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GOALI: Methods for designing antibodies specific for intrinsically disordered proteins

GOALI: Methods for designing antibodies specific for intrinsically disordered proteins
目标:设计针对本质无序蛋白质的特异性抗体的方法
批准号:
1813963
负责人:
Peter Tessier
金额:
$25.43万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-10-01 至 2021-07-31

项目摘要

项目成果

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中文摘要
翻译
1605266 Tessier,Peter M. 在生物技术领域,一个巨大的、未被满足的挑战是能够可靠地设计识别不同生物分子内预先选择的表位的抗体。该项目具有广泛的影响,了解设计的结合蛋白,可用于广泛的应用的基本原则。这个GOALI项目还将通过在辉瑞的研究经验,辉瑞科学家教授的短期课程以及与辉瑞科学家的定期互动,使研究生接触生物技术行业。该提案的另一个重点是为纽约首都地区代表性不足的少数民族和其他弱势学生制定科学外展计划。学术界(伦斯勒理工学院)和工业界(辉瑞公司)之间的这一合作GOALI项目旨在克服使用免疫接种产生针对固有无序蛋白质的抗体的局限性。所提出的用于设计抗体的方法受到折叠蛋白质内β链之间的天然氢键相互作用的启发。该方法基于在蛋白质数据库中挖掘β链内的小肽(3- 6聚体),这些小肽与相邻β链中的靶表位片段天然配对。通过鉴定与靶表位互补且彼此重叠的多个小肽,将产生较大的互补肽。将评价这些肽在移植到抗体片段的互补决定区(CDR)中时介导抗体与靶表位结合的能力。此外,分子建模和定向进化方法将用于鉴定移植互补肽和邻近CDR中进一步增加结合亲和力的突变。最后,将在CDR中及其附近引入增溶突变,以克服接枝肽的疏水性。CBET部门生物技术和生物化学工程项目的这一奖项与该奖项由工业创新与合作伙伴部门的GOALI计划和材料研究部门的生物材料计划资助。该奖项反映了NSF的法定使命,被认为是当之无愧的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来提供支持。
英文摘要
1605266 Tessier, Peter M. A grand, unmet challenge in the field of biotechnology is to be able to reliably design antibodies that recognize pre-selected epitopes within diverse biomolecules. This project has broad implications for understanding fundamental principles for designing binding proteins that can be used for a wide range of applications. This GOALI project will also expose graduate students to the biotech industry through research experiences at Pfizer, short courses taught by Pfizer scientists, and regular interactions with Pfizer scientists. Another key focus of this proposal is to develop science outreach programs to underrepresented minorities and other disadvantaged students in New York's Capital Region.This collaborative GOALI project between academia (Rensselaer Polytechnic Institute) and industry (Pfizer) is aimed at overcoming the limitations of using immunization to generate antibodies specific for intrinsically disordered proteins. The proposed method for designing antibodies is inspired by the natural hydrogen bonding interactions between beta-strands within folded proteins. This approach is based on mining the Protein Data Bank for small peptides (3-6mers) within beta-strands that are naturally paired to segments of the target epitope in neighboring beta-strands. By identifying multiple small peptides that are complementary to the target epitope and which overlap with each other, larger complementary peptides will be generated. These peptides will be evaluated for their ability to mediate antibody binding to the target epitope when grafted into the complementarity determining regions (CDRs) of antibody fragments. Moreover, molecular modeling and directed evolution methods will be used to identify mutations in the grafted complementary peptides and neighboring CDRs that further increase binding affinity. Finally, solubilizing mutations in and near the CDRs will be introduced to overcome the hydrophobic nature of the grafted peptides.This award by the Biotechnology and Biochemical Engineering Program of the CBET Division is co-funded by the GOALI Program of the Division of Industrial Innovation and Partnerships and by the Biomaterials Program of the Division of Materials Research.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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GOALI: Methods for designing antibodies specific for intrinsically disordered proteins
  • 批准号:
    1605266
  • 项目类别:
    Standard Grant
  • 资助金额:
    $35.0万
  • 财政年份:
    2016
  • 负责人:
    Peter Tessier
  • 依托单位:
Design of conformation-specific antibodies against unfolded and misfolded proteins
  • 批准号:
    1159943
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2012
  • 负责人:
    Peter Tessier
  • 依托单位:
CAREER: Loop engineering of protein surfaces for tunable self-association and phase behavior
  • 批准号:
    0954450
  • 项目类别:
    Continuing Grant
  • 资助金额:
    $41.19万
  • 财政年份:
    2010
  • 负责人:
    Peter Tessier
  • 依托单位:
国内基金
海外基金
Computational Methods for Analyzing Toponome Data