Kinetics-Driven Drug Discovery Using Persistent Homology, Rare-Event Molecular Dynamics and Experimental Data

使用持久同源性、罕见事件分子动力学和实验数据进行动力学驱动的药物发现

基本信息

  • 批准号:
    1761320
  • 负责人:
  • 金额:
    $ 110万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2018
  • 资助国家:
    美国
  • 起止时间:
    2018-07-15 至 2023-06-30
  • 项目状态:
    已结题

项目摘要

In this project a team of investigators from mathematics, molecular biology and medicinal chemistry will develop mathematical and computational tools to predict the efficacy of compounds that may help treat neuropathic pain in diabetics. Pharmaceutical drugs are mostly made up of very small molecules, which take their effect by binding to target biomolecules in our bodies and perturbing their functions. A major challenge in designing new drugs - such as treatments for cancer, diabetes and Alzheimer's disease - is figuring out how to bind a particular target both accurately (with little off-target binding), and effectively (a high percentage of targets occupied by drug molecules). A key quantity to maximize the effectiveness of a drug is its "residence time," the average amount of time the drug will remain in the binding site after each binding event. However, little is known about how the structure of a drug molecule determines its residence time, and this hinders our ability to incorporate residence time predictions in the drug design process. This research will predict the residence times of compounds binding to a pharmaceutical target molecule that affects diabetic neuropathic pain, as well as test those predictions experimentally. This study will potentially result in new treatments for diabetic neuropathic pain and also serve as a blueprint for future drug discovery efforts focused on residence time. To facilitate adoption of this approach the team of investigators will disseminate their results via a dedicated website, online servers and participation in world-wide competitions for predicting drug binding properties. This project also involves the training of graduate students with unique interdisciplinary backgrounds, and will inform the development of graduate courses at the intersection of mathematics and biological sciences. This project will develop a pipeline of mathematical and computational tools to enable kinetics-based drug discovery. The studies will be conducted on soluble epoxide hydrolase (sEH), an established pharmaceutical target for diabetic neuropathic pain for which only limited drugs have yet been approved. This project will use an integrated approach that encompasses topological modeling, machine learning, virtual screening, molecular simulation, as well as in vitro and in vivo assessment of compound efficacy. In PI Wei's laboratory, persistent homology will be used together with deep learning to abstract topological information from protein-ligand complexes and predict stable binding poses, binding affinities, and binding kinetics. It is believed that the combination of topological analysis and deep learning will be transformative: it will bring a surge in similar approaches in 3D biomolecular data predictions in the near future, as well as applications to other fields, such as chemistry, and material science. PI Dickson will use rare-event techniques in molecular modeling to simulate ligand release events, and identify the rate-limiting transition states of the ligand binding and release process. This project will also examine the robustness of ligand binding transition states for the first time, which is the key quantity to enable kinetics-based drug design. Thirdly, PI Lee will continually assess the binding affinity and residence times of the predicted compounds. Selected compounds will be tested in vivo with a novel mouse model, to determine the limits of the benefits of long in vitro residence times. This collaborative project will achieve synergistic benefits by bringing together expertise from advanced mathematics, computational biophysics, and molecular pharmacology. The collaborative tools for sampling and prediction resulting from this work can then be applied to the discovery of novel long residence time compounds for other targets of interest.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.
在这个项目中,来自数学,分子生物学和药物化学的研究人员将开发数学和计算工具来预测可能有助于治疗糖尿病神经性疼痛的化合物的疗效。 药物主要由非常小的分子组成,这些分子通过与我们体内的目标生物分子结合并干扰它们的功能来发挥作用。设计新药(如治疗癌症、糖尿病和阿尔茨海默病)的一个主要挑战是弄清楚如何准确(几乎没有脱靶结合)和有效(药物分子占据高比例的靶点)结合特定靶点。使药物有效性最大化的关键量是其“停留时间”,即每次结合事件后药物将保留在结合位点的平均时间量。然而,人们对药物分子的结构如何决定其停留时间知之甚少,这阻碍了我们将停留时间预测纳入药物设计过程的能力。 这项研究将预测与影响糖尿病神经性疼痛的药物靶分子结合的化合物的停留时间,并通过实验测试这些预测。 这项研究将可能导致糖尿病神经性疼痛的新治疗方法,并作为未来药物发现工作的蓝图,重点是停留时间。 为了促进这种方法的采用,研究人员团队将通过专门的网站、在线服务器和参与全球范围的预测药物结合特性的竞赛来传播他们的结果。该项目还涉及培养具有独特跨学科背景的研究生,并将为数学和生物科学交叉点的研究生课程的发展提供信息。该项目将开发一系列数学和计算工具,以实现基于动力学的药物发现。这些研究将对可溶性环氧化物水解酶(sEH)进行,sEH是糖尿病神经性疼痛的既定药物靶点,目前只有有限的药物获得批准。该项目将使用一种综合方法,包括拓扑建模,机器学习,虚拟筛选,分子模拟,以及化合物功效的体外和体内评估。在PI Wei的实验室中,持久同源性将与深度学习一起用于从蛋白质-配体复合物中提取拓扑信息,并预测稳定的结合姿势,结合亲和力和结合动力学。 人们相信,拓扑分析和深度学习的结合将是变革性的:在不久的将来,它将在3D生物分子数据预测中带来类似方法的激增,以及在化学和材料科学等其他领域的应用。 PI Dickson将在分子建模中使用稀有事件技术来模拟配体释放事件,并确定配体结合和释放过程的限速过渡态。该项目还将首次研究配体结合过渡态的稳健性,这是实现基于动力学的药物设计的关键量。 第三,PI Lee将持续评估预测化合物的结合亲和力和停留时间。将使用新型小鼠模型在体内测试所选化合物,以确定长体外停留时间的益处的限度。这个合作项目将通过汇集高等数学、计算生物物理学和分子药理学的专业知识来实现协同效益。这项工作产生的采样和预测的协作工具,然后可以应用到发现新的长停留时间化合物的其他目标的interests.This奖项反映了NSF的法定使命,并已被认为是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的支持。

