促进网络药理学驱动的COVID-19研究的综合资源工作流程。

A Workflow of Integrated Resources to Catalyze Network Pharmacology Driven COVID-19 Research.

作者信息

Zahoránszky-Kőhalmi Gergely, Siramshetty Vishal B, Kumar Praveen, Gurumurthy Manideep, Grillo Busola, Mathew Biju, Metaxatos Dimitrios, Backus Mark, Mierzwa Tim, Simon Reid, Grishagin Ivan, Brovold Laura, Mathé Ewy A, Hall Matthew D, Michael Samuel G, Godfrey Alexander G, Mestres Jordi, Jensen Lars J, Oprea Tudor I

机构信息

National Center for Advancing Translational Sciences, Rockville, MD, USA.

Department of Internal Medicine, University of New Mexico School of Medicine, Albuquerque, NM, USA.

出版信息

bioRxiv. 2020 Nov 5:2020.11.04.369041. doi: 10.1101/2020.11.04.369041.

DOI:10.1101/2020.11.04.369041

PMID:33173863

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7654851/

Abstract

MOTIVATION

In the event of an outbreak due to an emerging pathogen, time is of the essence to contain or to mitigate the spread of the disease. Drug repositioning is one of the strategies that has the potential to deliver therapeutics relatively quickly. The SARS-CoV-2 pandemic has shown that integrating critical data resources to drive drug-repositioning studies, involving host-host, hostpathogen and drug-target interactions, remains a time-consuming effort that translates to a delay in the development and delivery of a life-saving therapy.

RESULTS

Here, we describe a workflow we designed for a semi-automated integration of rapidly emerging datasets that can be generally adopted in a broad network pharmacology research setting. The workflow was used to construct a COVID-19 focused multimodal network that integrates 487 host-pathogen, 74,805 host-host protein and 1,265 drug-target interactions. The resultant Neo4j graph database named "Neo4COVID19" is accessible via a web interface and via API calls based on the Bolt protocol. We believe that our Neo4COVID19 database will be a valuable asset to the research community and will catalyze the discovery of therapeutics to fight COVID-19.

AVAILABILITY

https://neo4covid19.ncats.io.

摘要

动机

在因新出现的病原体而爆发疫情的情况下，控制或减轻疾病传播的时间至关重要。药物重新定位是有可能相对快速地提供治疗方法的策略之一。2019冠状病毒病大流行表明，整合关键数据资源以推动药物重新定位研究，涉及宿主-宿主、宿主-病原体和药物-靶点相互作用，仍然是一项耗时的工作，这导致挽救生命疗法的开发和交付延迟。

结果

在此，我们描述了一种为快速出现的数据集的半自动整合而设计的工作流程，该流程可在广泛的网络药理学研究环境中普遍采用。该工作流程用于构建一个以2019冠状病毒病为重点的多模态网络，该网络整合了487个宿主-病原体、74,805个宿主-宿主蛋白质和1,265个药物-靶点相互作用。由此产生的名为“Neo4COVID19”的Neo4j图形数据库可通过基于Bolt协议的Web界面和API调用访问。我们相信，我们的Neo4COVID19数据库将成为研究界的宝贵资产，并将促进对抗2019冠状病毒病疗法的发现。