AF: Small: Motion Planning Techniques for Protein Motion

蛋白质运动在许多生物化学过程中起着至关重要的作用。 例如，当蛋白质折叠到其天然的功能状态时，它们有时会经历影响其功能的关键构象变化，例如，疯牛病或阿尔茨海默病等疾病与蛋白质错误折叠和聚集有关。 蛋白质在与其他分子相互作用时也经历构象变化，因为它们在结合和未结合状态之间转变。 这些运动过程的力学知识可能有助于深入了解蛋白质如何以及为什么错误折叠，如何通过蛋白质结构传达结合调节，以及如何设计更有效的药物。 例如，更好地了解蛋白质错误折叠和聚集有可能提供对神经退行性疾病的洞察，如阿尔茨海默病，帕金森病，朊病毒疾病和对社会有重大影响的相关疾病。 该项目的一个重要且更直接的目标是在公开的蛋白质运动数据库中与社区共享新方法产生的详细结果。该项目将开发新的建模，模拟和分析工具，专门和应用一种新的计算方法来研究分子运动，该方法已在初步工作中根据实验数据开发和验证。 该方法代表了诸如分子动力学和蒙特卡罗模拟的方法（其提供详细的个体折叠轨迹）与诸如统计力学方法的技术（其提供全局景观统计）之间的权衡。 该方法源自机器人运动规划方法，构建了一个编码许多（通常为数千）运动路径的图。 拟议的工作涉及算法研究，以进一步开发和优化技术，以及将其应用于研究蛋白质科学当前感兴趣的问题所需的研究。 虽然算法研究将由计算机科学家进行，但这些技术的应用和测试将受益于与目前正在研究这些问题的实验室的合作。 主要的研究目标包括：（一）新的和/或改进的指标和分析技术的构象，路径和路线图，可应用于建模更复杂的运动应用的发展。 这些方法将被验证并应用于蛋白质转换，诱饵数据库的改进和配体结合。(ii)用于建模和模拟约束运动的新方法，并在合理需要的领域（当前框架均不支持）中纳入更大的债券灵活性。 这些方法将被应用于蛋白质转换和配体结合的建模。(iii)采用高性能计算来增加可研究系统的规模和复杂性的策略。

Protein motions play an essential role in many biochemical processes. For example, as proteins fold to their native, functional state, they sometimes undergo critical conformational changes that affect their functionality, e.g., diseases such as Mad Cow disease or Alzheimer's disease are associated with protein misfolding and aggregation. Proteins also undergo conformational change when interacting with other molecules as they transition between bound and unbound states. Knowledge of the mechanics of these motion processes may help provide insight into how and why proteins misfold, how binding regulation is communicated through the protein structure, and how to design more effective drugs. For example, a better understanding of protein misfolding and aggregation has the potential to provide insight into neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, prion diseases, and related diseases that have a major impact on society. An important, and more immediate, goal of the project is to share detailed results generated by the new methods with the community in a publicly available database of protein motions. This project will develop new modeling, simulation and analysis tools that specialize and apply a novel computational method for studying molecular motions that has been developed and validated against experimental data in preliminary work. This method represents a trade-off between methods such as molecular dynamics and Monte Carlo simulations that provide detailed individual folding trajectories and techniques such as statistical mechanical methods that provide global landscape statistics. The approach, derived from robotic motion planning methods, builds a graph that encodes many (typically thousands) of motion pathways. The proposed work involves both algorithmic research to further develop and optimize the techniques and research necessary to apply them to study issues of current interest in protein science. While the algorithmic research will be performed by computer scientists, the application and testing of the techniques will benefit from collaborations with labs currently studying these problems. The main research goals include: (i) The development of new and/or improved metrics and analysis techniques for conformations, pathways, and roadmaps that can be applied to modeling more complex motion applications. These methods will be validated and applied to protein transitions, decoy database improvement, and ligand binding. (ii) New methods for modeling and simulating constrained motion and incorporating greater bond flexibility in areas of legitimate need (neither supported by current framework). These methods will be applied to modeling protein transitions, and ligand binding. (iii) Strategies for employing high-performance computing to increase the size and complexity of the systems that can be studied.