III-SGER: Algorithms for Next-Generation Protein Modeling: Beyond Pair-wise Interactions

III-SGER：下一代蛋白质建模算法：超越成对相互作用

• 批准号: 0848389
• 负责人: Alexander Gray
• 金额: $ 20万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0848389&HistoricalAwards=false
• 关键词: III; SGER; Algorithms; Next; Generation

This work pursues the development of a new algorithmic framework whichallows for the first time efficient computation of higher-orderinteractions in biomolecules. Algorithms are created to demonstratetwo important applications on much larger scales than were previouslytractable, each representing a new door to a larger class of furtherpossibilities: Axilrod-Teller (3-body) simulation, and Hartree-Fock(4-index) quantum-level simulation. The multidisciplinary projectbrings together experts in computer science, protein folding, andquantum chemistry.Biomolecular simulations usually break down complex chemical systemsinto a balls-and-springs mechanical model augmented by torsionalterms, pair-wise point-charge electrostatic terms, and simplepair-wise dispersion (van der Waals) interactions. However such modelsoften fail to capture important, complex non-additive interactionsfound in real systems. Though the criticality of multi-bodypotentials for more accurate and realistic molecular modeling has beenargued by various authors, their evaluation in systems beyond tinysizes has not been previously possible due to the unavailability of anefficient way to realize the computation, which is cubic or higher.The work augments a framework for computational problems calledGeneralized N-Body Problems, which contains any such higher-orderphysical potential. The framework was originally developed toaccelerate common bottleneck statistical computations based ondistances, utilizing multiple kd-trees and other space-partitioningdata structures to bring down computation times both asymptoticallyand practically by multiple orders of magnitude. This work extendsthe framework with higher-order hierarchical series approximationtechniques, demonstrating how to do a fast multipole-type method forhigher-order interactions for the first time, effectively creating aGeneralized Fast Multipole Method.The algorithms are validated in biochemical systems chosen to clearlyillustrate many-body interactions: hydrogen bonds and three-bodydispersion interactions. Parameters for potential functions areobtained using customized machine learning methods on dual data setsgenerated by the co-PI's labs: high-quality quantum mechanicalbenchmark data and experimental protein structures.The goal is to demonstrate working many-body codes able to explore theeffect of modeling higher-order interactions on a larger scale andmore systematically than ever attempted previously. The intellectualmerit of the work is the elucidation of the first multi-tree multipolemethod capable of accurately and scalably performing these fundamentaltypes of higher-order physics computations. The potential broaderimpact is the ability to perform more accurate next-generationmolecular modeling, with implications for fundamental biology and drugdesign. For further information see the project web page at http://www.cc.gatech.edu/~agray/gfmm.html

这项工作追求一个新的算法框架whichallows的第一次有效的计算生物分子中的高阶相互作用的发展。算法的创建，以证明两个重要的应用程序在更大的规模比以前听话，每一个代表一个新的门到一个更大的类进一步的可能性：Axilrod-Teller（3体）模拟，和Hartree-Fock（4指数）量子级模拟。 生物分子模拟通常将复杂的化学系统分解成一个球弹簧力学模型，并由扭转项、成对点电荷静电项和简单的成对色散（货车van der Waals）相互作用增强。然而，这种模型往往不能捕捉到真实的系统中重要的、复杂的非加性相互作用。 虽然多体势对于更精确和更真实的分子模拟的重要性已经被不同的作者所论证，但是由于没有一种有效的方法来实现立方或更高的计算，它们在超过微小尺寸的系统中的评估以前是不可能的。这项工作增加了一个计算问题的框架，称为广义N体问题，它包含任何这样的高阶物理势。该框架最初是为了加速基于距离的常见瓶颈统计计算而开发的，它利用多个kd树和其他空间划分数据结构来将计算时间渐近地和实际地降低多个数量级。 本工作扩展了框架与高阶hierarchical series approximation技术，演示了如何做一个快速多极类型的方法为高阶相互作用，有效地创建了一个广义快速多极方法，算法在生化系统中进行了验证，选择清楚地说明多体相互作用：氢键和三体色散相互作用。势函数的参数是使用定制的机器学习方法在co-PI实验室生成的双数据集上获得的：高质量的量子力学基准数据和实验蛋白质结构。目标是展示工作的多体代码能够探索更大规模和更系统地建模高阶相互作用的效果。 intellectualmerit的工作是阐明了第一个多树multipolemethod能够准确和可扩展地执行这些基本类型的高阶物理计算。 潜在的更广泛的影响是能够进行更准确的下一代分子建模，对基础生物学和药物设计的影响。 欲了解更多信息，请访问项目网页http://www.cc.gatech.edu/~agray/gfmm.html