EAGER: Visualization of Protein Folding for Nano-Machine Design

这项探索性研究早期概念资助（EAGER）奖的研究目标是对现有方法进行可视化改进，以支持虚拟蛋白质操作作为制定设计理论的实验框架。研究人员将整合已知机制限制的见解，以创造更有效的蛋白质折叠算法，从而使纳米机器设计成为可能。在这个高风险、高回报的项目中，这个过程将在专为蛋白质折叠设计的高性能计算平台上可视化。结点的样条模型——包括它们的多边形控制结构——将被可视化。用户将能够快速进行广泛的“假设”视觉实验，以了解不同蛋白质操作的3D设计后果。可视化功能将通过关注固有交叉点和自交叉点所产生的设计特性，激发新的直觉和新颖的猜想。设计师将能够有效地探索比现有算法方法更丰富的设计空间。因此，可视化、工程设计和高性能计算构成了本实验改进蛋白质折叠算法的基础。如果已经在小规模原型中显示的性能改进能够成功地转移到实际的复杂模型中，那么这项工作将对许多社区产生重大影响，并对工业产生重大影响。这些算法目前是整个生物分子、医学和制药社区广泛研究的主题。在教育学生方面也会有更广泛的影响，因为参与其中的学生将有机会使用来自工业合作伙伴IBM的实际复杂和大量的数据集。学生们还将接触到最先进的高性能计算平台的复杂性，比如IBM提供的。

The research objective of this EArly-Concept Grants for Exploratory Research (EAGER) award is to create visualization refinements to an existing methodology that will support virtual protein manipulation as an experimental framework for formulating design theory. The investigators will integrate insights from known constraints on mechanisms to create more efficient protein folding algorithms so as to enable nano-machine design. In this high-risk, high-pay-off project, the process will be visualized on high performance computing platforms that have been designed for protein folding. Spline models of knots - inclusive of their polygon control structures - will be visualized. The user will be able to quickly conduct extensive 'what-if' visual experiments to understand the 3D design ramifications of differing protein manipulations. The visualization capabilities will prompt new intuition and novel conjectures by focusing attention on the design properties arising from the inherent crossings and self-intersections. The designer will be able to efficiently explore a much richer design space than can be achieved by existing algorithmic methods. Hence, visualization, engineering design, and high performance computing form the foundations for this experiment to improve protein folding algorithms. If successful and the performance improvements that have already been shown within small scale prototypes can be transferred to realistically complex models, then this work would have significant impact across numerous communities, with substantial impact in industry. These algorithms are currently the subject of extensive study throughout the bio-molecular, medical and pharmaceutical communities. There will also be a broader impact in educating students, as the students involved will have the opportunity to work with realistically complex and voluminous data sets from the industrial partner, IBM. Students will also be exposed to the complexity of the state of the art high performance computing platforms, such as IBM is providing.