Robust rational design of chemical tools to inhibit RNA-binding proteins
抑制 RNA 结合蛋白的化学工具的稳健合理设计
基本信息
- 批准号:9751928
- 负责人:
- 金额:$ 35.8万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-30 至 2021-07-31
- 项目状态:已结题
- 来源:
- 关键词:3-DimensionalAddressAdoptionBackBindingBiochemicalBiological AssayBiological ProcessBiologyCell physiologyCellsChemicalsCommunitiesComplexComputer SimulationDimensionsDrug DesignGene ExpressionGenetic TranscriptionGeometryGoalsHydrogen BondingIn VitroIndividualLibrariesLife Cycle StagesLigandsMalignant NeoplasmsMembrane ProteinsMessenger RNAMethodsMolecular ConformationNerve DegenerationPolyadenylationPolyribosomesPositioning AttributeProtein DynamicsProteinsRNARNA BindingRNA SplicingRNA-Binding ProteinsRNA-Protein InteractionResearchResearch PersonnelRoleRosaniline DyesSRSF2 geneSideStem cellsStructural ProteinStructureTestingTherapeutic AgentsTherapeutic InterventionTranslationsUntranslated RNAVariantVertebral columnbasecomputerized toolsdesignfunctional groupgenome-widehuman diseaseimprovedinhibitor/antagonistinsightinterestmembermimicrynovelnovel therapeuticspharmacophorepreservationprotein structurescaffoldscreeningsimulationsmall molecule inhibitorstemtargeted treatmenttoolweb server
项目摘要
Abstract
RNA-binding proteins (RBPs) regulate the life cycle of target mRNAs by controlling splicing,
polyadenylation, stability, localization and translation, and they also modulate function of non-coding RNAs.
Because of their crucial roles in many diverse cellular processes, RBPs are key targets for therapeutic
intervention in a variety of human diseases ranging from cancers to neurodegeneration.
We have recently developed a new and unique computational approach for designing inhibitors of a
given RBP, starting from its structure in complex with RNA. We have validated this approach by applying it to
three separate RBPs, and in each case we have identified inhibitors of our target proteins. Nonetheless, there
is room for further improvement in the compounds we identify using this method. Here, we propose to extend
this computational approach, to improve the initial hit compounds’ potency, selectivity, and diversity. With these
enhancements in place, this approach will provide new chemical tools to explore the biology of individual RBPs
at unprecedented detail. To facilitate rapid and broad adoption of this method, we will also make the
computational tools accessible through a webserver, so that the community of researchers wishing to design
inhibitors for their own RBPs of interest will be able to easily use them.
This research is expected to have an important positive impact because it will provide new insights and
tools to address the distinct challenges associated with finding small-molecule inhibitors of protein-RNA
interactions. This contribution is significant because a broad assortment of human diseases stem from
disregulation of protein-RNA interactions, positioning such inhibitors as potential starting points for developing
a variety of new therapeutic agents.
摘要
RNA结合蛋白(RBP)通过控制剪接来调节靶mRNA的生命周期,
它们与多聚腺苷酸化、稳定性、定位和翻译有关,并且它们还调节非编码RNA的功能。
由于它们在许多不同的细胞过程中的关键作用,RBP是治疗的关键靶标。
从癌症到神经退行性疾病的各种人类疾病的干预。
我们最近开发了一种新的和独特的计算方法,用于设计抑制剂,
给定RBP,从其与RNA复合的结构开始。我们通过将其应用于
三种不同的RBPs,在每种情况下,我们都确定了我们靶蛋白的抑制剂。尽管如此,
我们用这种方法鉴定的化合物还有进一步改进的空间。在这里,我们建议延长
这种计算方法,以提高初始命中化合物的效力,选择性和多样性。与这些
这种方法将提供新的化学工具来探索单个RBP的生物学
前所未有的细节。为方便迅速和广泛采用这一方法,我们还将制定
通过网络服务器访问的计算工具,以便希望设计的研究人员社区
抑制剂为自己的RBP的利益将能够很容易地使用它们。
这项研究预计将产生重要的积极影响,因为它将提供新的见解,
解决与寻找蛋白质-RNA小分子抑制剂相关的独特挑战的工具
交互.这一贡献是重要的,因为人类疾病的广泛分类源于
蛋白质-RNA相互作用的失调,将这些抑制剂定位为开发
各种新的治疗药物。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
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John Karanicolas其他文献
John Karanicolas的其他文献
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{{ truncateString('John Karanicolas', 18)}}的其他基金
Designing selective kinase inhibitors via deep learning
通过深度学习设计选择性激酶抑制剂
- 批准号:
10366318 - 财政年份:2022
- 资助金额:
$ 35.8万 - 项目类别:
Refolding Mutant p53: A Strategy for Cancer Prevention in Li-Fraumeni Syndrome
重折叠突变体 p53:Li-Fraumeni 综合征癌症预防策略
- 批准号:
10505614 - 财政年份:2022
- 资助金额:
$ 35.8万 - 项目类别:
Designing selective kinase inhibitors via deep learning
通过深度学习设计选择性激酶抑制剂
- 批准号:
10798523 - 财政年份:2022
- 资助金额:
$ 35.8万 - 项目类别:
Designing selective kinase inhibitors via deep learning
通过深度学习设计选择性激酶抑制剂
- 批准号:
10552030 - 财政年份:2022
- 资助金额:
$ 35.8万 - 项目类别:
Robust rational design of chemical tools to inhibit RNA-binding proteins
抑制 RNA 结合蛋白的化学工具的稳健合理设计
- 批准号:
9290770 - 财政年份:2017
- 资助金额:
$ 35.8万 - 项目类别:
Robust rational design of chemical tools to inhibit RNA-binding proteins
抑制 RNA 结合蛋白的化学工具的稳健合理设计
- 批准号:
9978889 - 财政年份:2017
- 资助金额:
$ 35.8万 - 项目类别:
Identifying stabilizers of p53 using pocket complementarity
利用口袋互补性识别 p53 的稳定剂
- 批准号:
9357613 - 财政年份:2016
- 资助金额:
$ 35.8万 - 项目类别:
Identifying inhibitors of protein interactions using pocket optimization
使用口袋优化识别蛋白质相互作用的抑制剂
- 批准号:
8826142 - 财政年份:2012
- 资助金额:
$ 35.8万 - 项目类别:
Identifying inhibitors of protein interactions using pocket optimization
使用口袋优化识别蛋白质相互作用的抑制剂
- 批准号:
8448099 - 财政年份:2012
- 资助金额:
$ 35.8万 - 项目类别:
Identifying inhibitors of protein interactions using pocket optimization
使用口袋优化识别蛋白质相互作用的抑制剂
- 批准号:
8304731 - 财政年份:2012
- 资助金额:
$ 35.8万 - 项目类别:
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