Motion-Planning Based Techniques for Modeling & Simulating Molecular Motions

编号：0830753机构：德克萨斯州工程实验站PI：Amato、Nancy Rauchwerger、Lawrence标题：基于运动规划的建模模拟分子运动技术分子运动在许多生化过程中发挥着重要作用。 由于很难在实验上观察分子运动，研究这些问题的计算方法是必不可少的。 本研究探讨了一种新的计算方法来研究分子运动，研究人员已经开发并验证了初步工作中的实验数据。 这项研究有可能深入了解与蛋白质折叠，稳定性和溶解性相关的一些重要问题。 例如，蛋白质错误折叠和聚集与破坏性神经变性疾病如阿尔茨海默病、帕金森病、朊病毒病和相关疾病相关。 除了出版物外，研究产生的结果还在公开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，这些蛋白质将被分析：http://parasol.tamu.edu/foldingserver/.The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人折叠轨迹和技术，如统计力学方法，提供全局折叠景观统计。 这种方法构建了一个图（路线图），对应于编码许多（通常是数千个）折叠路径的分子能量景观的近似地图。 虽然产生的单个途径不像分子动力学模拟产生的轨迹那样详细，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新的和/或改进的指标和分析技术的构象和路线图，可应用于蛋白质的稳定性和动力学研究和战略的发展，采用高性能计算，以增加的规模和复杂性的系统，可以研究。 研究人员验证并将这些新技术应用于折叠核心鉴定，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.