Suppr超能文献

预测蛋白质-蛋白质相互作用位点的进展与挑战。

Progress and challenges in predicting protein-protein interaction sites.

作者信息

Ezkurdia Iakes, Bartoli Lisa, Fariselli Piero, Casadio Rita, Valencia Alfonso, Tress Michael L

机构信息

Centro Nacional de Biotechnolgia, Spanish National Cancer Research Centre (CNIO), Madrid, Spain.

出版信息

Brief Bioinform. 2009 May;10(3):233-46. doi: 10.1093/bib/bbp021. Epub 2009 Apr 3.

Abstract

The identification of protein-protein interaction sites is an essential intermediate step for mutant design and the prediction of protein networks. In recent years a significant number of methods have been developed to predict these interface residues and here we review the current status of the field. Progress in this area requires a clear view of the methodology applied, the data sets used for training and testing the systems, and the evaluation procedures. We have analysed the impact of a representative set of features and algorithms and highlighted the problems inherent in generating reliable protein data sets and in the posterior analysis of the results. Although it is clear that there have been some improvements in methods for predicting interacting sites, several major bottlenecks remain. Proteins in complexes are still under-represented in the structural databases and in particular many proteins involved in transient complexes are still to be crystallized. We provide suggestions for effective feature selection, and make it clear that community standards for testing, training and performance measures are necessary for progress in the field.

摘要

蛋白质-蛋白质相互作用位点的识别是突变设计和蛋白质网络预测的重要中间步骤。近年来,已开发出大量方法来预测这些界面残基,在此我们综述该领域的现状。该领域的进展需要清楚了解所应用的方法、用于训练和测试系统的数据集以及评估程序。我们分析了一组具有代表性的特征和算法的影响,并强调了在生成可靠蛋白质数据集以及结果的后续分析中存在的固有问题。尽管很明显预测相互作用位点的方法已有一些改进,但仍存在几个主要瓶颈。复合物中的蛋白质在结构数据库中的代表性仍然不足,特别是许多参与瞬时复合物的蛋白质仍有待结晶。我们提供有效特征选择的建议,并明确指出测试、训练和性能测量的社区标准对于该领域的进展是必要的。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验