title: Proteome-wide prediction of self-interacting proteins based on multiple properties.
abstract: 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 research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six 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). 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.
authors: State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing 100850, China.
authors: Zhongyang
authors: Z
authors: Liu
authors: Feifei
authors: F
authors: Guo
authors: Jiyang
authors: J
authors: Zhang
authors: Jian
authors: J
authors: Wang
authors: Liang
authors: L
authors: Lu
authors: Dong
authors: D
authors: Li
authors: Fuchu
authors: F
authors: He
mesh_terms: D000465:Algorithms; D001665:Binding Sites; D030562:Databases, Protein; D006801:Humans; D016013:Likelihood Functions; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D060066:Protein Interaction Maps; D020543:Proteome / Q000378:metabolism*; D012984:Software
chemical_list: D020543:Proteome