Force field development for zinc metalloproteins
锌金属蛋白的力场开发
基本信息
- 批准号:8093181
- 负责人:
- 金额:$ 20.89万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2011
- 资助国家:美国
- 起止时间:2011-09-01 至 2013-08-31
- 项目状态:已结题
- 来源:
- 关键词:Active SitesAffectBiologicalBiological ProcessChemicalsComputer softwareDevelopmentDiseaseDockingDrug Delivery SystemsElectrostaticsEnzyme Inhibitor DrugsEnzyme InhibitorsEnzymesGene SilencingHistonesIonsLigandsLinkMalignant NeoplasmsMatrix MetalloproteinasesMedicalMetalloproteinsMethodsModelingPerformancePlayProteinsRoleSimulateStructureSystemTP53 geneTestingTumor Suppressor ProteinsZincbasedensitydesigndrug candidateflexibilitygene repressionimprovedinhibitor/antagonistinnovationmolecular dynamicsnovelnovel therapeutics
项目摘要
DESCRIPTION (provided by applicant): This proposal is directed at developing novel transferable non-bonded force fields to model zinc metalloproteins and to design zinc enzyme inhibitors. Zinc proteins play essential roles in many biological processes, and there is an increasing appreciation of their biological and medical importance. For example, zinc-dependent histone deacetylases (HDACs) play a critical role in transcriptional repression and gene silencing, and are among the most attractive targets for the development of new therapeutics against cancer and various other diseases. Thus, robust computational approaches are greatly needed to help characterize the structure and dynamics of zinc metalloproteins, and to facilitate the design and ranking of zinc enzyme inhibitors. However, progress along this direction has been very much impeded mainly due to the lack of transferable pairwise force fields to adequately describe zinc coordination. The current dominant view is that such a force field may not be possible and that it would be necessary to go beyond the pairwise non-bonded model for reasonable description of the zinc coordination. In our preliminary studies, we have discovered a novel practical strategy to overcome this inherent challenge, which is to design short-long effective functions (SLEF) to treat electrostatic interactions between the zinc ion and all other atoms. Our preliminary results indicated that this SLEF approach is very promising to adequately model flexible zinc coordination. Here we propose to develop SLEF force fields to simulate zinc metalloproteins, and to develop SLEF scoring functions for docking ligands into zinc enzymes. The developed SLEF patches to the AMBER and Autodock softwares as well as tutorials and test sets will be made freely available to the public.
PUBLIC HEALTH RELEVANCE: Zinc proteins play essential roles in many biological processes, and their malfunctioning or aberrant over-expression has been mechanistically linked to a variety of diseases. Several key zinc proteins have been established as attractive drug targets, including matrix metalloproteinases (MMPs), histone deacetylases (HDACs) and tumor suppressor protein P53. Our proposed studies would significantly advance the field of structure, dynamics and functional studies of zinc metalloproteins, and facilitate the design of new inhibitors for medically important zinc enzymes which can serve as novel drug candidates and innovative chemical probes.
描述(由申请人提供):本提案旨在开发新型可转移非键合力场,以模拟锌金属蛋白并设计锌酶抑制剂。锌蛋白在许多生物过程中起着重要作用,并且越来越多地认识到它们的生物学和医学重要性。例如,锌依赖性组蛋白脱乙酰酶(HDAC)在转录抑制和基因沉默中起关键作用,并且是开发针对癌症和各种其他疾病的新疗法的最有吸引力的靶标之一。因此,强大的计算方法是非常需要的,以帮助表征锌金属蛋白的结构和动力学,并促进锌酶抑制剂的设计和排名。然而,进展沿着这一方向一直非常受阻,主要是由于缺乏可转移的成对力场,以充分描述锌的协调。目前占主导地位的观点是,这样的力场可能是不可能的,这将是必要的,以超越成对的非键合模型的锌配位的合理描述。在我们的初步研究中,我们发现了一种新的实用策略来克服这一固有的挑战,即设计短-长有效函数(SLEF)来处理锌离子与所有其他原子之间的静电相互作用。我们的初步结果表明,这种SLEF方法是非常有前途的充分灵活的锌配位模型。在这里,我们建议开发SLEF力场来模拟锌金属蛋白,并开发SLEF评分功能对接配体到锌酶。开发的AMBER和Autodock软件的SLEF补丁以及教程和测试集将免费向公众提供。
公共卫生相关性:锌蛋白在许多生物学过程中发挥重要作用,其功能失调或异常过度表达与多种疾病有机械联系。几种关键的锌蛋白已被确定为有吸引力的药物靶点,包括基质金属蛋白酶(MMPs)、组蛋白脱乙酰酶(HDACs)和肿瘤抑制蛋白P53。我们提出的研究将显着推进锌金属蛋白的结构,动力学和功能研究领域,并促进新的抑制剂的设计,具有重要的医学意义的锌酶,可以作为新的候选药物和创新的化学探针。
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)
专利数量(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:
$ 20.89万 - 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:
$ 20.89万 - 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:
$ 20.89万 - 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:
$ 20.89万 - 项目类别:
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