Perfetto Livia, Briganti Leonardo, Calderone Alberto, Cerquone Perpetuini Andrea, Iannuccelli Marta, Langone Francesca, Licata Luana, Marinkovic Milica, Mattioni Anna, Pavlidou Theodora, Peluso Daniele, Petrilli Lucia Lisa, Pirrò Stefano, Posca Daniela, Santonico Elena, Silvestri Alessandra, Spada Filomena, Castagnoli Luisa, Cesareni Gianni
Department of Biology, University of Rome Tor Vergata, Rome, Italy.
Department of Biology, University of Rome Tor Vergata, Rome, Italy
Nucleic Acids Res. 2016 Jan 4;44(D1):D548-54. doi: 10.1093/nar/gkv1048. Epub 2015 Oct 13.
Assembly of large biochemical networks can be achieved by confronting new cell-specific experimental data with an interaction subspace constrained by prior literature evidence. The SIGnaling Network Open Resource, SIGNOR (available on line at http://signor.uniroma2.it), was developed to support such a strategy by providing a scaffold of prior experimental evidence of causal relationships between biological entities. The core of SIGNOR is a collection of approximately 12,000 manually-annotated causal relationships between over 2800 human proteins participating in signal transduction. Other entities annotated in SIGNOR are complexes, chemicals, phenotypes and stimuli. The information captured in SIGNOR can be represented as a signed directed graph illustrating the activation/inactivation relationships between signalling entities. Each entry is associated to the post-translational modifications that cause the activation/inactivation of the target proteins. More than 4900 modified residues causing a change in protein concentration or activity have been curated and linked to the modifying enzymes (about 351 human kinases and 94 phosphatases). Additional modifications such as ubiquitinations, sumoylations, acetylations and their effect on the modified target proteins are also annotated. This wealth of structured information can support experimental approaches based on multi-parametric analysis of cell systems after physiological or pathological perturbations and to assemble large logic models.
通过将新的细胞特异性实验数据与受先前文献证据约束的相互作用子空间进行对比,可实现大型生化网络的组装。信号网络开放资源库SIGNOR(可在线访问http://signor.uniroma2.it)就是为支持这一策略而开发的,它提供了生物实体之间因果关系的先前实验证据框架。SIGNOR的核心是一个包含约12,000条手动注释的因果关系的集合,这些关系涉及参与信号转导的2800多种人类蛋白质。SIGNOR中注释的其他实体包括复合物、化学物质、表型和刺激。SIGNOR中捕获的信息可以表示为一个带符号的有向图,说明信号实体之间的激活/失活关系。每个条目都与导致靶蛋白激活/失活的翻译后修饰相关。已经整理了4900多个导致蛋白质浓度或活性变化的修饰残基,并将其与修饰酶(约351种人类激酶和94种磷酸酶)相关联。还注释了诸如泛素化、SUMO化、乙酰化等其他修饰及其对修饰靶蛋白的影响。这些丰富的结构化信息可以支持基于生理或病理扰动后细胞系统多参数分析的实验方法,并有助于组装大型逻辑模型。