RAPID: Bioinformatic Search for Epitope-based Molecular Mimicry in the SARS-CoV-2 Virus using Chameleon

RAPID:使用 Chameleon 对 SARS-CoV-2 病毒中基于表位的分子拟态进行生物信息搜索

基本信息

  • 批准号:
    2037374
  • 负责人:
  • 金额:
    $ 19.9万
  • 依托单位:
  • 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
  • 起止时间:
    2020-07-01 至 2022-06-30
  • 项目状态:
    已结题

项目摘要

Cross-reactive immunity is a process by which an individual who was vaccinated by an unrelated vaccine or who has recovered from an infection from an unconnected pathogen in the past is seemingly protected against an infection by SARS-CoV-2. A key step for an individual to mount a successful immune response to a pathogenic infection is for a human antibody to recognize and bind to a specific epitope (fragment) from an antigenic protein from the infecting pathogen. Cross-reactivity by molecular mimicry may occur when an antibody fortuitously binds to an epitope from SARS-CoV-2 because of a structural similarity at the binding interface with the epitope for which it was intended. If verified, a rapid repurposing of drugs and vaccines designed for the other pathogens can be quickly validated and applied to the current pandemic. This project plans to use bioinformatic techniques to investigate how molecular mimicry may play a role in cross-reactive immunity.The software pipeline will use the high-performance computing resources in the Chameleon cloud computing platform to run computationally-intensive molecular dynamics simulations within a machine learning framework and help identify occurrences of molecular mimicry in SARS-CoV-2. The pipeline can be divided into two main parts. The first part involves extracting useful features from structures of known complexes available from public databases such as Protein Data Bank (PDB). The second part involves building machine learning models from these features so that molecular mimicry, if present, can be detected in SARS-CoV-2.The machine learning framework will result in reusable models of molecular mimicry and is expected to assist in vaccine development. If successful, the project can potentially (a) explain global disparities in hospitalizations and death rates; (b) lead to quick repurposing of drugs to fight the current pandemic; (c) be replicated for other pathogens; (d) lead to faster vaccine development; (e) impact development of novel bioinformatic strategies for the current and future pandemics.An interdisciplinary team with expertise in computational biophysics, bioinformatics, machine learning, evolutionary biology, infectious diseases, computational epigenetics, glycobioogy, high-performance computing and software engineering will drive this project. All results will be made available through the project website at: http://biorg.cs.fiu.edu/lemom, including examples of molecular mimicry, software for replicating the experiments, and performance benchmarking results on Chameleon.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.
交叉反应性免疫是一个过程,通过该过程,接种了不相关疫苗的个体或过去从不相关病原体感染中恢复的个体似乎可以免受SARS-CoV-2的感染。个体对病原性感染产生成功免疫应答的关键步骤是人抗体识别并结合来自感染病原体的抗原蛋白的特异性表位(片段)。当抗体偶然结合SARS-CoV-2的表位时,可能发生分子模拟的交叉反应性,因为在结合界面处与其预期的表位具有结构相似性。如果得到证实,针对其他病原体设计的药物和疫苗的快速再利用可以迅速得到验证并应用于当前的大流行。该项目计划使用生物信息学技术来研究分子模拟如何在交叉反应免疫中发挥作用。软件管道将使用Chameleon云计算平台中的高性能计算资源,在机器学习框架内运行计算密集的分子动力学模拟,并帮助识别SARS-CoV-2中的分子模拟。管道可分为两个主要部分。第一部分涉及从公共数据库如蛋白质数据库(PDB)中获得的已知复合物的结构中提取有用的特征。第二部分涉及从这些特征中构建机器学习模型,以便在SARS-CoV-2中检测分子模拟,如果存在的话。机器学习框架将导致可重复使用的分子模拟模型,并有望帮助疫苗开发。如果成功,该项目有可能:(a)解释住院率和死亡率方面的全球差异;(B)导致迅速改变药物用途,以抗击当前的流行病;(c)推广用于其他病原体;(d)导致更快的疫苗开发;(e)为当前和未来的流行病制定新的生物信息学战略。一个具有计算生物物理学、生物信息学、机器学习、进化生物学、传染病、计算表观遗传学、糖生物学、高性能计算和软件工程将推动这一项目。所有结果将通过项目网站http://biorg.cs.fiu.edu/lemom提供,包括分子模拟的例子,用于复制实验的软件,以及变色龙的性能基准测试结果。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Stage-Specific Co-expression Network Analysis for Cancer Biomarker Discovery
Structural and Dynamical Differences in the Spike Protein RBD in the SARS-CoV-2 Variants B.1.1.7 and B.1.351
  • DOI:
    10.1021/acs.jpcb.1c01626
  • 发表时间:
    2021-06-10
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Bhattarai, Nisha;Baral, Prabin;Chapagain, Prem P.
  • 通讯作者:
    Chapagain, Prem P.
Deep Learning to Discover Cancer Glycome Genes Signifying the Origins of Cancer
Significance of the RBD mutations in the SARS-CoV-2 omicron: from spike opening to antibody escape and cell attachment
  • DOI:
    10.1039/d2cp00169a
  • 发表时间:
    2022-03-30
  • 期刊:
  • 影响因子:
    3.3
  • 作者:
    Hossen, Md Lokman;Baral, Prabin;Chapagain, Prem
  • 通讯作者:
    Chapagain, Prem
Deep Learning to Discover Genomic Signatures for Racial Disparity in Lung Cancer
深度学习发现肺癌种族差异的基因组特征
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Giri Narasimhan其他文献

