Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning.
Motifier: An IgOme Profiler Based on Peptide Motifs Using Machine Learning.
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DOI：
10.1016/j.jmb.2021.167071
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发表时间：
2021-07-23
影响因子：
5.6
通讯作者：
Pupko T
中科院分区：
文献类型：
--
作者：
Ashkenazy H;Avram O;Ryvkin A;Roitburd-Berman A;Weiss-Ottolenghi Y;Hada-Neeman S;Gershoni JM;Pupko T
抗体提供了免疫系统遭遇威胁和侮辱的全面记录。检查血清中抗体库并发现那些最能代表各种临床情况的“区别特征”的抗体的能力可能非常有用。最近，噬菌体展示技术与新一代测序（NGS）相结合，产生了一种强大的实验方法，称为“Deep-Panning”，其中探测血清抗体的光谱。为了从Deep-Panning生成的数千万亲和选择肽中提取有意义的生物学见解，先进的生物信息学算法是必须的。在这项研究中，我们描述了Motifier，一个由一组算法组成的计算管道，该算法基于Deep-Panning识别的亲和选择肽系统地生成歧视性肽基序。这些基序被证明可以有效地表征抗体结合活性，并且通过机器学习协议的实现可以准确地分类代表各种生物条件的复杂抗体混合物。
Antibodies provide a comprehensive record of the encounters with threats and insults to the immune system. The ability to examine the repertoire of antibodies in serum and discover those that best represent “discriminating features” characteristic of various clinical situations, is potentially very useful. Recently, phage display technologies combined with Next-Generation Sequencing (NGS) produced a powerful experimental methodology, coined “Deep-Panning”, in which the spectrum of serum antibodies is probed. In order to extract meaningful biological insights from the tens of millions of affinity-selected peptides generated by Deep-Panning, advanced bioinformatics algorithms are a must. In this study, we describe Motifier, a computational pipeline comprised of a set of algorithms that systematically generates discriminatory peptide-motifs based on the affinity-selected peptides identified by Deep-Panning. These motifs are shown to effectively characterize antibody binding activities and through the implementation of machine-learning protocols are shown to accurately classify complex antibody mixtures representing various biological conditions.
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影响因子：
4.4
作者：
Chen, Xi;Ishwaran, Hemant
通讯作者：
Ishwaran, Hemant
DOI：
10.1093/bioinformatics/bts565
发表时间：
2012-12-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Fu L;Niu B;Zhu Z;Wu S;Li W
通讯作者：
Li W
影响因子：
11
作者：
Aghebati-Maleki L;Bakhshinejad B;Baradaran B;Motallebnezhad M;Aghebati-Maleki A;Nickho H;Yousefi M;Majidi J
通讯作者：
Majidi J
影响因子：
3.7
作者：
Hurwitz, Amy M.;Huang, Wanzhi;Palzkill, Timothy
通讯作者：
Palzkill, Timothy
影响因子：
8
作者：
Halperin, I;Wolfson, H;Nussinov, R
通讯作者：
Nussinov, R