Li Rang, Wilderotter Sabrina, Stoddard Madison, Van Egeren Debra, Chakravarty Arijit, Joseph-McCarthy Diane
Department of Biomedical Engineering, Boston University, Boston, MA, United States.
Fractal Therapeutics Inc., Cambridge, MA, United States.
Front Bioinform. 2024 Feb 23;4:1295972. doi: 10.3389/fbinf.2024.1295972. eCollection 2024.
A fundamental challenge in computational vaccinology is that most B-cell epitopes are conformational and therefore hard to predict from sequence alone. Another significant challenge is that a great deal of the amino acid sequence of a viral surface protein might not in fact be antigenic. Thus, identifying the regions of a protein that are most promising for vaccine design based on the degree of surface exposure may not lead to a clinically relevant immune response. Linear peptides selected by phage display experiments that have high affinity to the monoclonal antibody of interest ("mimotopes") usually have similar physicochemical properties to the antigen epitope corresponding to that antibody. The sequences of these linear peptides can be used to find possible epitopes on the surface of the antigen structure or a homology model of the antigen in the absence of an antigen-antibody complex structure. Herein we describe two novel methods for mapping mimotopes to epitopes. The first is a novel algorithm named MimoTree that allows for gaps in the mimotopes and epitopes on the antigen. More specifically, a mimotope may have a gap that does not match to the epitope to allow it to adopt a conformation relevant for binding to an antibody, and residues may similarly be discontinuous in conformational epitopes. MimoTree is a fully automated epitope detection algorithm suitable for the identification of conformational as well as linear epitopes. The second is an ensemble approach, which combines the prediction results from MimoTree and two existing methods.
计算疫苗学中的一个基本挑战是，大多数B细胞表位是构象性的，因此很难仅从序列来预测。另一个重大挑战是，病毒表面蛋白的大量氨基酸序列实际上可能没有抗原性。因此，基于表面暴露程度来确定蛋白质中最有希望用于疫苗设计的区域，可能不会引发临床相关的免疫反应。通过噬菌体展示实验筛选出的、对目标单克隆抗体具有高亲和力的线性肽（“模拟表位”），通常与对应抗体的抗原表位具有相似的物理化学性质。在没有抗原-抗体复合物结构的情况下，这些线性肽的序列可用于在抗原结构表面或抗原的同源模型上寻找可能的表位。在此，我们描述了两种将模拟表位映射到表位的新方法。第一种是一种名为MimoTree的新算法，它允许模拟表位和抗原上的表位存在缺口。更具体地说，一个模拟表位可能有一个与表位不匹配的缺口，以使其能够形成与抗体结合相关的构象，并且在构象表位中残基可能同样是不连续的。MimoTree是一种完全自动化的表位检测算法，适用于识别构象表位和线性表位。第二种是一种集成方法，它结合了MimoTree和两种现有方法的预测结果。
