基于计算免疫蛋白质组学的 COVID-19 靶点研究策略
Computational Immune Proteomics Approach to Target COVID-19.
机构信息
Department of Health Sciences, University "Magna Graecia" of Catanzaro, Catanzaro 88100, Italy.
Department of Basic Biotechnological Sciences, Intensivological and Perioperative Clinics, Università Cattolica del Sacro Cuore, Roma 00168, Italy.
出版信息
J Proteome Res. 2020 Nov 6;19(11):4233-4241. doi: 10.1021/acs.jproteome.0c00553. Epub 2020 Sep 23.
Progress of the omics platforms widens their application to diverse fields, including immunology. This enables a deeper level of knowledge and the provision of a huge amount of data for which management and fruitful integration with the past evidence requires a steadily growing computational effort. In light of this, immunoinformatics emerges as a new discipline placed in between the traditional lab-based investigations and the computational analysis of the biological data. Immunoinformatics make use of tailored bioinformatics tools and data repositories to facilitate the analysis of data from a plurality of disciplines and help drive novel research hypotheses and in silico screening investigations in a fast, reliable, and cost-effective manner. Such computational immunoproteomics studies may as well prepare and guide lab-based investigations, representing valuable technology for the investigation of novel pathogens, to tentatively evaluate specificity of diagnostic products, to forecast on potential adverse effects of vaccines and to reduce the use of animal models. The present manuscript provides an overview of the COVID-19 pandemic and reviews the state of the art of the omics technologies employed in fighting SARS-CoV-2 infections. A comprehensive description of the immunoinformatics approaches and its potential role in contrasting COVID-19 pandemics is provided.
组学平台的发展拓宽了其在包括免疫学在内的多个领域的应用。这使得人们能够更深入地了解相关知识,并提供大量的数据,而这些数据的管理和与以往证据的有效整合需要不断增加的计算工作量。有鉴于此,免疫信息学应运而生,它处于传统的实验室研究和生物数据的计算分析之间的新兴学科。免疫信息学利用定制的生物信息学工具和数据库,促进来自多个学科的数据的分析,并有助于以快速、可靠且经济有效的方式驱动新的研究假设和计算机筛选研究。此类计算免疫蛋白质组学研究还可以为基于实验室的研究提供准备和指导,是研究新型病原体的宝贵技术,可用于初步评估诊断产品的特异性、预测疫苗的潜在不良反应,并减少对动物模型的使用。本文概述了 COVID-19 大流行,并回顾了用于对抗 SARS-CoV-2 感染的组学技术的最新进展。本文还全面描述了免疫信息学方法及其在对抗 COVID-19 大流行中的潜在作用。