PROTEIN STRUCTURE MODELING USE BOTH EVOLUTIONARY RELATIONSHIPS AND PHYSICS-BA

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 结构基因组计划的目标是将所有蛋白质序列放在“建模”中 一个已知结构的距离。为了充分利用这一爆炸 信息，基于模板结构的蛋白质结构预测将 需要改进的地方。在检验蛋白质同源模型的过程中 第五轮关键评估中高级别小组的程序 蛋白质结构预测(CASP5)以及我们自己的一些程序， 我们认识到一些方法可以促进这一特定蛋白质区域的研究 结构预测。因为蛋白质序列与 未知结构(靶标)和已知结构的蛋白质序列 (模板)减少，序列比对方法的性能介于 目标和模板也减少了。这一下降在以下情况下尤为显著 序列同源性低于~35%。如果正确的序列比对可以 ，那么就可以得到更好的蛋白质结构模型 建造的。这些更好的模型使它们更有可能成为 使用基于物理的模型进行优化，或者通过基于低分辨率晶格的 模型、全原子分子力学(MM)模型或两者。要实现这些目标 目标，我们首先选择构建一个结构预测“管道”， 只需要最少的步骤。这将确保每一步都可以在以后 接受检查，以确定推进该方法的方法。流程图如下所示 揭示今年夏天我们将在CASP6中使用和测试的方法。该计划 “PROBA”用于生成替代路线。通常，我们将输出 100-500条替代路线。根据这些路线，结构模型是 使用程序建模器版本6.2构建。这些结构模型是 然后用ProsaII中存在的统计潜力进行评估，可能 其他。已经编写了Perl脚本来实现程序之间的流动。 然后对具有最低对相互作用能的模型进行目测检查 患有VMD。模型中不是基于模板构建的循环区域或 其他形成不良的二级结构元素随后将使用 使用MMTSB工具集增强了采样方法。(关于蛋白质的研讨会 使用该工具集的结构预测于#年夏天在PSC举行 2003年。)这些增强的采样方法包括模拟退火法 初始结构或副本交换模拟的集合。《能量》 然后使用MM-Generalized 与生俱来的潜力和聚集性，以确定最接近原生的结构。 初步结果可在www.psc.edu/BioMed/Research/Biostr上查阅。 随着我们的专业知识随着这些软件包的增加，我们将开始 自动化这个过程，着眼于开发一个用户网关，以允许 广大研究界要快速有效地制定合理的 基于同源的分子模型为他们的系统。虽然目前有一个 提供这些服务的站点数量，来自先前CASP的结果 实验已经清楚地表明，多重序列的质量 比对与次优比对的识别相结合导致 超凡的成绩。因此，我们预计这条管道将是有效的。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Structural genomic programs aim to place all protein sequences within "modeling distance" of a known structure. To take full advantage of this explosion of information, protein structure predictions based on template structures will need to be improved. In the course of examining protein homology modeling procedures of high-ranking groups from Round 5 of the Critical Assessment of Structure Prediction of Proteins (CASP5) as well as some procedures of our own, we recognized some methods that may advance this particular area of protein structure prediction. As the sequence identity between a protein sequence with unknown structure (the target) and the protein sequence with known structure (the template) decreases, the performance of sequence alignment methods between target and template also decreases. This decrease is particularly notable when the sequence identity goes below ~35%. If a correct sequence alignment could be achieved, then considerably better protein structural models could be constructed. These better models make it more likely that they can then be refined with physics-based models, either through low-resolution lattice based models, all-atom molecular mechanical (MM) models or both. To achieve these goals, we first chose to construct a structure prediction "pipeline" that required a minimal amount of steps. This would insure that each step can later be examined to determine ways to advance the method. The flowchart shown below reveal the methods we will using and testing this summer in CASP6. The program "probA" is used to generate alternative alignments. Typically we will output 100-500 alternative alignments. From these alignments, structural models are constructed with the program MODELLER version 6.2. These structural models are then assessed with the statistical potentials present in ProsaII and possibly others. Perl scripts have been written to enable the flow between programs. The models with the lowest pair interaction energy are then visually examined with VMD. Loop regions in the model that are not built from the template or other poorly formed secondary structure elements will then be refined using enhanced sampling methods with the MMTSB toolset. (A workshop on protein structure prediction using the toolset was held at the PSC in the summer of 2003.) These enhanced sampling methods include simulated annealing of an ensemble of initial structures or replica-exchange simulations. The energies of the resulting structures will then be calculated with the MM-Generalized Born potential and clustered to determine structures closest to the native. Initial results are available at www.psc.edu/biomed/research/biostr. As our expertise increases with these software packages, we will begin automating this process with an eye towards developing a user gateway to allow the general research community to quickly and effectively develop reasonable homology-based molecular models for their systems. While there are currently a number of sites offering these services, the results from prior CASP experiments have clearly shown that the quality of the multiple sequence alignment coupled with the identification of suboptimal alignments leads to superior results. Thus, we expect this pipeline to be effective.