Independent Researcher, Jijamata Nagar, Hingoli, India.
Instituto de Neurobiología, Universidad Nacional Autónoma de México, Querétaro, México.
Methods Mol Biol. 2023;2690:429-443. doi: 10.1007/978-1-0716-3327-4_33.
Functional annotation is lacking for over half of the proteins encoded in genomes and model or representative organisms are not an exception to this trend. One of the popular ways of assigning putative functions to uncharacterized proteins is based on the functions of well-characterized proteins that physically interact with them, i.e., guilt-by-association or functional context approach. In the last two decades, several powerful experimental and computational techniques have been used to determine protein-protein interactions (PPIs) at genome level and are made available through many public databases. The PPI data are often complex and heterogeneously represented across databases posing unique challenges in retrieving, integrating, and analyzing the data even for trained computational biologists, the end users-experimental biologists often struggle to work around the data for the protein of their interests. This chapter provides stepwise protocols to import interaction network of the protein of interest in Cytoscape using PSICQUIC, stringApp, and IntAct App. These are next-generation applications that import PPI from multiple databases/resources and provide seamless functions to study the protein of interest and its functional context directly in Cytoscape.
功能注释对于基因组和模型或代表性生物中编码的蛋白质的一半以上都是缺乏的，这种趋势也不例外。将假定功能分配给特征不明的蛋白质的一种流行方法是基于与它们物理相互作用的特征良好的蛋白质的功能，即牵连或功能背景方法。在过去的二十年中，已经使用了几种强大的实验和计算技术来确定基因组水平的蛋白质-蛋白质相互作用（PPIs），并通过许多公共数据库提供。PPIs 数据通常复杂且在数据库之间呈现出异构性，即使对于受过训练的计算生物学家来说，在检索、集成和分析数据方面也带来了独特的挑战，最终用户——实验生物学家经常难以围绕他们感兴趣的蛋白质的数据进行工作。本章提供了使用 PSICQUIC、stringApp 和 IntAct App 将感兴趣的蛋白质的相互作用网络导入 Cytoscape 的逐步方案。这些是下一代应用程序，可从多个数据库/资源导入 PPI，并提供无缝功能，可直接在 Cytoscape 中研究感兴趣的蛋白质及其功能背景。
