Prediction of peptide reactivity with human IVIg through a knowledge-based approach.
Prediction of peptide reactivity with human IVIg through a knowledge-based approach.
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DOI：
10.1371/journal.pone.0023616
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发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Bellazzi R
中科院分区：
文献类型：
--
作者：
Barbarini N;Tiengo A;Bellazzi R
由于抗体结构的巨大变异性，预测抗体-蛋白（抗原）相互作用是非常困难的。与抗体结合的抗原区域称为抗原表位。实验数据表明，许多抗体与一组不同的表位反应（阳性反应）。DREAM5的挑战1旨在了解是否存在预测肽/表位反应性的规则，即其与人类抗体结合的能力。DREAM 5提供了一组训练肽，实验鉴定了对人抗体的高反应性和低反应性。在这个训练集的基础上，挑战的参与者被要求开发一个反应性的预测模型。然后提供一个测试集来评估到目前为止实现的模型的性能。我们开发了一个逻辑回归模型来预测肽的反应性，通过面对机器学习问题的挑战。初始特征是根据可用知识和数据集中报告的信息生成的。我们的预测模型在挑战中表现第二好。我们还开发了一种基于聚类方法的方法，能够根据DREAM5“奖金轮”额外挑战的要求“在计算机上”生成阳性和阴性新肽序列列表。本文从反应性预测的角度描述了开发的模型及其结果，并强调了有关肽与人类抗体反应倾向的一些开放问题。
The prediction of antibody-protein (antigen) interactions is very difficult due to the huge variability that characterizes the structure of the antibodies. The region of the antigen bound to the antibodies is called epitope. Experimental data indicate that many antibodies react with a panel of distinct epitopes (positive reaction). The Challenge 1 of DREAM5 aims at understanding whether there exists rules for predicting the reactivity of a peptide/epitope, i.e., its capability to bind to human antibodies. DREAM 5 provided a training set of peptides with experimentally identified high and low reactivities to human antibodies. On the basis of this training set, the participants to the challenge were asked to develop a predictive model of reactivity. A test set was then provided to evaluate the performance of the model implemented so far. We developed a logistic regression model to predict the peptide reactivity, by facing the challenge as a machine learning problem. The initial features have been generated on the basis of the available knowledge and the information reported in the dataset. Our predictive model had the second best performance of the challenge. We also developed a method, based on a clustering approach, able to “in-silico” generate a list of positive and negative new peptide sequences, as requested by the DREAM5 “bonus round” additional challenge. The paper describes the developed model and its results in terms of reactivity prediction, and highlights some open issues concerning the propensity of a peptide to react with human antibodies.
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DOI：
10.1016/j.bbrc.2004.05.066
发表时间：
2004-07-02
影响因子：
3.1
作者：
Hua R;Zhou Y;Wang Y;Hua Y;Tong G
通讯作者：
Tong G
影响因子：
6.8
作者：
AKAIKE, H
通讯作者：
AKAIKE, H
DOI：
10.1073/pnas.78.6.3824
发表时间：
1981-01-01
期刊：
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA-BIOLOGICAL SCIENCES
影响因子：
--
作者：
HOPP, TP;WOODS, KR
通讯作者：
WOODS, KR
影响因子：
1.7
作者：
Kaczanowski, Szymon;Zielenkiewicz, Piotr
通讯作者：
Zielenkiewicz, Piotr
影响因子：
64.8
作者：
JANIN, J
通讯作者：
JANIN, J