Computational Studies of Histone Modifications
组蛋白修饰的计算研究
基本信息
- 批准号:7763234
- 负责人:
- 金额:$ 24.96万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2007
- 资助国家:美国
- 起止时间:2007-02-01 至 2012-01-31
- 项目状态:已结题
- 来源:
- 关键词:Active SitesAddressAmidohydrolasesAreaArtsChemistryChromatin StructureComputing MethodologiesDevelopmentDiseaseEnvironmentEnzymesFailureFamilyFree EnergyFutureGene ExpressionGene Expression RegulationGenerationsGenetic CodeGenetic TranscriptionGoalsHistone CodeHistone DeacetylaseHistone-Lysine N-MethyltransferaseHistonesHomologous GeneHumanKnowledgeLeadLysineMalignant NeoplasmsMechanicsMethodsMethylationMethyltransferaseModelingModificationMolecularMolecular BiologyMono-SNatureNeurodegenerative DisordersNucleosome Core ParticlePeptide HydrolasesPharmaceutical PreparationsProteinsPsychological TechniquesQuantum TheoryReactionResearch PersonnelSamplingSolutionsSpecificityTailThermolysinWritingZincage relatedantitumor drugbasecomputer studiesconformerdensitydesignhistone modificationhuman diseaseimprovedinsightmethyl groupmutantnovelprototypequantumresearch studystem
项目摘要
DESCRIPTION (provided by applicant): Our long term goal is to develop and apply state-of-the-art computational methods to provide theoretical underpinnings of the histone code. Histone proteins that form the nucleosome core are subject to a variety of post-translational transformations. These histone modifications make up the histone code which extends the information in the genetic code and is emerging as an essential mechanism to regulate gene expression. Failure of appropriate histone modifications can lead to aberrant gene regulation and is implicated in human diseases, notably cancer. In spite of a current flurry of significant advances in experimental studies, there has been little theoretical understanding regarding how the histone code is written, i.e., how enzymes generate or remove these modifications. In this proposal, we mainly focus on two such important histone-modifying enzyme families: Class I and II histone deacetylases (HDACs), and histone lysine methyltransferases (HKMTs). The former are among the most promising targets for the development of anti-tumor drugs, while histone lysine methylation has been addressed in studies of age-related neurodegenerative disorders as well as cancer. Our theoretical approaches will center on the combined ab initio quantum mechanical and molecular mechanical (QM/MM) methods, which allow for accurate modeling of the chemistry at the enzyme active site while properly .including the effects of protein environment. In aim 1, we will characterize the catalytic mechanism for class I and II histone deacetylases. In aim 2, we will investigate HKMTs to provide detailed insights into how the product specificity of histone lysine methylation is achieved. In aim 3, new ab initio QM/MM methods will be developed to further improve its accuracy, efficiency and capability. These proposed studies will not only make fundamental contributions to this new and important area of molecular biology, but also should facilitate the design of novel mechanism-based drugs for diseases stemming from aberrant histone modifications.
描述(由申请人提供):我们的长期目标是开发和应用最先进的计算方法,为组蛋白编码提供理论基础。形成核小体核心的组蛋白经历多种翻译后转化。这些组蛋白修饰组成了组蛋白密码,它扩展了遗传密码中的信息,并成为调节基因表达的重要机制。适当的组蛋白修饰的失败可导致异常的基因调控,并与人类疾病,特别是癌症有关。尽管目前在实验研究方面取得了一系列重大进展,但关于组蛋白密码是如何编写的,即,酶如何产生或去除这些修饰。在这个建议中,我们主要集中在两个这样重要的组蛋白修饰酶家族:I类和II类组蛋白脱乙酰基酶(HDAC),和组蛋白赖氨酸甲基转移酶(HKMT)。前者是开发抗肿瘤药物的最有前途的靶点之一,而组蛋白赖氨酸甲基化已在与年龄相关的神经退行性疾病以及癌症的研究中得到解决。我们的理论方法将集中在结合从头算量子力学和分子力学(QM/MM)的方法,它允许在酶活性位点的化学准确建模,同时适当。包括蛋白质环境的影响。在目标1中,我们将描述I类和II类组蛋白脱乙酰酶的催化机制。在目标2中,我们将研究HKMT,以详细了解组蛋白赖氨酸甲基化的产物特异性如何实现。在目标3中,将开发新的从头算QM/MM方法,以进一步提高其准确性,效率和能力。这些拟议中的研究不仅将为分子生物学这一新的重要领域做出根本性贡献,而且还将促进基于机制的新型药物的设计,用于治疗由异常组蛋白修饰引起的疾病。
项目成果
期刊论文数量(0)
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Yingkai Zhang其他文献
Yingkai Zhang的其他文献
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{{ truncateString('Yingkai Zhang', 18)}}的其他基金
Computational modulator design and machine learning to target protein-protein interactions
针对蛋白质-蛋白质相互作用的计算调节器设计和机器学习
- 批准号:
10623409 - 财政年份:2018
- 资助金额:
$ 24.96万 - 项目类别:
Computational modulator design and machine learning to target protein-protein interactions
针对蛋白质-蛋白质相互作用的计算调节器设计和机器学习
- 批准号:
10401777 - 财政年份:2018
- 资助金额:
$ 24.96万 - 项目类别:
Computational modulator design and machine learning to target protein-protein interactions
针对蛋白质-蛋白质相互作用的计算调节器设计和机器学习
- 批准号:
10152659 - 财政年份:2018
- 资助金额:
$ 24.96万 - 项目类别:
Computational modulator design and machine learning to target protein-protein interactions
针对蛋白质-蛋白质相互作用的计算调节器设计和机器学习
- 批准号:
9926115 - 财政年份:2018
- 资助金额:
$ 24.96万 - 项目类别:
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