，我们确定了降低同一 CDR3 α 或 β 链中包含 2 -mer 的熵的“必需”基序，以及降低两条链中熵的“超必需”基序。分子动力学分析进一步表明，这些基序可能在结合中发挥重要作用。然后，我们使用 SPAN-TCR 利用 TCR 序列的公共数据库来解决针对不同抗原的 TCR 库之间的相似性。
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