描述抗多肽抗体和内在无序肽抗原之间反应约束的聚合物理论方法的发展:对b细胞表位预测的影响

S. Caoili
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引用次数: 0

摘要

b细胞表位预测的目的是支持翻译应用,例如基于肽的疫苗设计。这需要选择免疫肽序列,这些序列往往是内在无序的,因此在聚合物理论的框架内适当地描述。一个完全扩展的六肽序列跨越一个典型的抗体足迹;但无序肽是柔性的而不是刚性的,因此它们的b细胞表位可能根据抗体结合时假设的构象的多样性而变化长度。因此,本文将多肽建模为蠕虫状链,使用径向概率密度分布函数的插值近似来估计多肽序列的末端被小于或等于典型抗体足迹直径的距离分开的概率。结果表明,这些表位的长度可能不超过17个残基,这与现有的由与同源肽抗原结合的抗肽抗体组成的免疫复合物的结构数据一致。因此,对于这些抗原,b细胞表位预测可以进行初始扫描,寻找长度达到物理化学上合理最大值(例如,17个残基)的内在无序序列,并对逐渐增加的子序列进行分析,以识别非冗余的假定表位集(例如,基于预测的亲和力)。
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Development of a Polymer-Theoretic Approach to Describing Constraints on Reactions Between Antipeptide Antibodies and Intrinsically Disordered Peptide Antigens: Implications for B-Cell Epitope Prediction
B-cell epitope prediction aims to support translational applications as exemplified by peptide-based vaccine design. This entails selection of immunizing peptide sequences that tend to be intrinsically disordered and thus appropriately described within the framework of polymer theory. A fully extended hexapeptide sequence spans a typical antibody footprint; but disordered peptides are flexible rather than rigid, such that their B-cell epitopes may vary in length according to the diversity of conformations assumed upon binding by antibodies. Hence, peptides were modeled herein as worm-like chains, using an interpolated approximation of the radial probability density distribution function to estimate the probability that the ends of a peptidic sequence are separated by a distance less than or equal to a typical antibody footprint diameter. The results suggest that the epitopes are likely to be no more than 17 residues long, which is consistent with available structural data on immune complexes consisting of antipeptide antibodies bound to cognate peptide antigens. For such antigens, B-cell epitope prediction thus could proceed with initial scanning for intrinsically disordered sequences of length up to a physicochemically plausible maximum value (e.g., 17 residues), with analysis of progressively longer subsequences to identify nonredundant sets of putative epitopes (e.g., based on predicted affinity).
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