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抗体 i-Patch 预测抗体结合位点可改善刚性局部抗体-抗原对接。

Antibody i-Patch prediction of the antibody binding site improves rigid local antibody-antigen docking.

机构信息

Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.

出版信息

Protein Eng Des Sel. 2013 Oct;26(10):621-9. doi: 10.1093/protein/gzt043. Epub 2013 Sep 4.

Abstract

Antibodies are a class of proteins indispensable for the vertebrate immune system. The general architecture of all antibodies is very similar, but they contain a hypervariable region which allows millions of antibody variants to exist, each of which can bind to different molecules. This binding malleability means that antibodies are an increasingly important category of biopharmaceuticals and biomarkers. We present Antibody i-Patch, a method that annotates the most likely antibody residues to be in contact with the antigen. We show that our predictions correlate with energetic importance and thus we argue that they may be useful in guiding mutations in the artificial affinity maturation process. Using our predictions as constraints for a rigid-body docking algorithm, we are able to obtain high-quality results in minutes. Our annotation method and re-scoring system for docking achieve their predictive power by using antibody-specific statistics. Antibody i-Patch is available from http://www.stats.ox.ac.uk/research/proteins/resources.

摘要

抗体是脊椎动物免疫系统不可或缺的一类蛋白质。所有抗体的总体结构非常相似,但它们包含一个超可变区,允许存在数百万种抗体变体,每种变体都可以与不同的分子结合。这种结合的可塑性意味着抗体是生物制药和生物标志物中越来越重要的一类。我们提出了抗体 i-Patch 方法,该方法可以注释最有可能与抗原接触的抗体残基。我们表明,我们的预测与能量重要性相关,因此我们认为它们可能有助于指导人工亲和力成熟过程中的突变。使用我们的预测作为刚性对接算法的约束条件,我们能够在几分钟内获得高质量的结果。我们的对接注释方法和重新评分系统通过使用抗体特异性统计数据来实现其预测能力。抗体 i-Patch 可从 http://www.stats.ox.ac.uk/research/proteins/resources 获得。

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