CATH-FunVar - Predicting Viral and Human Variants Affecting COVID-19 Susceptibility and Severity and Repurposing Therapeutics

CATH-FunVar - 预测影响 COVID-19 易感性和严重程度的病毒和人类变异并重新调整治疗用途

基本信息

批准号：
BB/W003368/1
负责人：
Christine Orengo
金额：
$ 14.89万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FW003368%2F1
关键词：
CATH FunVar Predicting Viral Human

项目摘要

SARS-CoV-2 has caused a pandemic resulting in millions of deaths worldwide and significant social and economic disruption. Although vaccine trials have been encouraging vaccines must be distributed globally and therapeutic interventions will be needed for some time. It is clear that some human populations are much more vulnerable to the disease. For example older men and black and Asian communities. The factors causing these differences are still unclear and whilst social, economic and cultural issues are likely to be important, genetic factors could also play a role. Furthermore, the biological mechanisms by which severe responses arise and increase morbidity are still not known.In this project we will analyse genetic variations (causing reside mutations in the proteins) in diverse human populations (e.g. gender, ethnicity, people with severe responses) and in SARS-CoV-2. We will use structural and evolutionary data to determine whether the mutations could affect binding between the virus and human proteins. Human proteins in which mutations do affect binding will be mapped to protein networks to identify biological pathways that could be affected. We have powerful tools for functionally annotating proteins and the pathway modules in which they operate. Our data will rationalise the impacts on disease severity and improve diagnostics for populations at risk. Finally, proteins in these pathways are likely to be effective drug targets and we will use our protein family data to identify or repurpose suitable drugs having low side effects. We will also analyse related coronaviruses to identify future risks.We have already established a website (https://funvar.cathdb.info/uniprot/dataset/covid) providing mapping of SARS-CoV-2 viral proteins, functional annotations and proximity of mutations to known/predicted functional sites. This is currently populated with preliminary pilot data. It will be extended to host interactors and provide information on pathways and repurposed drugs.Research PlanWe will: (a) Classify 'human interactor' proteins interacting with viral proteins into CATH-FunFams to extract known or predicted structures and map variants (residue mutations) from different genders and populations onto these structures.(b) Perform FunVar analyses to identify mutations in human interactor and SARS-CoV-2 proteins likely to have functional impacts.(c) Map human interactors to a protein network to highlight biological processes implicated in host response and differentially affected between different genders/ethnicities(d) Identify human interactors which have clinically approved drugs or which map to FunFams from which clinically approved drugs can be repurposed.(e) Disseminate information via FunVar-COVID19 pages Our pipeline will detect diverse variants in different human populations, likely to be impacting functions and affecting Covid-19 response. It will also analyse available drug data to suggest possible therapeutics. Furthermore, our pipeline will be generic and will also be used to analyse other closely related coronavirus genomes that could pose future risks.

SARS-COV-2引起了大流行，导致全球数百万次死亡以及重大的社会和经济破坏。尽管疫苗试验一直在鼓励全球分配疫苗，并且需要一段时间需要治疗干预措施。显然，某些人群更容易受到这种疾病的影响。例如，老年人，黑人和亚洲社区。引起这些差异的因素仍不清楚，而社会，经济和文化问题可能很重要，但遗传因素也可能发挥作用。此外，仍然尚不清楚严重反应并增加发病率的生物学机制。在该项目中，我们将分析多种人群中的遗传变异（引起蛋白质中的遗传突变）（例如性别，种族，种族，严重反应的人）和SARS-COV-2。我们将使用结构和进化数据来确定突变是否可能影响病毒和人蛋白之间的结合。突变确实影响结合的人蛋白将映射到蛋白质网络，以鉴定可能影响的生物途径。我们拥有强大的工具来注释蛋白质及其操作的途径模块。我们的数据将使对疾病严重程度的影响合理化，并改善对处于风险的人群的诊断。最后，这些途径中的蛋白质可能是有效的药物靶标，我们将使用蛋白质家族数据来识别或重新使用具有低副作用的合适药物。我们还将分析相关的冠状病毒以识别未来的风险。我们已经建立了一个网站（https://funvar.cathdb.info/uniprot/dataset/covid）提供了SARS-COV-2病毒蛋白的映射，功能性注释和突变与已知/预测功能的突变性。目前，这是有初步的先导数据。它将扩展到托管相互作用的人并提供有关途径和重新推荐药物的信息。研究我们将：（a）将与病毒蛋白与病毒蛋白相互作用的“人类互动者”蛋白分类到CATA-FUNFAM中，以将已知或预测的结构和地图变体（残基变异）（残基突变（残基突变）提取到不同的性别和种群中），以识别这些结构和范围，以识别这些结构，以识别（b）conterations intections intections。（c）将人类相互作用映射到蛋白质网络中，以突出显示与宿主反应有关的生物学过程，并在不同的性别/族裔/族裔之间有差异影响（d）确定具有临床认可的药物的人类相互作用者，或者映射到临床上批准的临床批准的药物可以通过funfam sportine contine contine contine futine file funvar coptine。会影响功能并影响共vid-19响应。它还将分析可用的药物数据，以提出可能的治疗剂。此外，我们的管道将是通用的，还将用于分析其他可能构成未来风险的密切相关的冠状病毒基因组。