生物通报道：来自北京蛋白质组研究中心，北京协和医学院等处的研究人员发表了题为“Proteome-wide prediction of self-interacting proteins based on multiple properties”的文章，研发出了一种在线分析工具SLIPPER (SeLf-Interacting Protein PrEdictoR) ，利用这一工具，研究人员可以从整体水平上理解自相互作用蛋白在细胞功能中的角色。相关成果公布在MOLECULAR ＆ CELLULAR PROTEOMICS杂志上。

文章的通讯作者是北京蛋白质组研究中心的贺福初院士，贺福初院士主要从事基因组学、蛋白质组学与生物信息学研究，曾获得了多项重要研究成果，如倡导并领衔了人类第一个组织、器官的“肝脏蛋白质组计划”，这也是中国第一次领导大型国际合作计划。

所谓自相互作用蛋白（Self-interacting proteins）是指自身两个或者两个以上拷贝能够发生相互作用的蛋白，这种蛋白在细胞功能和蛋白相互作用PINs进化方面扮演了重要的角色。

了解是否一个蛋白能够发生自相互作用，将有助于揭示其功能，并且有时对于揭示其功能，可以起到非常关键的作用。之前分析这种蛋白的研究主要聚焦于某些自相互作用蛋白的结构和功能，但是对于其整体属性了解得并不多。而且目前最常见的两种高通量蛋白相互作用检测技术并无法准确检测蛋白自相互作用，相关的生物信息学预测方法更是十分缺乏。

为此，在这篇文章中，贺福初院士开发出了一种在线分析工具SLIPPER (SeLf-Interacting Protein PrEdictoR) ，利用这一工具，研究人员可以从整体水平上理解自相互作用蛋白在细胞功能中的角色。

这一工具是在此前PRINCESS蛋白作用数据分析基础上发展起来的，研究人员从整体的角度通过系统地分析并预测自相互作用的蛋白，首先他们发现自相互作用的蛋白，在结构上与其它蛋白相比，包含更多的结构域。在进化方面也更倾向于保守和古老，在功能方面，则包含更多的酶基因，管家基因和药物靶标，在拓扑结构上更是在PINs中占据了重要地位。

根据这些特点，研究人员利用逻辑回归分析方法，研发出了一种全蛋白质组学范围的预测模型。并通过了五倍交叉验证和一个独立验证实验，证明了这一模型的有效性。由此研发出首个自相互作用蛋白预测模型，并将其发展成为一个用户友好的在线分析工具。

利用这一工具，研究人员能更好的了解自我相互作用蛋白，并且这一模型也将能用于自相互作用蛋白高通量研究，以及为解析其功能提供更多的线索。

目前由中国生物化学与分子生物学会蛋白质组学专业委员会（CNHUPO）主办的第八届中国蛋白质组学大会已接受报名，这一会议将于9月在重庆市召开，有兴趣的读者可以进一步了解：第八届中国蛋白质组学大会将于2013年9月召开

原文摘要：

Proteome-wide prediction of self-interacting proteins based on multiple properties.

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related researches mainly focus on the structures and functions of specific self-interacting proteins, while knowledge on their overall properties is limited. Meanwhile two current most common high-throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, while bioinformatic prediction method of self-interacting proteins is lacking. This paper aims to systematically study and predict self-interacting proteins from an overall perspective. First, we find that compared with other proteins, self-interacting proteins in structural aspect contain more domains, in evolutionary aspect tend to be conserved and ancient, in functional aspect are significantly enriched with enzyme genes, housekeeping genes and drug-targets, and in topological aspect tend to occupy important positions in PINs. Further based on these features, after feature selection, we use logistic regression to integrate 6 representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally the prediction model is developed into a user-friendly web-service SLIPPER (SeLf-Interacting Protein PrEdictoR) (http://www.bprc.ac.cn/slipper). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high-throughput finding of self-interacting proteins and provide clues for elucidating their functions.