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CDS&E: Controlling Protein - Protein Interactions: Computations and Experiments

CDS

基本信息

批准号：
1953311
负责人：
Sumit Sharma
金额：
$ 45万
依托单位：
Ohio University
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：
2020-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953311&HistoricalAwards=false
关键词：
CDS&E Controlling Protein Interactions Computations

项目摘要

There are tens of thousands of proteins that bind with other or identical proteins. These protein interactions play a critical role in a range of cellular and physiological functions including gene regulation and immune system response. As such, aberrant protein association has been implicated in pathological conditions including metabolic disorders, chronic inflammation, and cancer. Thus, developing molecules that can control protein interactions would have a broad impact on numerous scientific disciplines including immunology, pathology, and cellular/molecular biology. Furthermore, the identification of chemical agents that reduce unwanted protein interactions would lead to novel therapeutics and diagnostic tools having important clinical implications. Apart from the scientific and clinical impact, such studies would also advance biotechnology, including the manufacturing of specialty proteins and the ability to probe commercially important biological reaction mechanisms. Thus, there is a clear need to develop chemical agents that can control protein association. The overall objective of this project is to employ machine learning tools and molecular-scale simulation, coupled with experimental validation of the predicted molecular performance, to rationally design molecules that can control protein interactions.This research program focuses on the interferon regulator factors (IRF) family of proteins, where protein association is critical to the function of these proteins. IRFs play an important role in numerous physiological and pathological processes, such as in the signaling pathways operative during the immune response to pathogens. The IRF family has nine known members, providing a wide range of protein associations to study. Preliminary research has revealed that small organic molecules, like phenyl methimazole (termed C10), are effective at blocking the association of members of the IRF family. In this research program, high-throughput searches around the chemical space of C10, using a combination of genetic algorithms, machine learning, and molecular dynamics simulation, will be used to find molecules optimized for binding with IRF members. Studies will be conducted to determine the specificity of the binders, i.e., are the binders specific to one particular IRF member or do they behave as pan-IRF binders. Once optimized candidate binders are computationally identified, they will be procured or synthesized, and molecular/cellular assays will be used to quantify their ability to inhibit IRF association. The computational strategies will allow exploration of a large chemical space to narrow the search to a small number of potential binder molecules and the experiments will serve as validation for this approach. Because molecular-level details of how small molecules bind with IRF proteins will be revealed, the fundamental knowledge generated in this research program will be applicable in the search for inhibitors of association of other protein families. The investigators will be involved in a number of outreach activities, including organizing district-level science fairs and participation in university-level programs such as the Program to Aid Career Exploration to advance the Appalachian region of Ohio through education.This project is co-funded by Chemistry of Life Processes in the Division of Chemistry (Mathematical and Physical Sciences Directorate) and by the Process Systems, Reaction Engineering, and Molecular Thermodynamics Program of the Division of Chemical, Bioengineering, Environmental, and Transport Systems (Directorate for Engineering).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.

有成千上万的蛋白质与其他或相同的蛋白质结合。这些蛋白质相互作用在一系列细胞和生理功能中起着关键作用，包括基因调控和免疫系统反应。因此，异常蛋白质缔合已经涉及包括代谢紊乱、慢性炎症和癌症的病理状况。因此，开发可以控制蛋白质相互作用的分子将对许多科学学科产生广泛的影响，包括免疫学，病理学和细胞/分子生物学。此外，减少不需要的蛋白质相互作用的化学试剂的鉴定将导致具有重要临床意义的新的治疗和诊断工具。除了科学和临床影响外，这些研究还将促进生物技术，包括制造特殊蛋白质和探测具有商业重要性的生物反应机制的能力。因此，显然需要开发能够控制蛋白质缔合的化学试剂。本项目的总体目标是利用机器学习工具和分子尺度模拟，结合实验验证预测的分子性能，合理设计能够控制蛋白质相互作用的分子。本研究计划的重点是干扰素调节因子（IRF）蛋白质家族，其中蛋白质缔合对这些蛋白质的功能至关重要。IRF在许多生理和病理过程中发挥重要作用，例如在对病原体的免疫应答期间起作用的信号传导途径中。IRF家族有九个已知成员，提供了广泛的蛋白质关联可供研究。初步研究表明，小的有机分子，如苯基甲巯咪唑（称为C10），可以有效地阻止IRF家族成员的联系。在这项研究计划中，围绕C10化学空间的高通量搜索，使用遗传算法，机器学习和分子动力学模拟的组合，将用于寻找优化与IRF成员结合的分子。将进行研究以确定结合剂的特异性，即，是某一特定IRF成员特有的粘合剂，还是它们具有泛IRF粘合剂的性质。一旦通过计算鉴定出优化的候选结合剂，将获得或合成它们，并使用分子/细胞测定来量化它们抑制IRF结合的能力。计算策略将允许探索大的化学空间，以将搜索范围缩小到少量潜在的粘合剂分子，并且实验将作为这种方法的验证。由于将揭示小分子如何与IRF蛋白结合的分子水平细节，因此该研究计划中产生的基础知识将适用于寻找其他蛋白质家族的抑制剂。研究人员将参与一些推广活动，包括组织地区级的科学博览会和参与大学级的项目，如帮助职业探索项目，以通过教育促进俄亥俄州的阿巴拉契亚地区。（数学和物理科学理事会）和化学，生物工程，环境，该奖项反映了NSF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。