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IIBR:Informatics:RAPID: Structure-based identification of SARS-derived peptides with potential to induce broad protective immunity

IIBR:Informatics:RAPID: Structure-based identification of SARS-derived peptides with potential to induce broad protective immunity
IIBR:信息学:RAPID:基于结构的 SARS 衍生肽的鉴定,具有诱导广泛保护性免疫的潜力
批准号:
2033262
负责人:
Lydia Kavraki
金额:
$11.97万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-06-15 至 2022-05-31

项目摘要

项目成果

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中文摘要
翻译
我们现在正处于一种新型冠状病毒(SARS-CoV-2)引起的大流行时代,全球确诊病例数量快速增长。目前正在进行一些努力,以生产新的药物抑制剂,重新利用现有药物和设计联合治疗。同时,疫苗的开发既针对针对病毒包膜蛋白的中和抗体,也针对基于T细胞淋巴细胞的长期细胞介导免疫。T细胞反应对于对抗病毒感染尤其重要,因为它们可以发现并消灭被感染的细胞。该项目将使用先进的计算结构分析方法来鉴定SARS-CoV-2病毒蛋白的保守小片段(肽),这些片段可以用作广谱肽基疫苗的靶标,这种疫苗可以提供针对几种SARS-CoV-2毒株和其他潜在的sars样冠状病毒的保护性免疫。该工作流程将由广泛的病毒学研究界共享,任何已确定的肽将与SARS-CoV变体和其他病原体研究直接相关,这将缩短未来任何可能的新型冠状病毒的疫苗和药物开发周期。教育和培训未来的研究人员计划通过研究生和博士后研究指导,专业发展和职业指导。该项目将开发一个计算管道,以识别在不同SARS-CoV菌株中保守的肽,并有可能用于诱导针对这些病毒的广泛保护性细胞免疫。该方法是基于结合使用金标准序列方法,以及结合不同人类白细胞抗原(HLA)受体的肽的结构建模和分析的新前沿方法。hla负责将肽展示给t细胞淋巴细胞,并且所提出的管道将能够识别能够触发针对多种SARS-CoV变体的t细胞反应的保守热点。在本项目背景下,研究将针对SARS-CoV-2核衣壳(N)蛋白的保守肽。如有需要,将针对不同的HLA流行等位基因对预测肽进行优化。拟议的计算管道将使用通用软件工程原理构建,使其也适用于研究来自SARS-CoV变体的不同蛋白质,甚至其他病原体。该RAPID奖由生物基础设施部生物研究基础设施创新(IIBR信息学)项目颁发,资金来自《冠状病毒援助、救济和经济安全(CARES)法案》。该奖项反映了美国国家科学基金会的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
英文摘要
We are now living through a pandemic age caused by a novel strain of coronavirus (SARS-CoV-2) with a fast-growing number of confirmed cases all over the world. Several efforts are underway to produce new drug inhibitors, repurpose existing drugs and devise combination treatments. At the same time, vaccine development is targeting both neutralizing antibodies against envelope proteins of the virus, and long-term cell-mediated immunity based on T cell lymphocytes. T cell responses are particularly important for fighting viral infections, because they can find and eliminate infected cells. This project will use advanced methods of computational structural analysis to identify conserved small fragments (peptides) of SARS-CoV-2 viral proteins that can be used as targets for a broad-spectrum peptide-based vaccine, which could provide protective immunity against several strains of SARS-CoV-2 and potentially other SARS-like coronaviruses. The workflow will be shared by broad virology research community and any identified peptides will be directly related to SARS-CoV variants and other pathogen study, which will shorten vaccine and drug development cycle for any possible future new coronaviruses. Educating and training future researchers are planned through graduate and post-doc research mentoring, professional development, and career guidance. This project will develop a computational pipeline to enable the identification of peptides that are conserved across different SARS-CoV strains, and that can potentially be used to induce broad protective cellular immunity against these viruses. The approach is based on the combined use of gold-standard sequence-based methods, and new cutting-edge methods for the structural modeling and analysis of peptides bound to different Human Leukocyte Antigen (HLA) receptors. HLAs are responsible for displaying the peptides to T-cell lymphocytes, and the proposed pipeline will enable the identification of conserved hot-spots capable of triggering T-cell responses against multiple SARS-CoV variants. In the context of this project, research will target conserved peptides from the Nucleocapsid (N) protein of SARS-CoV-2. If needed, optimization of predicted peptides will be conducted for different prevalent HLA alleles. The proposed computational pipeline will be built using general software-engineering principles, making it also applicable to study different proteins from SARS-CoV variants, and even other pathogens. The work done on this project can be found in http://www.kavrakilab.org/nsf-rapid-sarscov2.html This RAPID award is made by the Infrastructure Innovation for Biological Research (IIBR Informatics) Program in the Division of Biological Infrastructure, using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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会议论文
A Framework for Manipulation Planning and Execution under Uncertainty in Partially-Known Environments
  • 批准号:
    2336612
  • 项目类别:
    Standard Grant
  • 资助金额:
    $71.53万
  • 财政年份:
    2024
  • 负责人:
    Lydia Kavraki
  • 依托单位:
Collaborative Research [FW-HTF-RM]: The Future of Nurse Training: Robotic Teaching Assistant Systems for Nursing Instructors
  • 批准号:
    2326390
  • 项目类别:
    Standard Grant
  • 资助金额:
    $82.8万
  • 财政年份:
    2023
  • 负责人:
    Lydia Kavraki
  • 依托单位:
Collaborative Research: FW-HTF-R: The Future of Robot-Assisted Nursing: Interactive AI Frameworks for Upskilling Nurses and Customizing Robot Assistance
  • 批准号:
    2222876
  • 项目类别:
    Standard Grant
  • 资助金额:
    $12.17万
  • 财政年份:
    2022
  • 负责人:
    Lydia Kavraki
  • 依托单位:
RI: Small: A Novel Framework for Informed Manipulation Planning
  • 批准号:
    2008720
  • 项目类别:
    Standard Grant
  • 资助金额:
    $42.5万
  • 财政年份:
    2020
  • 负责人:
    Lydia Kavraki
  • 依托单位:
海外基金