An easy-to-use and powerful tool for improved Rosetta comparative modeling of proteins

一个易于使用且功能强大的工具，用于改进 Rosetta 蛋白质比较模型

• 批准号: 9048495
• 负责人: Yifan Song
• 金额: $ 22.49万
• 依托单位: CYRUS BIOTECHNOLOGY, INC.
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9048495
• 关键词: Address; Algorithms; Antibodies; Area; Benchmarking; Cloud Computing; Complex; Computer Simulation; Computer software; Data; Data Set; Development; Docking; Drug Design; Drug Targeting; Ensure; Goals; High Performance Computing; Homology Modeling; Knowledge; Lead; Learning; Linux; Methodology; Methods; Modeling; Motion; Occupations; Output; Performance; Phase; Positioning Attribute; Process; Proteins; Resolution; Sampling; Scheme; Scientist; Sequence Alignment; Structure; System; Testing; Time; Training; Trust; Validation; base; comparative; computer science; computing resources; density; drug discovery; electron density; flexibility; graphical user interface; improved; kinematics; model design; protein structure; protein structure prediction; public health relevance; research study; restraint; skills; small molecule; software development; structural biology; success; tool

 DESCRIPTION (provided by applicant): In this project we aim to improve on the state-of-the-art homology modeling pipeline in the Rosetta software package, strengthen its capability in modeling large proteins and structure refinement with near-atomic resolution density data and sparse NMR data. We will also develop a graphical user interface (GUI) and an easy-to-use backend that will allow a user to easily set up modeling tasks and access large amounts of computing. The success of this project will facilitate drug design and other applications requiring accurate computational models. It will also provide accuracy estimations to inform the user's trust in the output model. The software developed here establishes a framework in which both academic and commercial users without an extensive computational background or the time to learn a complex new command-line tool can interact with the Rosetta modeling software package via a GUI. The three overlapping areas to be investigated here are: 1. Improving homology modeling methods. We will further develop the broken chain kinematics system incorporated in RosettaCM and benchmark against a large dataset collected from previous CASP and CAMEO experiments. A more hierarchical kinematics system will be developed for modeling with known contact information and tested using a dataset with sparse NMR data. 2. Graphical user interface (GUI). One of the challenges of using a modeling software package such as Rosetta is that it requires a large amount of prior training in computer science, including basic Linux skills, software compilation, simple scripting and tabular data manipulation. We will develop a powerful GUI so that interactions with the Rosetta software package become much easier, without sacrificing user control over key aspects of modeling. 3. Cloud computing. Large computing resources are necessary to achieve massive amounts of sampling during structure modeling, and this often leads to more accurate results. However to the access to such computing resources is scarce and expensive to deploy locally. By developing a deployment mechanism that is deliverable on both a local cluster or via cloud computing, we can ensure that tasks that require large amounts of sampling are easily accessible to all scientific users.

 描述（由适用提供）：在此项目中，我们旨在改进Rosetta软件包中最先进的同源性建模管道，以使用近原子分辨率密度数据和稀疏NMR数据来建模大蛋白质和结构改进的能力。我们还将开发图形用户界面（GUI）和易于使用的后端，该界面将使用户可以轻松地设置建模任务并访问大量计算。该项目的成功将有助于药物设计和其他需要的应用 准确的计算模型。它还将提供准确的估算，以告知用户对输出模型的信任。这里开发的软件建立了一个框架，在该框架中，没有广泛的计算背景的学术和商业用户，或者学习复杂的新命令行工具的时间可以通过GUI与Rosetta建模软件包进行交互。要研究的三个重叠区域是：1。改进的同源建模方法。我们将进一步开发在RosettACM和基准测试中融合的破碎链运动系统，以从以前的CASP和CASEO实验中收集的大型数据集中进行基准。将开发一个更分层的运动系统，用于使用已知的联系信息进行建模，并使用具有稀疏NMR数据的数据集进行了测试。 2。图形用户界面（GUI）。使用建模软件包（例如Rosetta）的挑战之一是，它需要大量的计算机科学培训，包括基本的Linux技能，软件编译，简单的脚本和表格数据操作。我们将开发一个强大的GUI，以便与Rosetta软件包的交互变得更加容易，而不会牺牲用户对建模关键方面的控制。 3。云计算。对于在结构建模过程中实现大量抽样是必要的，这通常是必要的，这通常会导致更准确的结果。但是，可以访问此类计算资源，在本地部署很稀缺。通过开发在本地群集或通过云计算上交付的部署机制，我们可以确保所有科学用户都可以轻松访问需要大量采样的任务。