GPU Accelerated Protein Docking Software with Flexible Refinement

描述(由申请人提供)：在几乎所有的生物途径中，蛋白质-蛋白质相互作用涉及多个点。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。 由于用X射线结晶学确定蛋白质络合物的结构昂贵且速度慢，因此开发计算对接方法非常重要，这种方法可以从组分蛋白质的结构或同源模型出发，确定它们的络合物的结构。因此，制药行业对蛋白质对接方法的需求越来越大。根据预测相互作用关键评估(CAPRI)的结果，由波士顿大学开发并授权给Acpharis的全球蛋白质对接竞赛Piper是目前可用的最好的蛋白质对接计划。一个主要问题是，Piper生成的结构的灵活改进需要计算资源，而这些资源在工业中通常是不可用的。该方案的总体目标是开发高效灵活的求精方法，并在GPU上实现计算代价高昂的步骤。改进将采用两种新的算法。首先，给定一个簇定义的假定界面，Acpharis将开发一个程序来识别界面中的“关键”可变侧链及其潜在的构象状态。其次，我们将开发一种蒙特卡罗最小化算法用于柔性精化，该算法结合了在所选侧链旋转体空间中的搜索和基于流形概念的旋转/平移空间中的创新最小化方法。一些由此产生的结构将受到进一步的改进，包括骨干松弛。除了使用更强大的灵活细化算法外，还将通过在GPU上实现耗时的对接和细化组件来实现进一步的加速。分析算法，我们发现了两个这样的组件，即(1)在对接中使用快速傅立叶变换(FFT)的相关性计算，以及(2)在灵活的精化步骤中的非键合能量评估。对于对接步骤，我们将在CPU上执行旋转和网格分配，而在GPU上计算FFT和滤波。加速能源评估步骤将需要更改底层数据结构，并以允许并行能源评估的方式将工作静态映射到GPU线程。通过上述算法和架构加速，我们可以预期，单CPU和2个NVIDIA Fermi GPU卡将在相同的时间内解决之前在128个CPU集群上需要几个小时的对接和优化问题。这样的系统目前可以组装成3500美元，对于小型制药初创公司或计算化学单位来说，这显然是触手可及的。 公共卫生相关性：了解蛋白质之间的相互作用对于某些药物和生物制品的发现至关重要。这项建议的目标是通过在具有成本效益的图形处理单元上实施的新颖计算方法来获得信息。(GPU)。

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).