CDI-Type I: Bridging the Gap Between Next-Generation High Performance Hybrid Computers and Physics Based Computational Models for Quantitative Description of Molecular Recognition

CDI-Type I:弥合下一代高性能混合计算机和基于物理的分子识别定量描述计算模型之间的差距

基本信息

  • 批准号:
    0941318
  • 负责人:
  • 金额:
    $ 45万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2009
  • 资助国家:
    美国
  • 起止时间:
    2009-10-01 至 2013-09-30
  • 项目状态:
    已结题

项目摘要

TECHNICAL ABSTRACTThis award is made on a proposal submitted to the Cyberenabled Discovery and Innovation initiative and in partnership with the Office of Experimental Program to Stimulate Competitive Research. This project projects on the From Data to Knowledge, and Virtual Organizations CDI themes. This award supports computational research and education to develop new polarizable force fields with an aim for application to the discovery of new drugs. The tools developed may also impact the discovery of new biomaterials. Molecular simulations play an integral role in the development of novel pharmaceuticals. Complementing experiment, computations strive to expedite the discovery process by screening for small-molecules with high binding affinity, specificity, and pharmacological properties. The fundamental quantity of interest is the binding affinity, and this is rigorously related to the free energy change associated with a binding reaction. Current methods employ empirical potential energy functions, or force fields, representing the physical interactions between the constituent atomic/molecular species. The electrostatic component of the interaction is treated in a mean field manner, with partial charges on given sites of the molecular construct taken to be fixed throughout the binding process; there is no explicit accounting for the differing nature of charge distribution for different ligand and protein conformations as physically dictated by quantum mechanics, whose inclusion has been demonstrated to be vital for accurate predictions. The PI will address this weakness in drug design by proposing transformative approaches to developing next-generation classical polarizable force fields to overcome current limitations. The challenge is the need for computing power to solve the non-linear problem of parameterizing a polarizable force field while simultaneously incorporating results of biologically relevant scale protein-ligand binding affinity simulations. With the emergence and integration of multicore architectures into commodity desktops, general-purpose graphics processing units (GPU), and special purpose field programmable gate arrays (FPGA), the concept of hybrid computing has entered the HPC arena. Cloud computing systems take advantage of these hybrid resources to generate larger data sets, without the users needing to have knowledge of, expertise in, or control over the technology infrastructure in the ?cloud.? Emerging cloud frameworks are far from transparently accommodating hybrid resources. The PIs will transform an open-source cloud computing framework such as Eucalyptus to transparently use hybrid resources for protein-ligand docking simulations, driven by intelligent scheduling policies based on game theory strategies. Knowledge deduction and use have to be built in and used among application tasks, each influencing the other?s next step. Feedback is also needed to adaptively drive new job generations from the application tasks. In the computational environment envisioned in this project, both knowledge and feedback require transforming human knowledge and discernment into cloud computing services that drive the parameter search. Intellectual merit: From the research point of view the work outlined in this proposal will provide automatic methods and tools for the parameterization of polarizable force field models for pharmaceutical molecules (Aim 1) and the protein-ligand binding affinity of large protein-ligand databases using polarizable force fields (Aim 2). A cross-campus cloud computing system that transparently and intelligently uses hybrid resources, i.e., multi-core and GPUs, in a unified, dynamically adaptable workspace, will support the simulations. This award supports educational and outreach activities to advance students? discovery and understanding of interdisciplinary research. Broader scientific and social impacts: There is great potential in terms of impact on the general scientific, and specifically the modeling communities. Research into more efficient and accurate approaches will significantly boost drug discovery and potentially the discovery of new biomaterials.NON-TECHNICAL SUMMARYThis award is made on a proposal submitted to the Cyberenabled Discovery and Innovation initiative and in partnership with the Office of Experimental Program to Stimulate Competitive Research. This project projects on the From Data to Knowledge, and Virtual Organizations CDI themes. This award supports computational research and education to develop new computer simulation tools to enable the modeling of molecules and their interactions with potential application to the discovery of new drugs and the discovery of new biomaterials. The research involves intense computation to create models for the forces that exist between molecules and for their thermodynamic properties. The PIs will develop a method to harness and utilize different computing resources that are available through the internet using a technique called ?cloud computing.? This award supports educational and outreach activities to advance students? discovery and understanding of interdisciplinary research. Broader scientific and social impacts: There is great potential in terms of impact on the general scientific, and specifically the modeling communities. Research into more efficient and accurate approaches will significantly boost drug discovery and potentially the discovery of new biomaterials.
该奖项是根据提交给网络驱动的发现和创新倡议的一项提案而颁发的,该提案是与激发竞争性研究的实验计划办公室合作的。该项目围绕从数据到知识和虚拟组织CDI主题进行项目。该奖项支持计算研究和教育,以开发新的可极化力场,旨在应用于新药发现。开发的工具也可能对新生物材料的发现产生影响。分子模拟在新药开发中发挥着不可或缺的作用。作为实验的补充,计算努力通过筛选具有高结合亲和力、特异性和药理学特性的小分子来加快发现过程。感兴趣的基本量是结合亲和力,这与与结合反应相关的自由能变化严格相关。目前的方法使用经验势能函数或力场来表示组成原子/分子物种之间的物理相互作用。相互作用的静电部分以平均场的方式处理,在整个结合过程中,分子结构的给定位置上的部分电荷被视为固定的;没有明确的解释不同配体和蛋白质构象的电荷分布的不同性质,这是由量子力学物理决定的,量子力学的包含已被证明对准确的预测至关重要。PI将通过提出开发下一代经典可极化力场的变革性方法来解决药物设计中的这一弱点,以克服目前的限制。挑战是需要计算能力来解决非线性问题,即在将可极化力场参数化的同时,结合生物相关规模的蛋白质-配体结合亲和力模拟的结果。随着多核体系结构的出现并集成到商用台式机、通用图形处理单元(GPU)和专用现场可编程门阵列(FPGA)中,混合计算的概念已经进入了高性能计算领域。云计算系统利用这些混合资源来生成更大的数据集,而无需用户了解、掌握或控制云中的技术基础架构。新兴的云框架远远不能透明地容纳混合资源。PI将转变Eucalyptus等开源云计算框架,在基于博弈论策略的智能调度策略的驱动下,透明地使用混合资源进行蛋白质-配体对接模拟。知识的演绎和运用都要在应用任务之间内建和运用,相互影响?S下一步。还需要反馈,以便从应用程序任务中自适应地推动新一代工作。在本项目设想的计算环境中,知识和反馈都需要将人类的知识和洞察力转化为推动参数搜索的云计算服务。智力优势:从研究的角度来看,本提案中概述的工作将为药物分子的可极化力场模型的参数化(目标1)和使用可极化力场的大型蛋白质-配体数据库的蛋白质-配体结合亲和力(目标2)提供自动化方法和工具。在统一的、动态适应的工作空间中,透明和智能地使用混合资源(即多核和GPU)的跨园区云计算系统将支持模拟。该奖项支持促进学生进步的教育和外展活动?对跨学科研究的发现和理解。更广泛的科学和社会影响:就对一般科学界,特别是模型界的影响而言,有巨大的潜力。研究更高效、更准确的方法将大大促进药物发现,并有可能发现新的生物材料。非技术性总结该奖项是根据提交给网络使能发现和创新倡议的一项提案而颁发的,并与刺激竞争性研究的实验计划办公室合作。该项目围绕从数据到知识和虚拟组织CDI主题进行项目。该奖项支持计算研究和教育,以开发新的计算机模拟工具,以实现分子及其相互作用的建模,并可能应用于发现新药和发现新的生物材料。这项研究涉及密集的计算,以创建分子之间存在的力及其热力学性质的模型。私人投资机构将开发一种方法,利用一种名为云计算的技术,利用互联网上可用的不同计算资源。该奖项支持促进学生进步的教育和外展活动?对跨学科研究的发现和理解。更广泛的科学和社会影响:就对一般科学界,特别是模型界的影响而言,有巨大的潜力。研究更有效和更准确的方法将大大促进药物发现,并有可能发现新的生物材料。

