AF: Small: Motion Planning Techniques for Protein Motion

AF：小：蛋白质运动的运动规划技术

• 批准号: 1941530
• 负责人: Nancy Amato
• 金额: $ 19.21万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1941530&HistoricalAwards=false
• 关键词: AF; Small; Motion; Planning; Techniques

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.

蛋白质运动在许多生化过程中起着至关重要的作用。 例如，当蛋白质折叠到其本地功能状态时，它们有时会经历影响其功能的批判性构象变化，例如，诸如疯牛病或阿尔茨海默氏病这样的疾病与蛋白质错误折叠和聚集有关。 当蛋白质与其他分子之间相互作用时，蛋白质在结合状态和未结合状态之间也会发生构象变化。 了解这些运动过程的力学可能会有助于洞悉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，对蛋白质错误折叠和聚集的更好理解有可能深入了解神经退行性疾病，例如阿尔茨海默氏病，帕金森氏病，病毒疾病以及对社会产生重大影响的相关疾病。 该项目的一个重要，更直接的目标是在公开可用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，仿真和分析工具，专门研究并应用一种新的计算方法来研究分子运动，这些分子运动已被开发和验证了初步工作中的实验数据。 该方法代表了分子动力学和蒙特卡洛模拟等方法之间的权衡，这些方法提供了详细的单个折叠轨迹和技术，例如提供全球景观统计的统计机械方法。 该方法源自机器人运动计划方法，它构建了一个编码许多（通常是数千个）运动途径的图。 拟议的工作涉及算法研究，以进一步开发和优化将其应用于蛋白质科学兴趣的问题所必需的技术和研究。 虽然算法研究将由计算机科学家进行，但技术的应用和测试将受益于与当前研究这些问题的实验室的合作。 主要的研究目标包括：（i）开发新的和/或改进的指标和分析技术，以实现构象，途径和路线图，可用于建模更复杂的运动应用。 这些方法将得到验证，并应用于蛋白质过渡，诱饵数据库改进和配体结合。 （ii）用于建模和模拟受限运动的新方法，并在合法需求的领域中融合了更大的键灵活性（当前框架既不支持）。 这些方法将应用于建模蛋白质过渡和配体结合。 （iii）采用高性能计算来增加可以研究系统的大小和复杂性的策略。