Computational Mapping of Proteins for Binding of Ligands

描述（由申请人提供）：计算映射移动分子探针-含有各种官能团的小有机分子-在蛋白质表面周围，使用经验自由能函数找到有利位置，将构象聚集在一起，并根据平均自由能对簇进行排序。图谱绘制是基于片段的药物设计的重要一步，它始于可药物结合位点的识别和表征，即蛋白质表面的区域是结合自由能的主要贡献者。基于片段大小化合物的核磁共振和x射线筛选实验，这些“热点”也结合了各种小有机分子，因此蛋白质定位的“命中率”是一个很好的药物预测指标。我们开发了CSMAP算法来重现现有的实验映射结果。在酶的应用中，探针总是聚集在活性位点的主要亚位上，与探针相互作用的氨基酸残基也结合特定的配体（主要是底物和过渡态类似物、抑制剂和产物）。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基于快速傅立叶变换（FFT）相关方法的新型映射算法，该算法适用于成对相互作用势。该方法保留了CSMAP算法的精度，但将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，并研究假设的有效性，即该方法可以识别蛋白质结合位点的最重要区域。除了方法开发之外，本提案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，包括可药物“热点”的识别以及预测与天然配体中功能基团同源的探针的正确位置的能力。我们将考虑一组具有高亲和力和低亲和力配体的蛋白质，绘制蛋白质图，并在x射线结构中将探针-残基相互作用与蛋白质-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库中的化合物作为探针。结果将有助于理解控制小分子在蛋白质功能位点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.