项目成果

期刊论文数量(82)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Omicron Variant (B.1.1.529): Infectivity, Vaccine Breakthrough, and Antibody Resistance
Decoding SARS-CoV-2 Transmission and Evolution and Ramifications for COVID-19 Diagnosis, Vaccine, and Medicine
Protein pocket detection via convex hull surface evolution and associated Reeb graph
  • DOI:
    10.1093/bioinformatics/bty598
  • 发表时间:
    2018-09
  • 期刊:
  • 影响因子:
    5.8
  • 作者:
    Rundong Zhao;Zixuan Cang;Y. Tong;G. Wei
  • 通讯作者:
    Rundong Zhao;Zixuan Cang;Y. Tong;G. Wei
Mechanisms of SARS-CoV-2 Evolution Revealing Vaccine-Resistant Mutations in Europe and America
  • DOI:
    10.1021/acs.jpclett.1c03380
  • 发表时间:
    2021-12-07
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Wang, Rui;Chen, Jiahui;Wei, Guo-Wei
  • 通讯作者:
    Wei, Guo-Wei
HOMOTOPY CONTINUATION FOR THE SPECTRA OF PERSISTENT LAPLACIANS.
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Alex Dickson其他文献

Erratum: “Nonequilibrium umbrella sampling in spaces of many order parameters” [J. Chem. Phys. 130, 074104 (2009)]
勘误表:“多阶参数空间中的非平衡伞采样”[J. Phys. 130, 074104 (2009)]
  • DOI:
  • 发表时间:
    2012
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alex Dickson;Aryeh Warmflash;A. Dinner
  • 通讯作者:
    A. Dinner
The zinc isotope composition of late Holocene open-ocean marine sediments
  • DOI:
    10.7185/gold2021.7744
  • 发表时间:
    2022-06
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alex Dickson
  • 通讯作者:
    Alex Dickson
People and Policy: Behavioural economics and its policy implications
人与政策:行为经济学及其政策影响
  • DOI:
  • 发表时间:
    2015
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alex Dickson;Marco Fongoni
  • 通讯作者:
    Marco Fongoni
Creating Maps of the Ligand Binding Landscape for Kinetics-Based Drug Discovery.
为基于动力学的药物发现创建配体结合景观图。
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Tom Dixon;Samuel D. Lotz;Alex Dickson
  • 通讯作者:
    Alex Dickson
The Role of Markets and Preferences on Resource Conflicts
市场和偏好对资源冲突的作用
  • DOI:
  • 发表时间:
    2018
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Alex Dickson;I. MacKenzie;Petros G. Sekeris
  • 通讯作者:
    Petros G. Sekeris

Alex Dickson的其他文献

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{{ truncateString('Alex Dickson', 18)}}的其他基金

REU Site: ACRES: Advanced Computational Research Experience for Students
REU 网站:ACRES:学生的高级计算研究体验
  • 批准号:
    2349002
  • 财政年份:
    2024
  • 资助金额:
    $ 110万
  • 项目类别:
    Standard Grant

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