EAGER: Visualization of Protein Folding for Nano-Machine Design

EARLY概念探索性研究赠款（EAGER）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。研究人员将从已知的机制约束中整合见解，以创建更有效的蛋白质折叠算法，从而实现纳米机器设计。在这个高风险、高回报的项目中，该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。节点的样条模型-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由固有交叉和自交叉产生的设计特性来促进新的直觉和新颖的设计。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形成了本实验的基础，以改善蛋白质折叠算法。 如果成功，并且已经在小规模原型中显示的性能改进可以转移到实际复杂的模型中，那么这项工作将对许多社区产生重大影响，并对行业产生重大影响。这些算法目前是生物分子、医学和制药界广泛研究的主题。在教育学生方面也将产生更广泛的影响，因为参与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。学生还将接触到最先进的高性能计算平台的复杂性，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.