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A Fast Fourier Transform correlation approach to protein mapping: Development and applications.

机译：快速傅立叶变换相关方法进行蛋白质作图：开发和应用。

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摘要

Computational protein mapping moves molecular probes - small organic molecules containing various functional groups - around the protein surface, finds favorable positions using empirical free energy functions, clusters the conformations, and ranks the clusters on the basis of the average free energy. Mapping is important for finding "hot spots", regions of the protein surface that are major contributors to the binding free energy making them prime targets in drug design. The FTMAP protein mapping algorithm performs a global search of the entire protein surface. The search is based on the extremely efficient Fast Fourier Transform (FFT) correlation approach which can sample billions of probe positions on dense translational and rotational grids. The novelty of the FTMAP algorithm is that it incorporates a detailed energy expression resulting in very accurate identification of low energy probe clusters. Overlapping clusters of different probes are defined as consensus sites. The largest consensus sites are generally located at the most important subsites of protein binding sites, and the nearby smaller consensus sites identify other important subsites. Mapping results are presented for a variety of proteins. The X-ray structures of porcine pancreatic elastase and thermolysin have been solved in aqueous solutions of several organic solvents, and FTMAP is shown to reproduce the experimental data with remarkable accuracy. The mapping of renin, a long standing pharmaceutical target for the treatment of hypertension, yields consensus sites that trace out the shape of the first approved renin inhibitor, aliskiren. Applying FTMAP to the influenza virus M2 proton channel identifies the potential binding regions for small molecules and predicts the correct binding pose for the drug amantadine. FTMAP is also shown to capture the critical binding "hot spots" in the interface regions of drug targets that participate in protein-protein interactions, including interleukin-2, Bcl-xL, MDM2, HPV 11 E2, ZipA, and TNF-alpha. For all these targets, the high ranked consensus sites identify the "hot spots" that can potentially bind small molecular inhibitors. The development of a new interaction potential which improves the accuracy of both mapping and docking results is also described.
机译：计算性蛋白质作图将分子探针-包含各种官能团的有机小分子-围绕蛋白质表面移动，利用经验自由能函数找到有利位置，对构象进行聚类，并根据平均自由能对聚类进行排名。定位对于发现“热点”非常重要，“热点”是蛋白质表面区域中结合自由能的主要贡献者，使它们成为药物设计的主要靶标。 FTMAP蛋白质作图算法对整个蛋白质表面进行全局搜索。该搜索基于极其高效的快速傅立叶变换（FFT）相关方法，该方法可以在密集的平移和旋转网格上采样数十亿个探针位置。 FTMAP算法的新颖之处在于它结合了详细的能量表达，可非常准确地识别低能量探针簇。不同探针的重叠簇定义为共有位点。最大的共有位点通常位于蛋白质结合位点最重要的亚位点，附近的较小的共有位点则确定其他重要的亚位点。给出了多种蛋白质的作图结果。猪胰腺弹性蛋白酶和嗜热菌蛋白酶的X射线结构已在几种有机溶剂的水溶液中溶解，并且FTMAP被证明可以以极高的精确度再现实验数据。肾素的定位图是长期治疗高血压的药物靶标，可产生共有位点，该位点可追踪第一个获准使用的肾素抑制剂阿利吉仑的形状。将FTMAP应用于流感病毒M2质子通道可识别小分子的潜在结合区域，并预测金刚烷胺药物的正确结合姿势。 FTMAP还显示可捕获参与蛋白质-蛋白质相互作用的药物靶标（包括白介素-2，Bcl-xL，MDM2，HPV 11 E2，ZipA和TNF-alpha）的界面区域中的关键结合“热点”。对于所有这些靶标，排名较高的共有位点识别出可能结合小分子抑制剂的“热点”。还描述了开发新的交互潜力的方法，该方法可以提高映射和对接结果的准确性。

著录项

  • 作者

    Chuang, Gwo-Yu.;

  • 作者单位

    Boston University.;

  • 授予单位 Boston University.;
  • 学科 Chemistry Biochemistry.;Engineering Biomedical.;Biophysics General.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 119 p.
  • 总页数 119
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 生物化学;生物物理学;生物医学工程;
  • 关键词

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