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主要组织相容性复合体I类分子的肽结合特异性解析为一系列明显独立的亚特异性:通过肽库进行定量分析并改进结合预测。

Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding.

作者信息

Stryhn A, Pedersen L O, Romme T, Holm C B, Holm A, Buus S

机构信息

Department of Experimental Immunology, University of Copenhagen, Denmark.

出版信息

Eur J Immunol. 1996 Aug;26(8):1911-8. doi: 10.1002/eji.1830260836.

DOI:10.1002/eji.1830260836
PMID:8765039
Abstract

Considerable interest has focused on understanding how major histocompatibility complex (MHC) specificity is generated and characterizing the specificity of MHC molecules with the ultimate goal being to predict peptide binding. We have used a strategy where all possible peptides of a particular size are distributed into positional scanning combinatorial peptide libraries (PSCPL) to develop a highly efficient, universal and unbiased approach to address MHC specificity. The PSCPL approach appeared qualitatively and quantitatively superior to other currently used strategies. The average effect of any amino acid in each position was quantitated, allowing a detailed description of extended peptide binding motifs including primary and secondary anchor residues. It also identified disfavored residues which were found to be surprisingly important in shaping MHC class I specificity. Assuming that MHC class I specificity is the result of largely independently acting subsites, the binding of unknown peptides could be predicted. Conversely, this argues that MHC class I specificities consist of an array of subspecificities acting in a combinatorial mode.

摘要

相当多的研究兴趣集中在理解主要组织相容性复合体(MHC)特异性是如何产生的,以及表征MHC分子的特异性,最终目标是预测肽结合。我们采用了一种策略,即将特定大小的所有可能肽段分布到位置扫描组合肽库(PSCPL)中,以开发一种高效、通用且无偏差的方法来解决MHC特异性问题。PSCPL方法在定性和定量方面均优于目前使用的其他策略。对每个位置上任何氨基酸的平均效应进行了定量分析,从而能够详细描述扩展肽结合基序,包括一级和二级锚定残基。它还鉴定出了不利残基,这些残基在塑造MHC I类特异性方面被证明具有惊人的重要性。假设MHC I类特异性是大量独立作用的亚位点的结果,那么未知肽的结合就可以被预测。相反,这表明MHC I类特异性由一系列以组合模式起作用的亚特异性组成。

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