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

这项工作追求了一种新的算法框架的开发，该算法框架首次有效地计算了生物分子中的高阶互比。创建算法是为了在更大的尺度上展示重要的应用程序，而不是以前可提取的算法，每种都代表了更大类别的进一步征服的新门：腋teller（3-boty）模拟和Hartree-Fock（4- index）量子级级别的模拟。 多学科的项目BRODER在计算机科学，蛋白质折叠和Quantum Chemistry方面的专家。生物分子模拟通常分解复杂的化学系统Intininto A Balls and-Springs机械模型，增强了扭转量，配对的点式静电术语，以及简单的静电术语，以及简单的PAIRPAIR-WISE-WISE PERASERION（VAN DER WAEL）的相互作用。然而，这种模型通常无法捕获实际系统中重要的，复杂的非加性交互作用。 尽管多种作者对多型电容性对更准确，更真实的分子建模的关键性已经受到了验证，但由于无法实现计算的效率方法无法获得的方法，它们在超越镀金的系统中的评估是不可能的。该框架最初是开发出基于延伸的通用瓶颈统计计算的，利用多个KD-Trees和其他空间分类DATA结构来降低计算时间，这两种计算时间都渐近地和多个数量级。 这项工作通过高阶层次系列近似技术扩展了框架，展示了如何首次进行快速多极型方法进行快速交互，从而有效地创建了加工的快速多极方法。算法在被选中的生物化学系统中被验证，以清楚地选择了多种型号的相互作用：Hyderogen bots和三型合成型。使用自定义的机器学习方法在CO-PI实验室赋予的双重数据集：高质量的量子机械基准数据和实验蛋白质结构上进行的双重数据集，对潜在功能进行了参数。目的是证明工作多体积代码能够证明能够探索在更大尺度上与以前尝试进行更大尺度和更多尝试的高级相互作用的效果。 这项工作的知识分裂是阐明了第一个多树多重方法，能够准确且可靠地执行高阶物理计算的这些基本类型。 潜在的广泛影响是执行更准确的下一代分子建模的能力，对基本生物学和药物设计的影响。 有关更多信息