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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >A SURVEY OF COMPUTATIONAL METHODS FOR PROTEIN COMPLEX PREDICTION FROM PROTEIN INTERACTION NETWORKS
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A SURVEY OF COMPUTATIONAL METHODS FOR PROTEIN COMPLEX PREDICTION FROM PROTEIN INTERACTION NETWORKS

机译：蛋白质相互作用网络预测蛋白质复杂度的方法综述

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摘要

Complexes of physically interacting proteins are one of the fundamental functional units responsible for driving key biological mechanisms within the cell. Their identification is therefore necessary to understand not only complex formation but also the higher level organization of the cell. With the advent of "high-throughput" techniques in molecular biology, significant amount of physical interaction data has been cataloged from organisms such as yeast, which has in turn fueled computational approaches to systematically mine complexes from the network of physical interactions among proteins (PPI network). In this survey, we review, classify and evaluate some of the key computational methods developed till date for the identification of protein complexes from PPI networks. We present two insightful taxonomies that reflect how these methods have evolved over the years toward improving automated complex prediction. We also discuss some open challenges facing accurate reconstruction of complexes, the crucial ones being the presence of high proportion of errors and noise in current high-throughput datasets and some key aspects overlooked by current complex detection methods. We hope this review will not only help to condense the history of computational complex detection for easy reference but also provide valuable insights to drive further research in this area.
机译：物理相互作用蛋白的复合物是负责驱动细胞内关键生物学机制的基本功能单元之一。因此，对它们的鉴定不仅对理解复杂的形成而且对细胞的更高层次的组织都是必需的。随着分子生物学中“高通量”技术的出现，已经从诸如酵母等生物中收集了大量的物理相互作用数据，从而为从蛋白质之间的物理相互作用网络（PPI）系统地挖掘复合物提供了动力网络）。在这项调查中，我们回顾，分类和评估了迄今为止开发的一些用于从PPI网络识别蛋白质复合物的关键计算方法。我们提出了两种有见地的分类法，它们反映了这些方法多年来如何改进自动复杂预测。我们还讨论了复杂物的准确重建所面临的一些开放挑战，其中关键的挑战是当前高通量数据集中存在高比例的错误和噪声，以及当前复杂物检测方法所忽略的一些关键方面。我们希望这篇评论不仅有助于浓缩计算复杂检测的历史以供参考，而且可以提供宝贵的见解，以推动该领域的进一步研究。

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