AF: SMALL: Developing Novel Computational Methods for Investigating Protein Dynamics Using a Multi-Scale Approach

蛋白质是细胞的主力，几乎参与了生命的每一个过程。许多蛋白质是灵活的分子，作为其功能的一部分，它们会经历结构变化。换句话说？它们可以通过从小范围运动到大范围运动的变化来假设各种可能的结构（构象）。几十年来，蛋白质的结构和动力学与其功能之间的关系一直是科学家们面临的一个挑战，但在很大程度上仍然没有定论。现有的模拟蛋白质动力学的计算方法可以对原子水平的动态过程进行采样，但它们的实用性有限，因为它们需要大量的计算资源，并且它们只允许在非常小的时间尺度（例如，几百纳秒）上进行相互作用的建模。了解蛋白质结构、动力学和功能之间的联系有助于理解分子机器的功能，并可能有助于药物设计和功能分析。在这项工作中提出了一个有效的大规模探索蛋白质构象变化的计算框架。给定一个蛋白质结构，目的是有效地产生一组不同的构象，代表该蛋白质在生理条件下的低能量景观。该方法可用于探索蛋白质和蛋白质复合物的构象空间，从而更好地了解蛋白质的动力学和功能。为了克服全尺度构象搜索的计算需求，搜索将分两个阶段进行：首先，以一种高效的方式对蛋白质和蛋白质复合物的低能量景观进行快速和近似的几何探索，暂时牺牲小尺度细节以提高效率。近似搜索通过一种新的偏倚方案得到增强，该方案将搜索导向蛋白质更灵活的区域，将巨大的搜索空间缩小到可管理的大小。构象景观的简化表示将得到加强，并辅以详细的、基于物理的模拟，应用于蛋白质中有趣和重要的区域或中间结构。最后一个阶段将利用大规模并行计算。快速、近似的搜索技术和详细的基于物理的模拟方法的结合，将创造一个增强的、更完整的蛋白质低能图景，并将提高对蛋白质如何发挥其功能的理解。该方法可应用于与蛋白质相互作用和合理药物设计有关的问题。该项目的广泛影响部分是由于蛋白质在几乎所有基本生物功能中的核心作用。这个项目解决了生物研究界的一个重要问题。教育和推广活动将通过以下方式实施：a)与麻省大学波士顿分校科学与数学学院CS系和其他系的成员进行跨学科合作。b)培训和指导本科生和研究生的研究，包括妇女和来自科学领域代表性不足群体的学生。c)帮助在马萨诸塞大学波士顿分校建立一个生物信息学研究和教学项目。

Proteins are the workhorses of the cell, involved in virtually every process in life. Many proteins are flexible molecules that undergo structural changes as part of their function. In other words ? they can assume various possible structures (conformations) via changes that range from small-scale movements to large domain motions. The question of how the structure and dynamics of proteins relate to their function has challenged scientists for several decades but still remains largely open. Existing computational methods for simulating protein dynamics can sample atomic level dynamic processes, yet their usefulness is limited as they require large computational resources, and they only allow for modeling of interactions that take place on very small time scales (e.g., several hundreds of nanoseconds). There is promise that understanding the connection between protein structure, dynamics and function can contribute a lot to the understanding of how molecular machines function and may aid in drug design and functional analysis. A computational framework for an efficient large-scale exploration of protein conformational changes is proposed in this work. Given a protein structure, the aim is to efficiently generate a diverse set of conformations representing the low energy landscape of this protein under physiological conditions. The suggested methodology can be used to explore the conformational space of proteins and protein complexes and gain better understanding of protein dynamics and function. To overcome the computational demands of a full scale conformational search, the search will be done in two stages: first, conduct a fast and approximate geometry-based exploration of the low energy landscape of proteins and protein complexes in an efficient way, temporarily sacrificing small-scale details for efficiency. The approximate search is enhanced with a novel biasing scheme that drives the search towards more flexible regions of the protein, reducing the huge search space into a manageable size. The reduced representation of the conformational landscape will be enhanced and complemented with detailed, physics based simulations applied to interesting and important regions in the proteins or to intermediate structures. This last stage will take advantage of massive parallel computing. The combination of fast, approximate search techniques and detailed physics-based simulation methods will create an enhanced, more complete picture of the low-energy landscape of those proteins and will improve understanding about how proteins perform their function. The methodology can be applied to problems related to protein interactions and rational drug design.The broader impact of this project is partly due to the central role of proteins in virtually every basic biological function. This project addresses a significant question of the biological research community. Educational and outreach activities will be implemented through the following: a) Interdisciplinary collaborations with members of the CS department and other departments in the College of Science and Mathematics at UMass Boston. b) Training and mentoring the research of undergraduate and graduate students, including women and students from under-represented groups in science. c) Help setting up a Bioinformatics research and teaching program at UMass Boston.