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.

研究将集中在计算生物学的两个领域-（1）探索生物分子构象空间的方法的应用，和（2）开发方法，以帮助在蛋白质结构的测定可溶性蛋白质和膜蛋白质。了解蛋白质折叠是重要的，在许多情况下，如蛋白质设计和更普遍的问题，找到一般规则有关的折叠过程的主要蛋白质的序列。计算机模拟蛋白质折叠的一个主要问题是，动力学发生在几十年的时间，从亚皮秒到毫秒甚至秒。我们采用的方法是限制蛋白质的运动，使其在固定数量的时间步长内从初始状态（如未折叠状态）移动到最终折叠状态。这种“轨迹退火”方法能够探索许多可能的途径，通过这些途径蛋白质可以折叠，同时保持系统处于生理温度。我们将继续我们目前的研究轨迹退火，并将其应用到两个小的蛋白质，其折叠特性已被广泛的实验采样。轨迹退火模拟提供了根据各种折叠途径的概率变化来解释这些实验的可能性，这些变化在实验中很难观察到。生物序列信息积累的快速增长给结构生物学社区带来了强大的压力，以产生具有高生物学活性的新基因的结构信息。吞吐量在实验上，大规模的X射线晶体学和NMR测量现在的目标是在几十年甚至几年内确定所有（1000到10000）可用的蛋白质折叠。然而，仍有许多可溶性蛋白质难以结晶，并且NMR方法可能对其无效。结构同源性是一个非常强大的工具，通过它，我们可以尝试通过计算机将功能分配给新基因，这些新基因与已知基因在序列同源性方面只有很小的联系。我们将建立在我们目前的从头计算结构预测的计算方法，通过添加额外的物理信息包含在小角X射线散射（SAXS）数据。这将提高我们发现给定蛋白质与已知结构的蛋白质的结构同源物的能力，但对于这些蛋白质，几乎没有或没有序列同源性。 我们还将扩展我们的方法重建低分辨率电子密度图从小角度散射数据的X射线散射膜蛋白结合在小泡。 数据库中约25%的基因序列编码膜蛋白的表达。 虽然已经确定了几千种可溶性蛋白质的结构，但由于蛋白质的结晶困难，只有少数膜蛋白质的结构是已知的，这些蛋白质在周围脂质的疏水环境下自然稳定。 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，其中包含有关蛋白质的形状和大小的信息，以及囊泡上的数据，以及囊泡散射和蛋白质散射之间的X射线干涉图案。 通过制备囊泡大小可以变化的制剂，我们相信我们可以扩展我们目前的重建方法，从囊泡贡献中挑选出散射的蛋白质贡献，从而获得膜蛋白的低分辨率结构信息。