项目成果

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Sandeep Patel其他文献

STUDY ON THE ASSOCIATION OF HUMAN LEUCOCYTE ANTIGEN-B * 15 ALLELE WITH GENERALIZED AGGRESSIVE PERIODONTITIS IN AN INDIAN POPULATION Dissertation submitted to THE TAMIL NADU DR
研究人类白细胞抗原-B * 15 等位基因与印度人群中广义侵袭性牙周炎的关联,提交给泰米尔纳德邦 DR 的论文
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Sandeep Patel;A. Kubavat;Brijesh Ruparelia;A. Agarwal;A. Panda
  • 通讯作者:
    A. Panda
Pre and Postoperative Analysis of Flatfoot Reconstruction Sparing the Talonavicular Joint.
保留距舟关节的平足重建术前和术后分析。
THE β1 BLOCKER/β3 AGONIST NEBIVOLOL PROTECTS AGAINST ACUTE MYOCARDIAL INFARCTION IN MICE
  • DOI:
    10.1016/s0735-1097(10)61091-1
  • 发表时间:
    2010-03-09
  • 期刊:
  • 影响因子:
  • 作者:
    Juan Pablo Aragon;Sandeep Patel;Marah Elston;John W. Calvert;David Bennett Grinsfelder;David J. Lefer
  • 通讯作者:
    David J. Lefer
CHARACTERIZING THE MICROBIOME IN PATIENTS WITH AND WITHOUT CORONARY ARTERY DISEASE
  • DOI:
    10.1016/s0735-1097(18)30670-3
  • 发表时间:
    2018-03-10
  • 期刊:
  • 影响因子:
  • 作者:
    Robert Anton Ulrich Baumgartner;Adam Fitch;Barbara Methe;Kelvin Li;Brian Firek;Sandeep Patel;Alison Morris;Michael Morowitz;Catalin Toma
  • 通讯作者:
    Catalin Toma
CARDIOMYOPATHY IN ACUTE ISCHEMIC STROKE
  • DOI:
    10.1016/s0735-1097(13)60723-8
  • 发表时间:
    2013-03-12
  • 期刊:
  • 影响因子:
  • 作者:
    Gavin Hickey;Omar Batal;Nadeem Kolia;Sandeep Patel;Bhavna Balaney;Zeina Dardari;Vivek Reddy;Tudor Jovin;Maxim Hammer;Mark Schmidhofer;Hunter Champion
  • 通讯作者:
    Hunter Champion

Sandeep Patel的其他文献

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{{ truncateString('Sandeep Patel', 18)}}的其他基金

CAREER: Development of Non-Additive Lipid Force Fields and Application to the Study of Charged Amino Acid Residues in Lipid Bilayers and the Role of Bilayer-Resident Water
职业:非加性脂质力场的开发及其在脂质双层中带电氨基酸残基和双层驻留水的作用研究中的应用
  • 批准号:
    1149802
  • 财政年份:
    2012
  • 资助金额:
    $ 45万
  • 项目类别:
    Continuing Grant

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