Motion-Planning Based Techniques for Modeling & Simulating Molecular Motions

编号：0830753 INSTITUTION：德克萨斯工程实验站PI：Amato，Nancy&Amp；Ruchwerger，LawrenceTITLE：基于运动规划的建模和模拟分子运动的技术分子运动在许多生化过程中扮演着重要的角色。由于很难在实验上观察分子运动，因此研究这类问题的计算方法是必不可少的。这项研究探索了一种用于研究分子运动的新的计算方法，该方法是研究人员开发的，并在初步工作中与实验数据进行了验证。这项研究有可能提供一些与蛋白质折叠、稳定性和溶解性相关的重要问题的洞察力。例如，蛋白质的错误折叠和聚集与毁灭性的神经退行性疾病有关，如阿尔茨海默病、帕金森氏病、普里恩疾病和相关疾病。除了出版物外，研究产生的结果还在公开可用的分子运动数据库中与社区共享。蛋白质折叠服务器还允许科学家提交他们自己的蛋白质进行分析：http://parasol.tamu.edu/foldingserver/.The在这项研究中投资的新计算方法代表了分子动力学和蒙特卡罗模拟等方法之间的权衡，这些方法提供了详细的个别折叠轨迹和技术，例如提供全球折叠景观统计的统计机械方法。这种方法建立了一个图形(路线图)，对应于分子能量景观的近似地图，该地图编码了许多(通常是数千条)折叠路径。虽然产生的单个路径没有分子动力学模拟产生的轨迹那么详细，但它们可以用于研究二级结构形成顺序和折叠动力学等性质。该项目的主要研究目标包括开发可应用于蛋白质稳定性和动力学研究的构象和路线图的新的和/或改进的度量和分析技术，以及开发利用高性能计算来增加可研究系统的规模和复杂性的策略。研究人员验证并将这些新技术应用于折叠核心识别、淀粉样蛋白形成、动力学研究和蛋白质的比较分析。

NUMBER: 0830753INSTITUTION: Texas Engineering Experiment StationPI: Amato, Nancy & Rauchwerger, LawrenceTITLE: Motion-Planning Based Techniques for Modeling & Simulating Molecular MotionsMolecular motions play an essential role in many biochemical processes. Since it is difficult to experimentally observe molecular motions, computational methods for studying such issues are essential. This research investigates a novel computational method for studying molecular motions that the investigators have developed and validated against experimental data in preliminary work. The research has the potential to provide insight into a number of important questions related to protein folding, stability, and solubility. For example, protein misfolding and aggregation is associated with devastating neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, prion diseases, and related diseases. In addition to publications, results generated by the research are shared with the community in a publicly available database of molecular motions. The protein folding server also allows scientists to submit their own proteins which will be analyzed for them: http://parasol.tamu.edu/foldingserver/.The new computational method invested in this research 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 folding landscape statistics. This method builds a graph (roadmap) corresponding to an approximate map of the molecule's energy landscape that encodes many (typically thousands) folding pathways. Though the individual pathways produced are not as detailed as trajectories generated from a molecular dynamics simulation, they can be used to study properties such as secondary structure formation order and folding kinetics. The major research goals of this project include the development of new and/or improved metrics and analysis techniques for conformations and roadmaps that can be applied in protein stability and kinetics studies and the development of strategies for employing high-performance computing to increase the size and complexity of the systems that can be studied. The investigators validate and apply these new techniques to folding core identification, amyloid formation, kinetics studies, and comparative analysis of proteins.