PEPTIDE-BASED BORONO LECTINS: NEW TOOLS FOR COLON CANCER

基于肽的硼凝集素:治疗结肠癌的新工具

基本信息

项目摘要

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Proposed in this project is the development of a novel biosensor for the detection of colon cancer. Specifically, the design, synthesis and analysis of a series of Peptide-based Borono Lectins are described. These reagents will be able to detect cancer associated glycans in sera and ultimately on tissues. This project is being pursued because a number of glycans are abnormally expressed in colon cancer and the current methods for carbohydrate detection are cumbersome. The approach described herein, which combines proven and promising methods from the fields of protein chemistry, small molecule sensor design, and advanced optical processing techniques, will generate a novel and biocompatible diagnostic agent. This method takes advantage of Nature's own building blocks and binding scenarios (namely peptides and sugar-lectin interactions respectively), for the creation of a new diagnostic to detect glycans abnormally expressed in colon cancer. Use of a peptide backbone enables a highly modular approach capable of readily changing the geometry, valency and binding affinity of the lectin which allows for rapid access to several different binding scenarios within the sensor and therefore the ability to swiftly assay for new and different targets. Randomized peptide libraries incorporating phenyl boronic acids will be synthesized and used to screen for binding with a number of simple and complex cancer associated glycans. This is a high throughput combinatorial approach that is expected to lead to the identification of sugar-specific and cross-reactive lectins. These borono-lectins are conjugated to a solid support and as such will be used to generate a novel biosensor platform for the detection of abnormal levels of specific saccharides present in sera. Successful development of this proposal has the potential to revolutionize colon cancer diagnostics. In a number of disease states, including colon cancer, the levels and types of serum and cell surface glycans (i.e. sugars) are abnormal. This proposal takes a high throughput, combinatorial approach to generate and screen Peptide-based Borono-Lectins (PBLs) for specific saccharide recognition. Successful completion of this project will provide an efficient diagnostic test to simply and rapidly identify the presence and/or extent of colon cancer; thereby aiding the development of an appropriate therapeutic response. .
这个子项目是众多研究子项目之一

项目成果

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专利数量(0)

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PAUL M THOMPSON其他文献

PAUL M THOMPSON的其他文献

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{{ truncateString('PAUL M THOMPSON', 18)}}的其他基金

CARE4Kids: Imaging Biomarker Core
CARE4Kids:成像生物标志物核心
  • 批准号:
    10203601
  • 财政年份:
    2021
  • 资助金额:
    $ 19.19万
  • 项目类别:
ENIGMA World Aging Center
ENIGMA世界老龄化中心
  • 批准号:
    10576402
  • 财政年份:
    2021
  • 资助金额:
    $ 19.19万
  • 项目类别:
ENIGMA World Aging Center
ENIGMA世界老龄化中心
  • 批准号:
    10328963
  • 财政年份:
    2021
  • 资助金额:
    $ 19.19万
  • 项目类别:
FiberNET: Deep learning to evaluate brain tract integrity worldwide and in AD
FiberNET:深度学习评估全球和 AD 脑道完整性
  • 批准号:
    10814696
  • 财政年份:
    2020
  • 资助金额:
    $ 19.19万
  • 项目类别:
Neuroimaging Core
神经影像核心
  • 批准号:
    10216924
  • 财政年份:
    2018
  • 资助金额:
    $ 19.19万
  • 项目类别:
ENIGMA-SD: Understanding Sex Differences in Global Mental Health through ENIGMA
ENIGMA-SD:通过 ENIGMA 了解全球心理健康中的性别差异
  • 批准号:
    9892045
  • 财政年份:
    2018
  • 资助金额:
    $ 19.19万
  • 项目类别:
Neuroimaging Core
神经影像核心
  • 批准号:
    10456750
  • 财政年份:
    2018
  • 资助金额:
    $ 19.19万
  • 项目类别:
Multi-Source Sparse Learning to Identify MCI and Predict Decline
多源稀疏学习识别 MCI 并预测衰退
  • 批准号:
    9008380
  • 财政年份:
    2016
  • 资助金额:
    $ 19.19万
  • 项目类别:
Data Science Research
数据科学研究
  • 批准号:
    9108711
  • 财政年份:
    2016
  • 资助金额:
    $ 19.19万
  • 项目类别:
ENIGMA Center for Worldwide Medicine, Imaging & Genomics
ENIGMA 全球医学影像中心
  • 批准号:
    9108710
  • 财政年份:
    2014
  • 资助金额:
    $ 19.19万
  • 项目类别:

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