GPU Accelerated Protein Docking Software with Flexible Refinement

具有灵活细化功能的 GPU 加速蛋白质对接软件

基本信息

批准号：
8394398
负责人：
MARTIN C HERBORDT
金额：
$ 10.49万
依托单位：
ACPHARIS, INC.
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-09-28 至 2014-09-27
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8394398
关键词：
Academia Acceleration Address Affinity Algorithms Architecture Biological Biological Models Boston Code Collaborations Communication Complex Computer software Computing Methodologies Consult Data Development Disease Pathway Docking Drug Delivery Systems Drug Industry Evaluation Fourier Transform Goals Homology Modeling Hour Industry Letters Licensing Maps Market Research Marketing Methodology Methods Molecular Conformation Molecular Models Pathway interactions Performance Pharmaceutical Preparations Pharmacologic Substance Procedures Process Production Proteins Relaxation Research Contracts Rotation Running Side Specificity Speed Staging Structure System Therapeutic Intervention Time Universities Vertebral column Work X-Ray Crystallography antibody engineering base commercialization computational chemistry computing resources cost effective cost effectiveness design extracellular flexibility innovation molecular modeling novel programs protein complex protein protein interaction receptor simulation

项目摘要

DESCRIPTION (provided by applicant): Protein-protein interactions are involved at multiple points in virtually all biological pathways. Understanding such interactions is also important for the design of biologics that can target extracellular receptors with high affinity and specificity. Since determining the structure of protein complexes by X-ray crystallography is expensive and slow, it is important to develop computational docking methods that, starting from the structures of component proteins or homology models, can determine the structure of their complexes. Accordingly, there is increasing demand for protein docking methods in the pharmaceutical industry. Based on the results of CAPRI (Critical Assessment of Predicted Interactions), a worldwide protein docking competition, PIPER, developed at Boston University and licensed to Acpharis, is the best protein-protein docking program currently available. A major problem is that the flexible refinement of the PIPER-generated structures requires computational resources that are generally not available in industry. The general goal of this proposal is to develop efficient flexible refinement methods, and to implement the computationally expensive steps on GPUs. The refinement will employ two novel algorithms. First, given a putative interface defined by a cluster, Acpharis will develop a program to identify the "key" variable side chains in the interface and their potential conformational states. Second, we will develop a Monte Carlo minimization algorithm for flexible refinement, which combines search in the space of the selected side chain rotamers with an innovative minimization method in the rotational/translational space based on manifold concepts. A number of the resulting structures will be subjected to further refinement involving backbone relaxation. In addition to the use of more powerful flexible refinement algorithms, further speed-up will be achieved by implementing the time consuming components of both docking and refinement on GPUs. Profiling the algorithms we have found two such components, namely (1) correlation calculations that use fast Fourier transforms (FFTs) in docking, and (2) the non-bonded energy evaluation in the flexible refinement step. For the docking step we will perform rotation and grid assignment on the CPU while the FFT and filtering will be computed on the GPU. Acceleration of the energy evaluation steps will require changing the underlying data structures and statically mapping the work onto GPU threads in a way that allows parallel energy evaluations. With the above algorithmic and architectural speed-up, we can expect that a docking and refinement problem that previously required several hours on a 128 CPU cluster will be solved in the same amount of time by a single CPU and 2 NVIDIA Fermi GPU cards. Such a system can currently be assembled for $3500, which is clearly within reach for small pharmaceutical start-up companies or computational chemistry units. PUBLIC HEALTH RELEVANCE: Understanding protein-protein interactions is crucial for discovery of certain drugs and biologics. The goal of this proposal is obtaining the information by novel computational methods implemented on cost effective graphic processing units. (GPUs).

描述（由申请人提供）：几乎所有生物途径中的多个点都涉及蛋白质-蛋白质相互作用。了解这种相互作用对于设计能够以高亲和力和特异性靶向细胞外受体的生物制剂也很重要。由于通过X射线晶体学确定蛋白质复合物的结构既昂贵又缓慢，因此开发计算对接方法非常重要，该方法从组分蛋白质的结构或同源模型开始，可以确定其复合物的结构。因此，制药行业对蛋白质对接方法的需求不断增加。根据全球蛋白质对接竞赛 CAPRI（预测相互作用的批判性评估）的结果，由波士顿大学开发并授权给 Acpharis 的 PIPER 是目前可用的最佳蛋白质-蛋白质对接程序。一个主要问题是 PIPER 生成的结构的灵活细化需要工业上通常不可用的计算资源。该提案的总体目标是开发高效灵活的细化方法，并在 GPU 上实现计算量大的步骤。改进将采用两种新颖的算法。首先，给定一个由簇定义的假定界面，Acpharis 将开发一个程序来识别界面中的“关键”变量侧链及其潜在的构象状态。其次，我们将开发一种用于灵活细化的蒙特卡洛最小化算法，该算法将所选侧链旋转异构体空间中的搜索与基于流形概念的旋转/平移空间中的创新最小化方法相结合。许多由此产生的结构将受到涉及主链松弛的进一步细化。除了使用更强大、更灵活的细化算法之外，通过在 GPU 上实现对接和细化的耗时组件，还将实现进一步的加速。对算法进行分析，我们发现了两个这样的组件，即（1）在对接中使用快速傅里叶变换（FFT）的相关计算，以及（2）灵活细化步骤中的非键能量评估。对于对接步骤，我们将在 CPU 上执行旋转和网格分配，而 FFT 和过滤将在 GPU 上计算。加速能量评估步骤需要更改底层数据结构，并以允许并行能量评估的方式将工作静态映射到 GPU 线程上。通过上述算法和架构加速，我们可以预期以前在 128 个 CPU 集群上需要几个小时的对接和细化问题将通过单个 CPU 和 2 个 NVIDIA Fermi GPU 卡在相同的时间内得到解决。目前这样的系统的组装成本为 3500 美元，这对于小型制药初创公司或计算化学单位来说显然是可以承受的。公共卫生相关性：了解蛋白质之间的相互作用对于发现某些药物和生物制剂至关重要。该提案的目标是通过在具有成本效益的图形处理单元上实现的新颖计算方法来获取信息。（GPU）。