Computational Mapping of Proteins for Binding of Ligands

描述(申请人提供)：计算映射在蛋白质表面周围移动分子探测器-包含各种功能基团的小有机分子-使用经验自由能函数找到有利位置，聚集构象，并根据平均自由能对聚集进行排序。作图是基于片段的药物设计中的一个重要步骤，它从识别和表征可药物结合部位开始，即蛋白质表面主要贡献结合自由能的区域。根据对碎片大小的化合物的核磁共振和X射线筛选实验，这样的“热点”还结合了各种有机小分子，因此蛋白质图谱中的“命中率”是一个很好的可药性预测指标。我们已经开发了CSMAP算法，该算法重现了可用的实验映射结果。在酶的应用中，探针总是聚集在活性部位的主要亚位，与探针相互作用的氨基酸残基也与特定的配体(主要是底物和过渡态类似物、抑制剂和产物)结合。该方法也适用于非酶蛋白，并提供了结合位点的详细信息。我们最近开发了一种新的映射算法，该算法基于快速傅立叶变换(FFT)相关方法，使用成对相互作用势。该方法保持了CSMAP算法的精度，但计算时间减少了两个数量级。这将使我们有机会绘制非常大的蛋白质集，并研究该方法可以识别蛋白质结合位点最重要区域的假设的有效性。除了方法开发，这项建议的总体目标是分析和传播在蛋白质结合位点上获得的信息，并严格评估其对基于片段的药物设计的价值，包括识别可用药的“热点”和预测与天然配体中的功能基团同源的探针的正确位置的能力。我们将考虑一组既具有高亲和力又具有较低配体的蛋白质，绘制蛋白质图，并将探针-残基相互作用与X射线结构中的蛋白质-配体相互作用进行比较。预计最大的共识位点将位于对高亲和力配体结合至关重要的“热点”。为了研究特定探针的结合位置与天然配体中相似官能团的位置之间的关系，我们将使用这些配体的片段和扩展片段文库中的化合物作为探针。结果将有助于理解支配蛋白质功能部位小分子弱特异性结合的原理和作图方法的潜在局限性。

DESCRIPTION (provided by applicant): Computational 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 an important step in fragment-based drug design, which starts with the identification and characterization of druggable binding sites, i.e., regions of the protein surface that are major contributors to the binding free energy. Based on NMR and X-ray screening experiments with fragment-sized compounds, such "hot spots" also bind a variety of small organic molecules, and hence the "hit rate" in protein mapping is a good predictor of druggability. We have developed the CSMAP algorithm that reproduces the available experimental mapping results. In applications to enzymes, the probes always cluster in major subsites of the active site, and the amino acid residues that interact with the probes also bind the specific ligands (primarily substrate and transition state analogues, inhibitors, and products). The method also applies to non-enzyme proteins, and provides detailed information on the binding sites. We have recently developed a novel mapping algorithm based on the Fast Fourier Transform (FFT) correlation approach that works with pairwise interaction potentials. The method retains the accuracy of the CSMAP algorithm, but reduces computing times by two orders of magnitude. This will give us the opportunity to map very large sets of proteins, and study the validity of the hypothesis that the method can identify the most important regions of protein binding sites. In addition to method development, the general goals of this proposal are the analysis and dissemination of the information obtained on protein binding sites, and a rigorous assessment of its value for fragment-based drug design, including the identification of druggable "hot spots" and the ability to predict correct positions for probes that are homologous to a functional group in the native ligand. We will consider a curated set of proteins with both high affinity and less potent ligands, map the proteins, and compare the probe-residue interactions to the protein-ligand interactions in the x-ray structures. It is expected that the largest consensus sites will be at "hot spots" that are critical for the binding of high affinity ligands. To study the relationship between the bound positions of specific probes and the positions of similar functional groups in the native ligand we will use both fragments of these ligands and compounds from an extended fragment library as probes. Results will help to understand both the principles that govern the weakly specific binding of small molecules in functional sites of proteins and the potential limitations of the mapping method.