Department of Physics, Sapienza University and Istituto Pasteur - Fondazione Cenci Bolognetti, 00185 Rome, Italy.
Bioinformatics. 2013 Sep 15;29(18):2285-91. doi: 10.1093/bioinformatics/btt369. Epub 2013 Jun 26.
Antibodies or immunoglobulins are proteins of paramount importance in the immune system. They are extremely relevant as diagnostic, biotechnological and therapeutic tools. Their modular structure makes it easy to re-engineer them for specific purposes. Short of undergoing a trial and error process, these experiments, as well as others, need to rely on an understanding of the specific determinants of the antibody binding mode.
In this article, we present a method to identify, on the basis of the antibody sequence alone, which residues of an antibody directly interact with its cognate antigen. The method, based on the random forest automatic learning techniques, reaches a recall and specificity as high as 80% and is implemented as a free and easy-to-use server, named prediction of Antibody Contacts. We believe that it can be of great help in re-design experiments as well as a guide for molecular docking experiments. The results that we obtained also allowed us to dissect which features of the antibody sequence contribute most to the involvement of specific residues in binding to the antigen.
http://www.biocomputing.it/proABC.
anna.tramontano@uniroma1.it or paolo.marcatili@gmail.com
Supplementary data are available at Bioinformatics online.
抗体或免疫球蛋白是免疫系统中至关重要的蛋白质。它们作为诊断、生物技术和治疗工具具有极其重要的意义。它们的模块化结构使得它们很容易被重新设计用于特定的目的。除了进行反复试验的过程之外，这些实验以及其他实验都需要依赖于对抗体结合模式的特定决定因素的理解。
在本文中，我们提出了一种仅基于抗体序列来识别抗体中哪些残基与相应抗原直接相互作用的方法。该方法基于随机森林自动学习技术，召回率和特异性高达 80%，并实现为一个免费且易于使用的服务器，名为抗体接触预测。我们相信，它可以在重新设计实验以及指导分子对接实验方面提供很大的帮助。我们得到的结果还允许我们剖析抗体序列的哪些特征对特定残基参与与抗原结合的贡献最大。
http://www.biocomputing.it/proABC。
anna.tramontano@uniroma1.it 或 paolo.marcatili@gmail.com
补充数据可在Bioinformatics 在线获得。
