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

这项工作致力于开发一种新的算法框架，首次有效地计算生物分子中的高阶相互作用。创建算法是为了在比以前更大的规模上展示两个重要的应用程序，每个算法代表着通往更大类别进一步可能性的一扇新门：Axilrod-Teller(三体)模拟和Hartree-Fock(4指数)量子水平模拟。这个多学科的项目汇集了计算机科学、蛋白质折叠和量子化学的专家。生物分子模拟通常将复杂的化学系统分解成球和弹簧的力学模型，并增加扭矩项、成对的点电荷静电项和简单的空中弥散(Van Der Waals)相互作用。然而，这样的模型软化未能捕捉到在真实系统中发现的重要的、复杂的非加性相互作用。虽然多体势对于更精确和真实的分子建模的重要性已经被不同的作者所争论，但由于没有一种有效的方法来实现立方或更高的计算，因此它们在小尺寸以外的系统中的评估是不可能的。这项工作增加了一个被称为广义N体问题的计算问题的框架，其中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，利用多个kd-树和其他空间划分数据结构来渐进地和实际地将计算时间降低数个数量级。这项工作扩展了高阶层次级数近似技术的框架，首次演示了如何对高阶相互作用进行快速多极子类型的方法，有效地创建了广义快速多极子方法。算法在选定的生物化学系统中进行了验证，以清楚地说明多体相互作用：氢键和三体弥散相互作用。势函数的参数是使用定制的机器学习方法在由共同PI的实验室生成的双重数据集上获得的：高质量的量子力学基准数据和实验蛋白质结构。目标是演示工作中的多体代码，能够探索在更大范围和更系统地模拟高阶相互作用的效果，比以往任何时候都要尝试。这项工作的学术价值在于阐明了第一个能够准确和可扩展地执行这些基本类型的高阶物理计算的多树多极化方法。潜在的更广泛的影响是能够执行更准确的下一代分子建模，这将对基础生物学和药物设计产生影响。欲了解更多信息，请访问项目网页：http://www.cc.gatech.edu/~agray/gfmm.html

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