Computer Simulation of Protein Structure and Dynamics

蛋白质结构和动力学的计算机模拟

• 批准号: 0140140
• 负责人: Sebastian Doniach
• 金额: $ 30万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-09-01 至 2007-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0140140&HistoricalAwards=false
• 关键词: Computer; Simulation; Protein; Structure; Dynamics

Research will focus on two areas of computational biology - (1)application of methods which explore conformation space for biomolecules, and (2) development of methods to help in protein structure determination both for soluble proteins and for membrane proteins.Understanding protein folding is important in a number of contexts such as protein design and the more general issue of finding general rules relating the folding process to the primary sequence of the protein. A major problem for computer simulation of protein folding is the fact that the dynamics takes place over many decades of time, from sub-picoseconds to milliseconds or even seconds. The approach we adopt is to constrain the motions of a protein so that it moves from an initial state, such as an unfolded state, to a final folded state in a fixed number of time steps. This "trajectory annealing" approach has the capability to explore a number of possible pathways by which a protein may fold while keeping the system at a physiological temperature. We will continue our present studies of trajectory annealing and apply them to two small proteins whose folding properties have been extensively sampled experimentally. The trajectory annealing simulations offer the possibility of interpreting those experiments in terms of changes in probability of various pathways for folding which are very difficult to observe experimentally.As a result of the tremendous progress in large-scale DNA sequencing projects, rapid growth in accumulation of biological sequence information has put strong pressure on the structural biology community to produce structural information for new genes with high throughput. Experimentally, large scale X-ray crystallography and NMR measurements now aim to determine all (1000 to 10000) available protein folds within a few decades or even years. However there remain many soluble proteins which have difficulties in crystallizing and for which NMR methods may be ineffective. Structural homology is a very powerful tool by which we can try to assign functions in silico to new genes which bear only remote if any association with known genes in terms of sequence homology. We will build on our current computational methods of ab initio structure prediction by adding additional physical information contained in small angle x-ray scattering (SAXS) data. This will improve our ability to find structural homologs of a given protein with proteins of known structure but for which there is little or no sequence homology. We will also extend our methods of reconstructing low resolution electron density maps from small-angle scattering data to x-ray scattering from membrane proteins bound in small vesicles. Some 25% of gene sequences in the database code for expression of membrane proteins. Although structures for several thousand soluble proteins have been determined, only a handful of structures for membrane proteins are known owing to the difficulties of crystallizing proteins which are naturally stabilized by the hydrophobic environment of the s urrounding lipids. By embedding a membrane protein in small lipid vesicles, we believe we can generate SAXS data which contains information about the shape and size of the protein in addition to data on the vesicle, and also about the x-ray interference pattern between the scattering from the vesicle and the scattering from the protein. By making a preparation in which vesicle sizes can be varied, we believe we can extend our present reconstruction methods to sort out the protein contributions to the scattering from the vesicle contributions and hence obtain low resolution structural information on the membrane protein.

研究将重点关注计算生物学的两个领域 - （1）应用探索生物分子的构象空间的应用，以及（2）开发用于可溶性蛋白质和膜蛋白质确定蛋白质结构确定的方法。理解的蛋白质折叠在蛋白质的许多上下文中都很重要，例如蛋白质设计和一般规则的范围，很重要。计算机模拟蛋白质折叠的一个主要问题是，动力学发生在数十年的时间内，从子picseconds到毫秒甚至几秒钟。我们采用的方法是限制蛋白质的运动，以便以固定的时间步骤从诸如展开状态的初始状态（例如展开状态）移动到最终折叠状态。这种“轨迹退火”方法具有探索许多可能的途径，蛋白质可以在将系统保持在生理温度下的同时折叠。我们将继续对轨迹退火的研究，并将其应用于两个小蛋白，其折叠特性已通过实验进行了广泛采样。轨迹退火模拟提供了解释这些实验的可能性，以各种折叠途径的变化来解释这些实验，这是很难在实验上观察到的。由于大规模DNA测序项目在大规模DNA测序项目中取得了巨大进展，生物学序列的积累的快速增长使生物学社区的强大压力赋予了与新基因的结构生物学社区的强大压力。在实验上，大规模X射线晶体学和NMR测量值现在旨在确定几十年甚至几年内所有可用蛋白质折叠的（1000至10000）。但是，仍然存在许多可溶性蛋白质，这些蛋白质在结晶方面存在困难，并且NMR方法可能无效。结构同源性是一种非常强大的工具，我们可以尝试将硅中的功能分配给新基因，这些新基因仅在序列同源性方面与已知基因相关的新基因只有远程。我们将通过在小角度X射线散射（SAXS）数据中添加其他物理信息来构建我们当前的Ab Inition结构预测方法。这将提高我们找到具有已知结构蛋白的给定蛋白质的结构同源物的能力，但几乎没有序列同源性。 我们还将扩展将低分辨率电子图从小角度散射数据重建到从小囊泡中结合的膜蛋白的X射线散射的方法。 数据库代码中的大约25％的基因序列用于表达膜蛋白。 尽管已经确定了数千种可溶性蛋白质的结构，但由于结晶蛋白的困难，仅少数几个膜蛋白结构，这些结构被脂质的疏水环境自然稳定。 通过将膜蛋白嵌入小脂质囊泡中，我们相信我们可以生成SAXS数据，除了囊泡的数据以及X射线干扰模式之间，该数据还包含有关蛋白质的形状和大小的信息，以及X射线干扰模式与蛋白质的散射之间的散射。 通过进行囊泡大小可以变化的制备，我们相信我们可以扩展当前的重建方法，以从囊泡贡献中弥补蛋白质的贡献，从而获得膜蛋白上的低分辨率结构信息。