Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries.
Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries.
复制标题
DOI：
10.1186/1745-7580-4-2
复制
发表时间：
2008-01-25
期刊：
影响因子：
--
通讯作者：
Peters B
中科院分区：
文献类型：
--
作者：
Sidney J;Assarsson E;Moore C;Ngo S;Pinilla C;Sette A;Peters B
先前已经表明，组合肽文库是表征I类MHC分子的结合特异性的有用工具。与其他方法相比，如池测序或测量单个肽的亲和力，利用位置扫描组合文库提供了MHC分子特异性的基线表征，其是成本有效的、定量的和无偏倚的。在这里，我们提出了一个大规模的应用，这项技术的19个不同的人类和小鼠的I类等位基因。这些包括非常好地表征的等位基因（例如HLA A*0201），几乎没有先前数据可用的等位基因（例如HLA A*3201），以及与先前关于特异性的报告相冲突的等位基因（例如HLA A*3001）。对于所有等位基因，位置扫描组合文库能够阐明用统一方法定义的不同结合模式，我们在这里提供。我们引入了一种启发式方法来将这些数据转化为主要和次要锚位置及其首选残基的经典定义。最后，我们验证了这些矩阵可以用来确定候选的MHC结合肽和T细胞表位在牛痘病毒和流感病毒系统，分别。这些数据在大规模上（包括15个人类和4个小鼠I类等位基因）证实了位置扫描组合文库方法用于描述MHC I类结合特异性和鉴定高亲和力结合肽的功效。这些文库被证明可用于鉴定特定的一级和二级锚位置，从而鉴定更简单的基序，类似于通过其他方法描述的那些。本研究还提供了可用于预测几个等位基因的高亲和力结合物的矩阵，这些等位基因先前无法获得结合特异性的详细定量描述，包括A*3001、A*3201、B*0801、B*1501和B*1503。
It has been previously shown that combinatorial peptide libraries are a useful tool to characterize the binding specificity of class I MHC molecules. Compared to other methodologies, such as pool sequencing or measuring the affinities of individual peptides, utilizing positional scanning combinatorial libraries provides a baseline characterization of MHC molecular specificity that is cost effective, quantitative and unbiased. Here, we present a large-scale application of this technology to 19 different human and mouse class I alleles. These include very well characterized alleles (e.g. HLA A*0201), alleles with little previous data available (e.g. HLA A*3201), and alleles with conflicting previous reports on specificity (e.g. HLA A*3001). For all alleles, the positional scanning combinatorial libraries were able to elucidate distinct binding patterns defined with a uniform approach, which we make available here. We introduce a heuristic method to translate this data into classical definitions of main and secondary anchor positions and their preferred residues. Finally, we validate that these matrices can be used to identify candidate MHC binding peptides and T cell epitopes in the vaccinia virus and influenza virus systems, respectively. These data confirm, on a large scale, including 15 human and 4 mouse class I alleles, the efficacy of the positional scanning combinatorial library approach for describing MHC class I binding specificity and identifying high affinity binding peptides. These libraries were shown to be useful for identifying specific primary and secondary anchor positions, and thereby simpler motifs, analogous to those described by other approaches. The present study also provides matrices useful for predicting high affinity binders for several alleles for which detailed quantitative descriptions of binding specificity were previously unavailable, including A*3001, A*3201, B*0801, B*1501 and B*1503.