III: Medium: Collaborative Research: Computational Methods to Advance Chemical Genetics by Bridging Chemical and Biological Spaces

III:媒介:合作研究:通过桥接化学和生物空间推进化学遗传学的计算方法

基本信息

  • 批准号:
    0905117
  • 负责人:
  • 金额:
    $ 33.15万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Continuing Grant
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-09-01 至 2014-08-31
  • 项目状态:
    已结题

项目摘要

The recent development of various government and University funded screening centers has provided the academic research community with access to state-of-the-art high-throughput and high-content screening facilities. As a result, chemical genetics, which uses small organic molecules to alter the function of proteins, has emerged as an important experimental technique for studying and understanding complex biological systems. However, the methods used to develop small-molecule modulators (chemical probes) of specific protein functions and analyze the phenotypes induced by them have not kept pace with advances in the experimental screening technologies. Developing probes for novel protein targets remains a laborious process, whereas experimental approaches to identify the proteins that are responsible for the phenotypes induced by small molecules require a large amount of time and capital expenditure. There is a critical need to develop new methods for probe development and target identification and make them publicly available to the research community. Lack of such tools represents an important problem as it impedes the identification of chemical probes for various proteins and reduces our ability to effectively analyze the experimental results in order to elucidate the molecular mechanisms underlying biological processes. Intellectual Merit This project will develop novel algorithms in the areas of cheminformatics, bioinformatics, and machine learning to analyze the publicly available information associated with proteins and the molecules that modulate their functions (target-ligand activity matrix). These algorithms will be used to develop new classes of computational methods and tools to aid in the development of chemical probes and the analysis of the phenotypes elicited by small molecules. The key hypothesis underlying this research is that the target-ligand activity matrix contains a wealth of information that if properly analyzed can provide insights connecting the structure of the chemical compounds (chemical space) to the structure of the proteins and their functions (biological space). Novel methods will be developed to: (i) better analyze the screening results and identify high affinity and selective hits, (ii) build models that can predict the compounds that are active against a novel protein target and select a set of compounds to be included in a high-throughput screen that will be enriched in actives, (iii) virtually generate a set of core molecules (scaffolds) for a given protein target that can be significantly different from those currently available in the various libraries and have a high probability of being active against the target, and (iv) identify the proteins being targeted by compounds in phenotypic assays. In addition, the research will be facilitated by creating a database to integrate a large portion of the publicly-available target-ligand binding data along with information about the targets and the compounds involved. The successful completion of this research will transform the field of chemical genetics by establishing a new methodology by which the increasing amount of target-ligand activity information is used in a systematic way to explicitly guide the discovery of new probes and the analysis of phenotypic assays. Broader Impact The ability to discover chemical probes for a wide range of novel protein targets will make it possible to identify drugs for pharmaceutically relevant proteins, positively impacting the rate of drug discovery. In addition, it will greatly increase the set of proteins that can be selectively modulated via small organic molecules, expand the various biological processes that can be investigated via chemical genetics approaches, and allow researchers to use chemical genetics techniques to gain insights on the mechanisms of action associated with certain phenotypes. This will provide a better understanding of the dynamics of these processes and will supplement existing approaches based on molecular genetics. To further aid in the broad dissemination of the results and enhance scientific understanding, the computational methods developed will be made freely available via stand-alone or web-based services to aid researchers working in the area of chemical genomics. Finally, the project integrates the research with an educational plan that focuses on interdisciplinary undergraduate, graduate, and post-graduate education in the areas of Computer Science, Medicinal Chemistry, and Chemical Genetics. Key Words: supervised learning; semi-supervised learning; cheminformatics; structural bioinformatics; data mining; graph algorithms
最近发展的各种政府和大学资助的筛选中心提供了学术研究界获得最先进的高通量和高内容的筛选设施。因此,化学遗传学,它使用小的有机分子来改变蛋白质的功能,已经成为研究和理解复杂生物系统的重要实验技术。然而,用于开发特定蛋白质功能的小分子调节剂(化学探针)并分析其诱导的表型的方法没有跟上实验筛选技术的进步。开发用于新蛋白质靶标的探针仍然是一个费力的过程,而鉴定负责由小分子诱导的表型的蛋白质的实验方法需要大量的时间和资本支出。迫切需要开发新的探针开发和目标识别方法,并将其公开提供给研究界。缺乏这样的工具代表了一个重要的问题,因为它阻碍了各种蛋白质的化学探针的鉴定,并降低了我们有效分析实验结果以阐明生物过程背后的分子机制的能力。本项目将在化学信息学、生物信息学和机器学习领域开发新的算法,以分析与蛋白质和调节其功能的分子(靶-配体活性矩阵)相关的公开信息。这些算法将用于开发新的计算方法和工具,以帮助开发化学探针和分析小分子引起的表型。这项研究的关键假设是,目标配体活性矩阵包含丰富的信息,如果正确分析,可以提供将化合物结构(化学空间)与蛋白质结构及其功能(生物空间)联系起来的见解。将开发新方法,以便:(i)更好地分析筛选结果并鉴定高亲和力和选择性命中,(ii)建立可以预测对新蛋白质靶标有活性的化合物的模型,并选择一组将被包括在高通量筛选中的化合物,其将富含活性物质,(iii)虚拟地生成一组核心分子对于给定的蛋白质靶标,其可以显著不同于目前在各种文库中可获得的那些蛋白质靶标,并且具有针对靶标的活性的高概率,和(iv)在表型测定中鉴定化合物靶向的蛋白质。此外,将通过创建数据库来整合大部分公开可用的靶标-配体结合数据沿着有关靶标和所涉及化合物的信息,从而促进研究。这项研究的成功完成将改变fi通过建立一种新的方法学,通过该方法学,以系统的方式使用越来越多的靶配体活性信息来明确指导新探针的发现和表型测定的分析,从而在化学遗传学领域取得了进展。更广泛的影响发现各种新型蛋白质靶点的化学探针的能力将使识别药物相关蛋白质的药物成为可能,从而积极影响药物发现的速度。此外,它将大大增加可以通过小有机分子选择性调节的蛋白质组,扩展可以通过化学遗传学方法研究的各种生物过程,并允许研究人员使用化学遗传学技术来深入了解与某些表型相关的作用机制。这将提供一个更好地了解这些过程的动态,并将补充现有的方法基于分子遗传学。为了进一步帮助广泛传播研究结果并提高科学认识,将通过独立或基于网络的服务免费提供所开发的计算方法,以帮助在化学基因组学领域工作的研究人员。最后,该项目将研究与教育计划相结合,重点是计算机科学,药物化学和化学遗传学领域的跨学科本科,研究生和研究生教育。关键词:监督学习;半监督学习;化学信息学;结构生物信息学;数据挖掘;图算法

