Biomolecular Recognition and Binding Mechanisms

我们从PDB中更新了结构独特的双链接口数据集；http://protein3d.ncifcrf.gov/keskino/].We根据其空间结构相似性对界面进行聚类，而不考虑其在蛋白质链上的残基的连通性。该数据集比2004年的数据集增加了几倍。从结构蛋白数据库中加入并分类了其他复合物。这个实质上更加多样化的数据集反映了结构数量的增长，特别是基于我们目前在PDB中大量高分子量蛋白质的统计。新旧数据集的比较表明，新发现的接口簇的数量比可用的新PDB结构的数量增加得快得多。这可能表明唯一接口的数量还没有达到上限。我们将集群分为三种类型：I型集群由相似的接口组成，其父链也相似。在II型集群中，接口是相似的；然而，产生界面的亲本蛋白的整体结构是不同的。在我们所研究的所有II型病例中，聚集蛋白属于不同的SCOP家族，具有不同的功能。第三类是指接口的一端相似而另一端不同的接口集群。III型簇表明，一个结合位点可以与多个具有不同几何形状、大小和组成的链相互作用。蛋白质科学中的一种范式指出，相似的整体结构可能具有相似的功能。我们的观察表明了这种范例的扩展：类似的接口架构可能具有不同的功能。就像蛋白质的结构一样，进化重复利用了“好的”有利的界面结构支架，并使它们适应不同的功能。其功能从酶/抑制剂扩展到毒素和免疫球蛋白。我们没有在II型群集中观察到同型二聚体。这可能是由于单体的尺寸较小以及两态同型二聚体中覆盖大部分链的广泛界面。我们观察到全球不同的蛋白质结构以相似的方式结合产生相似的基序，这很有趣。显然，单体可以有很多种组合方式。值得注意的是，其中有一些首选的接口架构，它们与在单体中观察到的类似。这一观察既强调了有利基序的数量在自然界是有限的观点，也强调了结合和折叠之间的类比。这些现在已经被包括在预测新界面的常规中，它们的结合模式，从而预测蛋白质的功能。我们已经证明，热点主要发生在大分子复合物的界面上，将结合位点与表面的其余部分区分开来。因此，热点可以用来定义结合表位。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，并提出结合界面上的保守残基赋予刚性以最小化结合的熵成本，而周围的残基形成柔性缓冲。此外，我们发现相似的残基热点出现在不同的蛋白质家族中，这表明亲和性和特异性并不一定耦合：更高的亲和性并不直接意味着更高的特异性。色氨酸在蛋白质表面的保存表明一个极可能的结合位点。在较小程度上，Phe和Met的保存也意味着一个结合位点。对于这三个残基，结合位点有明显的保守性，而在暴露表面没有保守性。利用蛋白质-蛋白质界面数据集，我们正在开发一种方案，通过将已知界面映射到蛋白质表面，直接从蛋白质结构预测蛋白质功能。此外，人们还在继续努力预测p53的有害结构及其与DNA结合时的功能动态。我们刚刚解释了为什么p53识别元件在人类基因组上没有间隔（或有一个或两个核苷酸的间隔）的情况下更容易发生，并表明它与p53二聚体和p53- dna的协同性有关。这将有助于设计预测人类基因组上p53识别元件的算法。在实验中，p53识别元件绝大多数没有间隔。

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.