Principles for Designing Stimulus-Responsive Enzymes
Principles for Designing Stimulus-Responsive Enzymes
批准号:
9897295
负责人:
Sagar D Khare
金额:
$46.43万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
已结题
起止时间:
2019-09-27 至 2023-06-30
关键词:
Active SitesBindingBiological ModelsBiological ProcessBiological Response Modifier TherapyBiophysicsBlood coagulationCarboxypeptidaseCell Culture TechniquesChemotherapy-Oncologic ProcedureComputer SimulationComputing MethodologiesCouplingCysteineCytosine deaminaseDevelopmentEngineeringEnzyme PrecursorsEnzymesFeedbackFluorouracilGeneticGeometryGoalsKineticsKnowledgeLeadLearningLightMechlorethamineMediatingMetalloproteasesMetalsMethodologyMethodsModelingMolecular ConformationMutagenesisOpsinOpticsOutcomePathologyPeptide HydrolasesPhototransductionProcessProdrugsPropertyProtein ConformationProtein EngineeringProteinsProteolysisProtocols documentationResearchResolutionRetinalRoentgen RaysSiteStimulusStructural ProteinStructureStructure-Activity RelationshipSystemTechniquesTestingTherapeuticTherapeutic InterventionTissuesValidationVertebral columnVisionWound HealingX-Ray Crystallographyazobenzenebasebiophysical propertiesbiophysical techniqueschemotherapychromophorecis trans isomerizationdeep sequencingdesignenvironmental chemicalexperimental studyimprovedinsightinterestmetal chelatornovelprogramsprotease Eprotein structureprotein structure functionspatiotemporaltumorunnatural amino acids
中文摘要
摘要:
将刺激反应能力设计成任何一种选择的酶的能力将有助于我们
以极高的空间和时间精度审问和干预生物过程。通过
自下而上将刺激响应性构建到酶中,深入了解基础生物物理
还将实现自然系统中变构效应的基本原理(从构建中学习)。焦点
这一提议的核心是自下而上地开发刺激反应性前药物激活酶
通过计算方法进行蛋白质设计(KARE)与蛋白质工程的协同结合
通过非天然氨基酸(UAA)突变(Deiters)。非天然氨基酸的基因掺入
允许以特定位置的方式引入生物正交开关,而计算建模使
蛋白质活性部位和UAA结构和构象景观的合理重新布线
原子分辨率的微环境。通过将这些最先进的技术与成熟的
关于详细的动力学、结构和生物物理特征的协议,我们假设自下而上
在蛋白质中引入刺激反应性的框架将被破译。作为我们的模型系统,我们
将使用羧肽酶G2/氮芥前药和胞嘧啶脱氨酶/5-氟尿嘧啶
酶/前药物对,这两种药物都在一种名为
定向酶前药疗法(DEPT)。它们在DEPT的治疗环境中的效用将通过以下方式增强
使它们可以有条件地激活。在目标1中,我们将使用含偶氮苯的光响应型尿酸
以使所设计的酶可光控制。在目标2中,我们将开发设计方法
可被组织特定的蛋白水解酶激活的酶的酵素形式,如基质
金属蛋白酶。在目标3中,我们将使用各种结构和生物物理技术来验证设计
并为进一步的设计迭代和建模方法改进提供反馈。而当
我们的重点是上面提到的两种酶,然而,我们正在开发的方法将可以转移到
控制各种其他酶的活性和传递,使其成为设计新的
刺激反应酶,并潜在地允许前所未有的选择性和最佳的输送
化疗。
英文摘要
Summary:
The ability to design stimulus-responsiveness into any enzyme of choice would aid in our ability to
interrogate and intervene in biological processes with exquisitely high spatial and temporal precision. By
constructing stimulus-responsiveness into enzymes from the bottom up, insights into fundamental biophysical
principles underlying allosteric effects in natural systems will also be achieved (learning by building). The focus
of this proposal is the bottom-up development of stimulus-responsive prodrug-activating enzymes by
synergistically combining protein design through computational approaches (Khare) with protein engineering
through unnatural amino acid (UAA) mutagenesis (Deiters). Genetic incorporation of unnatural amino acids
allows introducing bio-orthogonal switches in a site-specific manner, and computational modeling enables
rational rewiring of the structure and conformational landscape of the protein active site and UAA
microenvironment with atomic resolution. By combining these state-of-the-art techniques with well-established
protocols for detailed kinetic, structural, and biophysical characterization, we hypothesize that a bottom up
framework for introducing stimulus-responsiveness in proteins will be deciphered. As our model systems, we
will use the carboxypeptidase G2/nitrogen mustard prodrug and cytosine deaminase/5-fluorouracil
enzyme/prodrug pairs, both of which have been extensively investigated in a chemotherapy application called
directed enzyme prodrug therapy (DEPT). Their utility in a therapeutic setting for DEPT will be enhanced by
rendering them conditionally activatable. In Aim 1, we will use azobenzene-containing photo-responsive UAAs
for rendering the designed enzymes photocontrollable. In Aim 2, we will develop methodology for designing
zymogenized versions of enzymes that can be activated by tissue-specific proteolytic enzymes, such as matrix
metalloproteases. In Aim 3, we will use a variety of structural and biophysical techniques to validate designed
enzymes and provide feedback for further design iterations and modeling methodology improvement. While
our focus is on the two enzymes mentioned, the methods we are developing, however, will be transferable to
the control of activity and delivery of a variety of other enzymes, making this a general approach to design new
stimulus-responsive enzymes and potentially also allowing unprecedented selectivity and optimal delivery of
chemotherapies.
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资助金额:$23.69万
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财政年份:2022
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负责人:Sagar D Khare
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批准号:10207681
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