项目成果

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Huzefa Rangwala其他文献

WLA4ND: a Wearable Dataset of Learning Activities for Young Adults with Neurodiversity to Provide Support in Education
WLA4ND:为具有神经多样性的年轻人提供学习活动的可穿戴数据集,为教育提供支持
Counterfactually Fair Dynamic Assignment: A Case Study on Policing
反事实公平动态分配:警务案例研究
Protein Function Prediction Using Multilabel Ensemble Classification
使用多标签集成分类进行蛋白质功能预测
spanProtein Function Prediction with Incomplete Annotations/span
注释不完整的蛋白质功能预测
<span>Protein Function Prediction with Incomplete Annotations</span>

Huzefa Rangwala的其他文献

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{{ truncateString('Huzefa Rangwala', 18)}}的其他基金

REU Site: Undergraduate Research in Educational Data Mining
REU 网站:教育数据挖掘本科生研究
  • 批准号:
    1757064
  • 财政年份:
    2018
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant
BIGDATA: IA: DKA: Collaborative Research: Learning Data Analytics: Providing Actionable Insights to Increase College Student Success
大数据:IA:DKA:协作研究:学习数据分析:提供可行的见解以提高大学生的成功
  • 批准号:
    1447489
  • 财政年份:
    2014
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant
CAREER: Annotating the Microbiome using Machine Learning Methods
职业:使用机器学习方法注释微生物组
  • 批准号:
    1252318
  • 财政年份:
    2013
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant
Career Mentoring Forum and Student Travel Support for 2012 IEEE International Conference on Data Engineering (ICDE)
2012 年 IEEE 国际数据工程会议 (ICDE) 职业指导论坛和学生旅行支持
  • 批准号:
    1228466
  • 财政年份:
    2012
  • 资助金额:
    $ 33.15万
  • 项目类别:
    Standard Grant

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