尽管有大量的数据库可用于监管要素,由于缺乏生物信息学工具来预测调控元件的相互作用模式,已经造成了瓶颈。为了缩小这个差距,我们开发了拟南芥转录调控因子域/域相互作用分析工具-液/液相分离(LLPS),低聚,GO分析(艺术基础日志),一个有用的工具包,用于基于结构域-结构域相互作用(DDI)的蛋白质-核酸相互作用(PNI)和蛋白质-蛋白质相互作用(PPI)分析。LLPS,蛋白质寡聚化,蛋白质结构域的结构特性,蛋白质修饰是PPI和PNI时空动态编排的主要组成部分。我们的目标是将PPI/PNI信息整合到预测模型的开发中,以识别桃子中的重要遗传变异。我们的程序基于蛋白质域统一了数据库间关系键,以方便从模型物种推断。该计划的一个关键优势在于相关功能的集成信息,如蛋白质寡聚化,日志分析,域的结构特征(例如,域接头,内在无序的区域,DDIs,结构域-基序(肽)相互作用,β表,和跨膜螺旋),和翻译后修饰。我们提供了简单的测试来演示如何使用这个程序,可以应用于其他真核生物。
Although a large number of databases are available for regulatory elements, a bottleneck has been created by the lack of bioinformatics tools to predict the interaction modes of regulatory elements. To reduce this gap, we developed the Arabidopsis Transcription Regulatory Factor Domain/Domain Interaction Analysis Tool-liquid/liquid phase separation (LLPS), oligomerization, GO analysis (ART FOUNDATION-LOG), a useful toolkit for protein-nucleic acid interaction (PNI) and protein-protein interaction (PPI) analysis based on domain-domain interactions (DDIs). LLPS, protein oligomerization, the structural properties of protein domains, and protein modifications are major components in the orchestration of the spatiotemporal dynamics of PPIs and PNIs. Our goal is to integrate PPI/PNI information into the development of a prediction model for identifying important genetic variants in peaches. Our program unified interdatabase relational keys based on protein domains to facilitate inference from the model species. A key advantage of this program lies in the integrated information of related features, such as protein oligomerization, LOG analysis, structural characterizations of domains (e.g., domain linkers, intrinsically disordered regions, DDIs, domain-motif (peptide) interactions, beta sheets, and transmembrane helices), and post-translational modification. We provided simple tests to demonstrate how to use this program, which can be applied to other eukaryotic organisms.