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

• 批准号: 0905220
• 负责人: George Karypis
• 金额: $ 85.47万
• 依托单位: University of Minnesota-Twin Cities
• 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 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905220&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Computational

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为学术研究界提供了最先进的高通量和高含量的筛选设施。因此，利用小有机分子改变蛋白质功能的化学遗传学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尚未跟上实验筛选技术的进步。开发新的蛋白质靶点探针仍然是一个艰苦的过程，而鉴定小分子诱导表型的蛋白质的实验方法需要大量的时间和资本支出。迫切需要开发探针开发和目标识别的新方法，并将其公开提供给研究界。缺乏这样的工具是一个重要的问题，因为它阻碍了对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。该项目将开发化学信息学、生物信息学和机器学习领域的新算法，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由小分子引起的表型。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，如果分析得当，可以提供将化合物的结构（化学空间）与蛋白质的结构及其功能（生物空间）联系起来的见解。将开发新的方法来：(i)更好地分析筛选结果并确定高亲和力和选择性命中，（ii）建立可以预测对新蛋白靶点有活性的化合物的模型，并选择一组化合物纳入高通量筛选，这些化合物将富含活性。（iii）为给定的蛋白质靶点生成一组核心分子（支架），这些分子可能与目前在各种文库中可用的分子明显不同，并且对靶点具有高活性的可能性；（iv）在表型分析中识别化合物靶向的蛋白质。此外，通过建立一个数据库来整合大部分公开可用的目标配体结合数据以及有关目标和所涉及化合物的信息，将促进研究。本研究的成功完成将改变通过建立一种新的方法，通过这种方法，越来越多的靶配体活性信息被系统地用于明确指导新探针的发现和表型分析。更广泛的影响为广泛的新蛋白质靶点发现化学探针的能力将使鉴定药物相关蛋白质的药物成为可能，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集，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。这将提供对这些过程的动力学更好的理解，并将补充现有的基于分子遗传学的方法。为了进一步帮助结果的广泛传播和加强科学理解，开发的计算方法将通过独立或基于网络的服务免费提供，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结合起来，重点关注计算机科学、药物化学和化学遗传学领域的跨学科本科生、研究生和研究生教育。关键词：监督学习；semi-supervised学习;cheminformatics;结构生物信息学;数据挖掘;图算法