ITR/AP: A Motion Planning Approach for Protein Folding Simulation

该项目将基于机器人运动规划社区最近开发的技术，开发一个研究蛋白质折叠的框架。特别是，这项工作使用了概率路线图（PRM）运动规划技术，该技术已被证明在涉及高维构型空间的问题上非常成功。PRMs为蛋白质科学提供的关键进步是能够有效地探索蛋白质现实模型的大构象转变。这项工作涉及蛋白质科学和信息技术的研究。该项目有两个与蛋白质科学相关的主要研究目标。首先，PRMs有望提供一种计算方法来预测蛋白质的折叠动力学，当天然结构已经已知。这使得PRMs非常适合研究蛋白质折叠动力学中的经典问题，如动力学中间体，动力学陷阱，平行与串联折叠路线，以及与势能景观和拉链过程相关的一般折叠机制问题。其次，PRM与新的ENPOP参数优化策略结合使用，有可能改进用于预测生物分子构象的高分辨率和低分辨率能量模型。该项目有两个与信息技术相关的主要研究目标。两者的动机都是研究蛋白质折叠问题所需的模拟的大量计算需求。首先，需要新的策略和优化技术来支持从数百自由度问题的路线图中有效地提取高质量的路径。其次，由于已知prm可以并行实现，因此它们将利用高性能计算。这将包括c++ STL（标准模板库）的自适应并行版本STAPL的开发和应用。使用STAPL，模拟可以在各种并行和分布式系统上进行优化和运行，并产生接近为每个平台手动优化的代码的性能

This project will develop a framework for studying protein folding that is based on techniques recently developed in the robotics motion planning community. In particular, this work uses Probabilistic Roadmap (PRM) motion planning techniques which have proven to be very successful for problems involving high-dimensional configuration spaces. The key advance that PRMs offer protein science is the ability to efficiently explore large conformational transitions of realistic models of proteins. The work involves research in both protein science and information technology. The project has two main research goals related to protein science. First, PRMs are expected to provide a computational method to predict the folding kinetics of proteins, when the native structure is already known. This makes PRMs ideal for investigating classical problems in protein folding kinetics such as kinetic intermediates, kinetic traps, parallel vs. series folding routes, and general folding mechanism questions related to potential energy landscapes and zippers processes. Second, PRM, used in conjunction with a new ENPOP parameter optimization strategy, has the potential to improve energy models both high-resolution and low-resolution models for predicting biomolecule conformations. The project has two main research goals related to information technology. Both are motivated by the massive computational requirements of the simulations needed to investigate the protein folding questions. First, new strategies and optimization techniques for PRMs will be needed to support the efficient extraction of high quality paths from the roadmaps for problems with hundreds of dof. Second, since PRMs are known to be amenable to parallel implementation, they will utilize high-performance computing. This will include the development and application of an adaptive parallel version of the C++ STL (Standard Templates library) called STAPL. With STAPL, the simulations can be optimized and run on various parallel and distributed systems and yield a performance approaching that of code manually optimized for each platform