Predicting antigenic determinants in proteins: looking for unidimensional solutions to a three-dimensional problem?

Peptide research Pub Date : 1994-07-01
M H Van Regenmortel, J L Pellequer
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Abstract

In a recent review, Hopp (Peptide Research 6:183-190, 1993) claimed that the Hopp and Woods hydrophilicity method for locating antigenic determinants is superior to all other existing methods for predicting the B cell epitopes of proteins but that it is not useful to aid the investigator in producing peptide-protein cross-reactive antisera. In this article, we challenge both these assertions. Most investigators utilize antigenicity prediction algorithms because they wish to produce anti-peptide antibodies capable of cross-reacting with the intact protein. All prediction methods are based on propensity scales for the 20 amino acids, which describe the tendency of each residue to be associated with properties such as hydrophilicity, surface accessibility or segmental mobility. When we compared the prediction efficacy of 22 different scales, taking into account both correct and incorrect predictions, we found that none of the scales gave a level of correct prediction higher than about 50%-60%. If no antigenicity was found in a particular region of the protein, we took the view that hydrophilicity peaks located in that region amounted to wrong predictions. The much higher success rate reported by Hopp for this method stems from the way he assesses prediction efficacy, i.e., by counting the number of known epitopes located inside and outside hydrophilicity peaks. Reasons for the low success rate of antigenicity prediction are discussed. In most cases, it is unrealistic to try to reduce the complexity of discontinuous, conformational epitopes to simple, linear peptide models.

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预测蛋白质中的抗原决定因素:寻找三维问题的一维解决方案?
在最近的一篇综述中,Hopp (Peptide Research 6:183- 190,1993)声称,Hopp和Woods的亲水性方法定位抗原决定因子优于所有其他现有的预测蛋白质B细胞表位的方法,但它无助于帮助研究者生产肽-蛋白交叉反应抗血清。在本文中,我们将挑战这两个断言。大多数研究人员利用抗原性预测算法,因为他们希望产生能够与完整蛋白交叉反应的抗肽抗体。所有的预测方法都是基于20种氨基酸的倾向量表,它描述了每个残基与诸如亲水性、表面可及性或片段迁移性等特性相关的趋势。当我们比较22种不同量表的预测效能时,考虑到正确和不正确的预测,我们发现没有一个量表给出的正确预测水平高于约50%-60%。如果在蛋白质的特定区域没有发现抗原性,我们认为位于该区域的亲水性峰相当于错误的预测。Hopp报告的这种方法的高得多的成功率源于他评估预测效果的方式,即通过计算位于亲水性峰内外的已知表位的数量。讨论了抗原性预测成功率低的原因。在大多数情况下,试图将不连续构象表位的复杂性降低到简单的线性肽模型是不现实的。
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