Accurate accessible cloud software for protein folding for molecular biologists

描述(申请人提供)：大多数药物与蛋白质分子相互作用，以引起细胞反应。传统的药物发现是一个费力且昂贵的实验过程，因此对评估蛋白质功能和加速发现过程的计算方法的需求很高。虚拟药物筛选和基于结构的药物设计代表了对现代药物发现和开发过程非常重要的计算方法。这两种方法都依赖于高分辨率的三级(3D)蛋白质结构，并受到缓慢且往往不成功的实验结构确定方法的阻碍。蛋白质结构预测将通过加速构建药物靶点和生物制药的高置信度结构模型来影响人类健康，这将有助于确定新的治疗策略。然而，目前的方法在预测高分辨率模型方面的能力非常有限，这阻碍了广泛的治疗方法的发现。此外，还需要技术来尽早预测候选药物是否会在开发过程中失败。随着精确度的提高，蛋白质结构预测可以用于降低药物开发成本，并将实验集中在最有希望的候选药物上。DNAStar最近发布了NovaFold，这是在云计算平台上运行的世界领先的i-tasser蛋白质折叠算法的商业版本(杨章，密歇根大学)。自2006年以来，i-tasser赢得了两年一次的蛋白质结构预测关键评估(CASP)比赛，这是一项盲目研究，世界各地的团队将针对未发表的蛋白质结构测试他们的工具。目前的产品被证明对分子生物学社区很有用；然而，它无法利用云计算的广泛并行机会，也不能从蛋白质运动计算中受益，每一种计算都可以显著提高程序预测的准确性。我们建议创建一个大规模并行的软件流水线来预测高分辨率蛋白质的最高频率 适合药物筛选和药物设计项目的结构。在第一阶段，我们将评估使用更快、更深入和更多样化的计算技术来预测更准确结构的最佳方法。这包括评估并行化技术以执行比目前该程序执行的计算至少多100倍的计算，并确认通过使用改进的结构模板支架可以提高预测精度。在第二阶段，我们将使用蛋白质运动来改进加速采样技术。此外，我们将把这种方法与最近蒙特卡罗模拟的进步和分布式计算环境中的大规模并行化结合起来，以进一步提高精度。最终，我们将支持数千个相互关联的模拟，而不是像最初的算法那样，每个蛋白质只有14个模拟。在这项工作结束时，我们将提供一个基于云的软件产品，该产品具有适当的精确度，可用于药物生物模拟项目。

DESCRIPTION (provided by applicant): Most drugs interact with protein molecules to elicit a cellular response. Traditional drug discovery is a laborious and expensive experimental process, so computational approaches to assess protein function and to accelerate the discovery process are in high demand. Virtual drug screening and structure-based drug design represent computational approaches that can be important to the modern drug discovery and development process. Both are reliant on high-resolution tertiary (3D) protein structures and are hampered by the slow and often unsuccessful methods of experimental structure determination. Protein structure prediction is poised to impact human health by accelerating the construction of high-confidence structural models of drug targets and biopharmaceuticals, which will help identify new therapeutic strategies. However, current methods are very limited in their ability to predict high-resolution models, which is preventing broad classes of therapeutics from being discovered. Also, technologies are needed to predict as early as possible if a candidate drug will fail in the development process. With improvements in accuracy, protein structure prediction can be used to lower drug development costs and focus experiments on the most promising drug candidates. DNASTAR recently released NovaFold-a commercial version of the world leading I-TASSER protein folding algorithm (Yang Zhang, U. Michigan) running on a cloud computing platform. Since 2006, I-TASSER has won the biennial Critical Assessment of Protein Structure Prediction (CASP) competition, a blind study where teams worldwide test their tools against unpublished protein structures. The current product is proving useful to the molecular biology community; however, it cannot take advantage of the cloud's extensive parallelization opportunities nor is it adapted to benefit from protein motion calculations, each of which could dramatically improve the accuracy of the program's predictions. We propose to create a massively parallel software pipeline that predicts the highest frequency of high-resolution protein structures that are suitable for drug screening and drug design projects. In Phase I, we will evaluate the best way to use faster, deeper, and more diverse computing techniques to predict more accurate structures. This includes evaluating parallelization techniques to perform at least 100 times more calculations than are performed by the program today and confirming that an increase in prediction accuracy is achievable by using modified structure template scaffolds. In Phase II, we will use protein motion to improve the accelerated sampling technique. Additionally, we will combine that approach with recent Monte Carlo simulation advancements and massive parallelization in a distributed computing environment to enhance the accuracy further. Ultimately, instead of just 14 simulations per protein like the original algorithm, we wil support thousands of interconnected simulations. At the conclusion of this work, we will deliver a cloud-based software product of suitable accuracy to be relied upon for pharmaceutical biosimulation projects.