IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.
IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.
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DOI：
10.1038/s41598-018-28815-x
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发表时间：
2018-07-12
影响因子：
4.6
通讯作者：
Veljkovic N
中科院分区：
文献类型：
--
作者：
Perovic V;Sumonja N;Marsh LA;Radovanovic S;Vukicevic M;Roberts SGE;Veljkovic N
固有无序蛋白(IDPs)的特征是缺乏固定的三级结构，通过与多个蛋白质伙伴结合参与调节关键的生物学过程。国内流离失所者具有可塑性，可以适应不同结构的合作伙伴，这种灵活性源于主要结构中编码的特征。普遍的序列信息将有助于覆盖人类相互作用组的稀疏区，这一假设促使我们探索基于序列特征预测涉及IDPs的蛋白质-蛋白质相互作用(PPI)的可能性。我们开发了一种方法，该方法依赖于相互作用和非相互作用的蛋白质对的特征，并利用机器学习来分类和预测IDP PPI。考虑到构象组织特有的序列决定因素和训练阶段内身份识别程序相互作用的多样性，确保了一种可靠的方法，该方法优于目前最先进的方法。通过应用严格的评估程序，我们确认我们的方法甚至在蛋白质组尺度上预测了感兴趣的IDP的相互作用。这项服务是作为网络工具提供的，以便以更高的效率加快发现新的互动和身份识别功能。
Intrinsically disordered proteins (IDPs) are characterized by the lack of a fixed tertiary structure and are involved in the regulation of key biological processes via binding to multiple protein partners. IDPs are malleable, adapting to structurally different partners, and this flexibility stems from features encoded in the primary structure. The assumption that universal sequence information will facilitate coverage of the sparse zones of the human interactome motivated us to explore the possibility of predicting protein-protein interactions (PPIs) that involve IDPs based on sequence characteristics. We developed a method that relies on features of the interacting and non-interacting protein pairs and utilizes machine learning to classify and predict IDP PPIs. Consideration of both sequence determinants specific for conformational organizations and the multiplicity of IDP interactions in the training phase ensured a reliable approach that is superior to current state-of-the-art methods. By applying a strict evaluation procedure, we confirm that our method predicts interactions of the IDP of interest even on the proteome-scale. This service is provided as a web tool to expedite the discovery of new interactions and IDP functions with enhanced efficiency.
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影响因子：
3.9
作者：
Cuchillo, Remi;Michel, Julien
通讯作者：
Michel, Julien
影响因子：
5.4
作者：
Forsova, Oksana S.;Zakharov, Vladislav V.
通讯作者：
Zakharov, Vladislav V.
影响因子：
2.9
作者：
Dunker, AK;Lawson, JD;Obradovic, Z
通讯作者：
Obradovic, Z
影响因子：
5.8
作者：
Galzitskaya, Oxana V.;Garbuzynskiy, Sergiy O.;Lobanov, Michail Yu.
通讯作者：
Lobanov, Michail Yu.
DOI：
10.1073/pnas.0812101106
发表时间：
2009-04-07
影响因子：
11.1
作者：
Hartl, Markus;Nist, Andrea;Bister, Klaus
通讯作者：
Bister, Klaus