Protein-to-Protein Interactions: Technologies, Databases, and Algorithms
Protein-to-Protein Interactions: Technologies, Databases, and Algorithms
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DOI：
10.1145/1824795.1824796
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发表时间：
2010-11-01
影响因子：
16.6
通讯作者：
Veltri, Pierangelo
中科院分区：
文献类型：
--
作者：
Cannataro, Mario;Guzzi, Pietro H.;Veltri, Pierangelo
研究蛋白质及其结构对于理解蛋白质功能具有重要作用。最近，由于蛋白质组学获得的重要结果，人们对相互作用组学产生了极大的兴趣，即蛋白质与蛋白质相互作用的研究，称为 PPI，或者更一般地说，大分子之间的相互作用，特别是细胞内的相互作用。相互作用组学意味着研究、建模、存储和检索蛋白质之间的相互作用以及用于操纵、模拟和预测相互作用的算法。 PPI 数据可以从研究相互作用的生物实验中获得。利用图论和图数据管理可以实现对PPI的建模和存储，从而可以查询图数据库进行进一步的实验。 PPI 图可用作数据挖掘算法的输入，其中原始数据是形成交互图的二元交互，分析算法检索蛋白质之间的生物相互作用（即 PPI 生物学意义）。例如，可以通过挖掘数据库中存储的相互作用网络来预测两个或多个蛋白质之间的相互作用。在本文中，我们调查了 PPI 数据的建模、存储、分析和操作。在描述了主要基于图的主要 PPI 模型之后，本文回顾了 PPI 数据表示和存储以及 PPI 数据库。深入讨论了用于分析和管理 PPI 网络的算法和软件工具。本文最后讨论了 PPI 网络的主要挑战和研究方向。
Studying proteins and their structures has an important role for understanding protein functionalities. Recently, due to important results obtained with proteomics, a great interest has been given to interactomics, that is, the study of protein-to-protein interactions, called PPI, or more generally, interactions among macromolecules, particularly within cells. Interactomics means studying, modeling, storing, and retrieving protein-to-protein interactions as well as algorithms for manipulating, simulating, and predicting interactions. PPI data can be obtained from biological experiments studying interactions. Modeling and storing PPIs can be realized by using graph theory and graph data management, thus graph databases can be queried for further experiments. PPI graphs can be used as input for data-mining algorithms, where raw data are binary interactions forming interaction graphs, and analysis algorithms retrieve biological interactions among proteins (i.e., PPI biological meanings). For instance, predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases. In this article we survey modeling, storing, analyzing, and manipulating PPI data. After describing the main PPI models, mostly based on graphs, the article reviews PPI data representation and storage, as well as PPI databases. Algorithms and software tools for analyzing and managing PPI networks are discussed in depth. The article concludes by discussing the main challenges and research directions in PPI networks.