Computational design of the affinity and specificity of a therapeutic T cell receptor.
Computational design of the affinity and specificity of a therapeutic T cell receptor.
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DOI：
10.1371/journal.pcbi.1003478
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发表时间：
2014-02
影响因子：
4.3
通讯作者：
Baker BM
中科院分区：
文献类型：
--
作者：
Pierce BG;Hellman LM;Hossain M;Singh NK;Vander Kooi CW;Weng Z;Baker BM
T 细胞受体 (TCR) 是抗原特异性免疫的关键，并且越来越多地被探索作为治疗方法，最明显的是在癌症免疫治疗中。由于 TCR 通常对其肽/MHC (pMHC) 配体仅具有低至中等的亲和力，因此人们认识到需要开发亲和力增强的 TCR 变体。先前的体外工程工作已经在 TCR 亲和力方面取得了显着的改进，但仍存在对肽特异性的维持和超高亲和力的生物学影响的担忧。与体外工程相反，计算设计可以直接解决这些问题，理论上允许合理控制肽特异性以及相对受控的亲和力增量。在这里，我们探索了临床相关 TCR DMF5 的计算设计的功效，该 TCR DMF5 识别由 I 类 MHC HLA-A2 呈现的黑色素瘤相关 Melan-A/MART-1 蛋白的九聚体和十聚体表位。我们测试了通过灵活和严格的建模方案选择的多个突变，评估对亲和力和特异性的影响，并利用数据来检查和改进算法性能。我们鉴定了多个提高结合亲和力的突变，并表征了先前报道的双突变体的结构、亲和力和结合动力学，该双突变体对十聚体 pMHC 配体的亲和力提高了 400 倍，且未检测到与非同源配体的结合。这种高亲和力突变体的结构表明构象后果非常小，并强调了我们建模过程的高保真度。总体而言，我们的工作展示了计算设计生成具有改进的 pMHC 亲和力的 TCR 的能力，同时明确考虑肽特异性，以及生成具有定制抗原靶向能力的 TCR 的潜力。 T 细胞受体 (TCR) 在免疫中发挥着重要作用，可识别主要组织相容性复合体蛋白呈现的肽抗原。由于它们能够靶向细胞内产生的蛋白质并启动细胞杀伤，因此人们对开发基于 TCR 的治疗策略（特别是针对癌症）产生了浓厚的兴趣。 TCR 的一个问题是它们的亲和力为弱到中等，这限制了治疗潜力。虽然体外进化已被用来增强 TCR 亲和力，有时会产生令人惊叹的结果，但这些技术会降低肽特异性，并且对亲和力增强几乎没有控制。在这里，我们探索了使用基于结构的计算设计来增强 TCR 亲和力，原则上可以控制特异性和亲和力增益。我们检查了最近用于黑色素瘤免疫治疗的临床相关 TCR，识别和表征了增强亲和力且对结合特异性没有可检测到的影响的突变。我们解决了最高亲和力设计的 TCR 与抗原复合物的晶体结构，这表明设计过程中结构建模的准确性很高，并且我们严格评估了几种设计方案和功能，以进一步提高设计成功率。这些结果为 TCR 计算设计的使用提供了宝贵的见解。最后，所鉴定的增强亲和力变体可能具有潜在的临床益处。
T cell receptors (TCRs) are key to antigen-specific immunity and are increasingly being explored as therapeutics, most visibly in cancer immunotherapy. As TCRs typically possess only low-to-moderate affinity for their peptide/MHC (pMHC) ligands, there is a recognized need to develop affinity-enhanced TCR variants. Previous in vitro engineering efforts have yielded remarkable improvements in TCR affinity, yet concerns exist about the maintenance of peptide specificity and the biological impacts of ultra-high affinity. As opposed to in vitro engineering, computational design can directly address these issues, in theory permitting the rational control of peptide specificity together with relatively controlled increments in affinity. Here we explored the efficacy of computational design with the clinically relevant TCR DMF5, which recognizes nonameric and decameric epitopes from the melanoma-associated Melan-A/MART-1 protein presented by the class I MHC HLA-A2. We tested multiple mutations selected by flexible and rigid modeling protocols, assessed impacts on affinity and specificity, and utilized the data to examine and improve algorithmic performance. We identified multiple mutations that improved binding affinity, and characterized the structure, affinity, and binding kinetics of a previously reported double mutant that exhibits an impressive 400-fold affinity improvement for the decameric pMHC ligand without detectable binding to non-cognate ligands. The structure of this high affinity mutant indicated very little conformational consequences and emphasized the high fidelity of our modeling procedure. Overall, our work showcases the capability of computational design to generate TCRs with improved pMHC affinities while explicitly accounting for peptide specificity, as well as its potential for generating TCRs with customized antigen targeting capabilities. T cell receptors (TCRs) play a major role in immunity, recognizing peptide antigens presented by major histocompatibility complex proteins. Due to their capacity to target intracellularly produced proteins and initiate cell killing, there is significant interest developing TCR-based therapeutic strategies, particularly towards cancer. A concern with TCRs is their weak-to-moderate affinities, which limits therapeutic potential. While in vitro evolution has been used to enhance TCR affinity, with sometimes spectacular results, these techniques can reduce peptide specificity and offer little control over affinity enhancements. Here we explored the use of structure-based computational design to enhance TCR affinity, which in principle can permit control over both specificity and affinity gains. We examined a clinically relevant TCR recently used in melanoma immunotherapy, identifying and characterizing mutations which enhanced affinity with no detectable impacts on binding specificity. We solved a crystal structure of our highest affinity designed TCR in complex with antigen, which indicated high accuracy of the structural modeling during the design process, and we critically evaluated several design protocols and functions to further improve design success. These results provide valuable insights into the use of computational design for TCRs. Lastly, the enhanced affinity variants identified may be of potential clinical benefit.
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DOI：
10.1107/s0907444904019158
发表时间：
2004-12-01
影响因子：
2.2
作者：
Emsley, P;Cowtan, K
通讯作者：
Cowtan, K
影响因子：
4.4
作者：
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通讯作者：
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影响因子：
4.8
作者：
Laugel, B;Boulter, JM;Sewell, AK
通讯作者：
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影响因子：
2.9
作者：
Karlsson, R;Katsamba, PS;Myszka, DG
通讯作者：
Myszka, DG
影响因子：
64.5
作者：
Colf, Leremy A.;Bankovich, Alexander J.;Garcia, K. Christopher
通讯作者：
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