Foot in the Door: Developing Opportunities for Computing Undergraduates to Gain Industry Experience
踏入大门:为计算机本科生提供获得行业经验的机会
A comprehensive survey of scoring functions for protein docking models
  • DOI:
    10.1186/s12859-024-05991-4
  • 发表时间:
    2025-01-22
  • 期刊:
  • 影响因子:
    3.300
  • 作者:
    Azam Shirali;Vitalii Stebliankin;Ukesh Karki;Jimeng Shi;Prem Chapagain;Giri Narasimhan
  • 通讯作者:
    Giri Narasimhan
Title : Geometric Spanners Name :
名称: 几何扳手 名称:
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Joachim Gudmundsson;Giri Narasimhan;M. Smid;Levcopoulos Gudmundsson;Narasimhan
  • 通讯作者:
    Narasimhan
Software Guild: A Workshop to Introduce Women and Non-Binary Undergraduate Students from other Majors to Computing
软件协会:向其他专业的女性和非二元本科生介绍计算机的研讨会
Pilot Study on the Effect of Cocaine Use on the Intestinal Microbiome and Metabolome and Inflammation in HIV-Infected Adults in the Miami Adult Studies in HIV (MASH) Cohort (P13-027-19)
  • DOI:
    10.1093/cdn/nzz036.p13-027-19
  • 发表时间:
    2019-06-01
  • 期刊:
  • 影响因子:
  • 作者:
    Sabrina Martinez;Adriana Campa;Giri Narasimhan;Danielle Portuando;Leslie Seminario;Juphshy Jasmin;Marianna Baum
  • 通讯作者:
    Marianna Baum

Giri Narasimhan的其他文献

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{{ truncateString('Giri Narasimhan', 18)}}的其他基金

RIA: Sparse Geometric Spanners, Geometric Analysis, and Applications
RIA:稀疏几何扳手、几何分析和应用
  • 批准号:
    9409752
  • 财政年份:
    1994
  • 资助金额:
    $ 19.9万
  • 项目类别:
    Standard Grant

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  • 批准号:
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DATA ANALYTICS, STATISTICAL AND BIOINFORMATIC ANALYSIS AND TOOL DEVELOPMENT, Genome wide association studies (GWAS)
数据分析、统计和生物信息分析及工具开发、全基因组关联研究 (GWAS)
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利用新的生物信息学方法研究针对全球病毒感染的感染免疫
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Statistical methods for genetic and bioinformatic studies
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