Biomolecular Recognition and Binding Mechanisms

我们已经更新了来自PDB的两链界面的结构独特的数据集；http://protein3d.ncifcrf.gov/keskino/].We基于它们的空间结构相似性对界面进行了聚类，而不管它们在蛋白质链上残基的连通性。该数据集比2004年的数据增加了几倍。结构蛋白质数据库中还包括了更多的复合体并对其进行了分类。这个更加多样化的数据集反映了结构数量的增长，特别是基于我们对目前PDB中更多更高分子量蛋白质的统计。新旧数据集的比较表明，与可用的新PDB结构的数量相比，新发现的界面集群的数量增长得更快。这可能表明唯一接口的数量仍未达到其上限。我们将团簇分为三种类型：第一类团簇由父链也相似的相似界面组成。在II型簇中，界面是相似的；然而，衍生界面的亲本蛋白质的整体结构不同。在我们研究的所有II型病例中，聚集的蛋白属于不同的SCOP家族，具有不同的功能。类型III类别引入了接口集群，其中只有接口的一侧相似，而另一侧不同。III型簇表明，一个结合位点可以与一个以上的链相互作用，具有不同的几何形状、大小和组成。蛋白质科学中的一个范例指出，相似的全球结构可能具有相似的功能。我们的观察显示了这一范例的扩展：类似的接口体系结构可能具有不同的功能。就像蛋白质的结构一样，进化重复使用了“良好的”良好的界面结构支架，并使它们适应不同的功能。其功能从酶/抑制剂延伸到毒素和免疫球蛋白。我们没有在II型簇中观察到同源二聚体。这可能是因为单体的尺寸较小，以及覆盖大部分链的两态同二聚体中广泛的界面。我们观察到，全球不同的蛋白质结构以相似的方式结合在一起，产生相似的基序，这是有趣的。显然，有很多种单体可以组合组装的方式。值得注意的是，其中有首选的接口体系结构，这些类似于在单体中观察到的。这一观察既强调了有利基序的数量在性质上是有限的这一观点，也强调了结合和折叠之间的类比。这些现在已经被包括在一个例行程序中，用于预测新的界面、它们的关联模式，从而预测蛋白质的功能。我们已经表明，热点主要出现在大分子络合物的界面上，将结合位置与表面的其余部分区分开来。因此，热点可用于定义结合表位。我们进一步证明了能量热点和结构保守残基之间的对应关系，并提出了结合界面上的保守残基提供了刚性，以最小化结合的熵成本，而周围的残基则形成了一个灵活的缓冲。此外，我们发现类似的残基热点出现在不同的蛋白质家族中，这表明亲和力和特异性并不一定是耦合的：更高的亲和力并不直接意味着更高的特异性。蛋白质表面色氨酸的保守表明极有可能存在结合位点。在较小程度上，Phe和Met的保守也意味着有一个结合位点。对于这三个残基，结合部位有显著的保守性，而暴露表面没有保守性。利用蛋白质-蛋白质界面的数据集，WARE现在正在开发一种计划，通过将已知的界面绘制到这些蛋白质的表面上，直接从蛋白质结构预测蛋白质的功能。此外，在预测p53的有害结构及其与DNA结合时的功能动力学方面的努力仍在继续。我们刚刚解释了为什么P53识别元件在人类基因组上不存在间隔区(或具有一个或两个核苷酸的间隔区)是非常受欢迎的，并表明它与P53二聚体-二聚体和P53-DNA协同作用有关。这应该有助于设计算法来预测人类基因组上的p53识别元件。在实验中，P53识别元件绝大多数没有间隔区。

We have updated our structurally unique data set of two-chain interfaces from the PDB; http://protein3d.ncifcrf.gov/keskino/].We clustered the interfaces based on their spatial structural similarities, regardless of the connectivity of their residues on the protein chains. The data set increased several fold from the one derived in 2004. Additional complexes have been included and classified from the structural protein database. This substantially more diverse data set reflects both the growth in the number of structures and in particular based on our statistics of the larger number of higher molecular weight proteins currently in the PDB. The comparison of the old and new data sets indicates that the number of newly found interface clusters has increased much more rapidly compared to the number of the available new PDB structures. This may suggest that the number of unique interfaces has still not reached its upper limit. We divided the clusters into three types: Type I clusters consist of similar interfaces whose parent chains are also similar. In Type II clusters, the interfaces are similar; however, the overall structures of the parent proteins from which the interfaces derive are different. In all Type II cases that we have studied, the clustered proteins belong to different SCOP families, with different functions. Type III category introduces clusters of interfaces where only one side of the interface is similar but the other side differs. Type III clusters illustrate that a binding site can interact with more than one chain, with different geometries, sizes, and composition. One of the paradigms in protein science states that similar global structures may have similar functions. Our observations suggest an extension of this paradigm: Similar interface architectures may have different functions. As in proteins' structures, evolution has reused "good" favorable interface structural scaffolds and adapted them to diverse functions. The functions extend from enzymes/inhibitors to toxins and immunoglobulins. We did not observe homodimers in Type II clusters. This is probably due to the smaller sizes of the monomers and the extensive interfaces in the two-state homodimers that cover large portions of the chains. Our observation that globally different protein structures associate in similar ways to yield similar motifs, is interesting. Clearly, there is a very large number of ways that monomers can combinatorially assemble. Remarkably, among these there are preferred interface architectures and these are similar to those observed in monomers. This observation both underscores the view that the number of favorable motifs is limited in nature, and highlights the analogy between binding and folding. These have now been included in a routine to predict new interfaces, their mode of associations and consequently the protein function. We have shown that hot spots occur predominantly at the interfaces of macromolecular complexes, distinguishing binding sites from the remainder of the surface. Consequently, hot spots can be used to define binding epitopes. We have further shown a correspondence between energy hot spots and structurally conserved residues and proposed that conserved residues at the binding interfaces confer rigidity to minimize the entropic cost of binding, whereas surrounding residues form a flexible cushion. Furthermore, our finding that similar residue hot spots occur across different protein families suggests that affinity and specificity are not necessarily coupled: higher affinity does not directly imply greater specificity. Conservation of Trp on the protein surface indicates a highly likely binding site. To a lesser extent, conservation of Phe and Met also imply a binding site. For all three residues, there is a significant conservation in binding sites, whereas there is no conservation on the exposed surface. Using the dataset of protein-protein interfaces were are now developing a scheme to predict protein function directly from protein structures by mapping known interfaces onto the surfaces of these proteins. In addition, efforts are continuing in the prediction of the detrimer structure of the p53 and its functional dynamics when bound to the DNA. We have just now rationalized why the p53 recognition elements are highly preferred to occur without spacers (or with a spacer of one or two nucleotides) on the human genome, and shown it to correlate with p53 dimer-dimer and p53-DNA cooperativity. This should assist in devising algorithms for prediction of p53 recognition elements on the human genome. In experiment, p53 recognition elements are overwhelmingly without spacers.