Predicting Protein Evolution with Phage Escape
Predicting Protein Evolution with Phage Escape
批准号:
7707091
负责人:
TOBIN J DICKERSON
金额:
$28.49万
依托单位国家:
美国
项目类别:
财政年份:
2009
资助国家:
美国
项目状态:
已结题
起止时间:
2009-06-05 至 2011-05-31
关键词:
AffectAmino Acid SequenceAntibodiesAntigensAnxietyAreaAvian InfluenzaBacteriophage M13BacteriophagesBindingBiologicalBiological ModelsCell modelCellsCollectionCytotoxic T-LymphocytesDiversity LibraryDrug resistanceEnsureEpitopesEscape MutantEvolutionExperimental DesignsFutureGenerationsGenesGenomic SegmentGoalsH5 hemagglutininHemagglutininHumanImmuneImmune responseImmune systemImmunityIn VitroInfectionInfluenzaInvadedLaboratoriesLibrariesLigand BindingLightMammalian CellMembrane GlycoproteinsMembrane ProteinsMethodsMindMolecularMolecular Biology TechniquesMonoclonal AntibodiesMutateMutationPan GenusPeptide Sequence DeterminationPhage DisplayPlayProceduresProcessProtein RegionProteinsPublic HealthResearchScanningSchemeScreening procedureSideSolutionsStagingSystemTechniquesTechnologyTimeVariantViralVirulentVirusbasecombatcombinatorialexperienceimprovedin vivoinfluenza outbreakinfluenzavirusmembermortalitymutantneutralizing antibodypandemic diseasepandemic influenzapathogenpressurepreventprotein functionpublic health relevanceresearch studyresponsesialic acid receptor
中文摘要
描述(由申请人提供):鉴于与人感染禽流感毒株相关的极高死亡率,流感大流行爆发的出现令人极为关切。大流行毒株被认为是在与多种流感病毒共同感染期间,通过病毒基因组片段的重组引入遗传差异蛋白序列(抗原转移),以及突变的逐渐积累,足以改变给定毒株的抗原性,从而使先前存在的宿主免疫变得无效(抗原漂移)。该项目的目标是通过使用无偏组合文库方法在体外反映这些过程,从而允许访问大部分抗原空间。具体来说,我们将利用我们实验室开发的噬菌体逃逸技术来生成病毒表面糖蛋白血凝素(HA)的组合文库,以允许访问流感病毒可用的序列空间。我们策略的本质是交替筛选人类抗体,首先阻止血凝素与细胞结合,然后选择突变的血凝素蛋白,这些蛋白“逃脱”我们的抗体并保持与细胞结合。这一过程的反复迭代将揭示出一系列能够与细胞结合的不同HA克隆,以及与每种HA相对应的一组中和抗体。最终,我们设想我们的方法将允许评估当前的禽流感毒株(H5),过去发生的毒株(例如H1)以及尚未出现的毒株。此外,我们的实验设计固有的是同时识别特定的人类抗体,这些抗体可以中和HA与宿主细胞的结合。公共卫生相关性:很少有一年流感不是一个公共卫生问题,特别是近年来禽流感大流行的威胁在公众心中引起了极大的焦虑。我们的建议利用现代分子生物学技术来模拟感染所需的流感蛋白的进化。我们的目标是建立一个能够预测流感毒株的实验室系统,并在此过程中,在这些毒株对公众健康构成威胁之前,开发出针对它们的中和疗法。
英文摘要
DESCRIPTION (provided by applicant): In light of the extreme mortality rates associated with human infection with avian influenza strains, the emergence of a pandemic influenza outbreak is of immense concern. Pandemic strains are thought to occur through the introduction of genetically divergent protein sequences by reassortment of viral genomic segments during coinfection with multiple influenza viruses (antigenic shift), as well as gradual accumulation of mutations sufficient to alter the antigenicity of a given strain such that preexisting host immunity becomes ineffective (antigenic drift). The goal of this project is to mirror these processes in vitro by using an unbiased combinatorial library approach, thereby allowing access to large sections of antigenic space. Specifically, we will utilize phage escape technologies developed in our laboratory to generate combinatorial libraries of the viral surface glycoprotein hemagglutinin (HA) to allow access to the sequence space available to the influenza virus. The essence of our tactic is an alternating scheme of screening for human antibodies which first prevent HA binding to cells, followed by a selection for mutant HA proteins that "escape" our antibodies and remain bound to cells. Repeated iteration of this process will reveal an array of varying HA clones capable of binding to cells, as well as the set of neutralizing antibodies corresponding to each HA. Ultimately, we envision that our approach will allow for an assessment of both the current avian influenza strain (H5), virulent HA mutants which occurred in the past (e.g., H1), as well as those that have not yet emerged. Furthermore, inherent to our experimental design is a concurrent identification of specific human antibodies that neutralize HA binding to host cells. PUBLIC HEALTH RELEVANCE: Rarely does a year go by that influenza is not a public health concern, and in particular, the threat of a pandemic avian influenza in recent years has caused immense anxiety in the minds of the public. Our proposal utilizes modern molecular biology techniques to mimic the evolution of the influenza protein required for infection. We aim to generate a laboratory system that can predict influenza strains and in the process, develop neutralizing therapies for these strains before they can become a threat to public health.
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