Biomolecular Recognition and Binding Mechanisms

我们已经解决了单个枢纽蛋白如何与如此多不同的伙伴结合的问题。许多研究都在寻找集线器和非集线器之间的差异，以解释是什么使蛋白质成为集线器，以及一个共享的集线器结合位点是如何混杂的，同时又是特异性的。我们认为这个问题在很大程度上是不存在的，它存在于蛋白质相互作用网络的流行表示中：来自单个基因的蛋白质产物，即使不同，也会在图谱中聚集到单个节点中。这给人的印象是，单个蛋白质与非常多的伴侣结合。在现实中，它不是；相反，蛋白质网络反映了多种蛋白质的组合，每种蛋白质都有不同的构象。p53-应答元件（p53-REs）是由间隔0到20个碱基对（bp）的回文DNA片段的两个重复组成的。几个实验表明，在绝大多数人类p53-REs中，两个重复序列之间没有间隔；带有间隔的细胞，尤其是长度超过两个核苷酸的细胞，非常罕见。这就提出了一个问题，即它表明了决定p53-RE基因组组织的因素。显然，考虑到DNA的双螺旋构象，两个p53核心结构域二聚体相对于彼此的取向将根据间隔物的大小而变化：0到2 bps的小间隔物将导致最接近的p53二聚体-二聚体取向；10bp间隔将p53二聚体定位在相同的DNA表面，但需要DNA环；而一个5bp间隔将p53二聚体定位在相反的DNA表面。构象分析表明，当存在0-2 bp间隔时，p53-DNA结合是协同的；然而，当间隔器的尺寸超过2bp时，协作性大大降低。协同结合被广泛认为是生物过程的关键，包括转录调控。我们的研究结果清楚地表明，p53-DNA结合的协同性主导了p53-REs的基因组组织，这就提出了p53-REs的结构组织和更大间隔的功能作用的问题。我们进一步提出，p53和p53- res的动态景观情景可以更好地解释简并的p53- res的选择性。我们的结论与p53-RE组织的进化偏好有关，因此，预计对其他多聚体转录因子响应元件的组织具有广泛的启示。蛋白质-蛋白质相互作用图的检查表明，枢纽蛋白可以与非常多的蛋白质相互作用，达到数十甚至数百。由于单个蛋白质不能同时与如此多的伙伴相互作用，这就提出了一个挑战：我们能否弄清楚哪些相互作用可以同时发生，哪些相互作用是相互排斥的？解决这个问题为交互地图增加了第四个维度：时间。在结构网络中包含时间维度是一项巨大的资产；时间维度将网络节点和边缘映射转换为细胞过程，有助于理解细胞通路及其调控。虽然可以通过将蛋白质复合物与mRNA表达数据的时间序列联系起来进一步增强时间维度，但目前缺乏可靠的网络实验数据。在这里，我们概述了如何使用结构数据，有效的结构比较算法和适当的数据集和过滤器来帮助了解交互网络中的时间维度；预测哪些相互作用可以共存，哪些不能共存；在得到与实验相一致的具体预测时。作为一个例子，我们展示了p53链接的过程。药物开发的关键步骤是鉴定要靶向的蛋白质及其拓扑细胞网络位置和相互作用。这些与致病事件中的信息流和药物效果有关。信息流涉及一系列结合或共价修饰过程；每一步都受到前一步的影响。蛋白质是灵活的，信息通过其构象群分布的动态变化而流动；分子识别很大程度上是由这些变化决定的。药物发现通常集中在细胞网络的十字路口的信号蛋白上。信号蛋白通过共享的结合位点有多个伴侣结合。我们突出显示了这些共享的结合位点。回顾的数据表明，这些位点上的伴侣结合似乎通过不同的能量优势热点残基相互作用，尽管构象群的分布发生了动态变化，但热点构象仍保持在其预组织状态。一种新的氨基酸被设计为精氨酸（Arg， R）的替代品，以保护肿瘤归巢的五肽CREKA （Cys-Arg-Glu-Lys-Ala）免受蛋白酶的侵害。这种氨基酸，标记为（Pro）hArg，其特征是脯氨酸骨架带有一个特定取向的胍侧链。这种残基结合了Pro诱导类回合构象和Arg侧链功能的能力。采用模拟退火和分子动力学相结合的方法研究了含该精氨酸代用品的CREKA类似物的构象。结果表明，（Pro）hArg显著降低了肽的构象柔韧性。尽管在脊柱中观察到一些变化……主链和侧链…侧链相互作用时，修饰肽表现出强烈的适应以（Pro）hArg残基为中心的旋转构象的倾向，并且分子的整体形状具有最低能量构象的特征，为天然肽和修饰肽表现出高度的相似性。特别是，这种转变使主链的Arg、Glu和Lys侧链面向分子的同一侧，这被认为对生物活性很重要。我们的研究结果表明，用（Pro）hArg替代CREKA中的Arg可能有助于提供对蛋白水解酶的抗性，同时保留对肿瘤归巢活性至关重要的构象特征。多肽和蛋白质在生物医学和材料工程领域的应用越来越广泛。利用具有多种物理化学和结构特征的非蛋白质原性氨基酸，为设计具有新特性和功能的蛋白质和肽提供了可能。此外，非蛋白原性残基对于控制肽链的三维排列特别有用，这对于大多数应用来说是一个关键问题。然而，关于这类氨基酸的信息——也被称为非编码、非规范或非标准——通常分散在相当不同领域的专门出版物以及专利中。要使所有这些数据对科学界有用，需要新的工具和框架来汇编和连贯地组织这些数据。我们已经成功地编译、组织和建立了一个数据库（NCAD, Non-Coded Amino acids database），其中包含了通过量子力学计算确定的非蛋白残基的内在构象偏好信息，以及它们的合成、物理和光谱表征、实验建立的构象倾向和应用的书目信息。数据库的结构是在这项工作中提出的，包括第一家族的非编码残基，即α -四取代α -氨基酸。此外，通过一个测试用例应用程序示例演示了NCAD的有用性。

We have addressed the question of how can a single hub protein bind so many different partners. Numerous studies have sought differences between hubs and non-hubs to explain what makes a protein a hub and how a shared hub-binding site can be promiscuous, yet at the same time be specific. We suggest that the problem is largely non-existent and resides in the popular representation of protein interaction networks: protein products derived from a single gene, even if different, are clustered in maps into a single node. This leads to the impression that a single protein binds to a very large number of partners. In reality, it does not; rather, protein networks reflect the combination of multiple proteins, each with a distinct conformation. p53-response elements (p53-REs) are organized as two repeats of a palindromic DNA segment spaced by 0 to 20 base pairs (bp). Several experiments indicate that in the vast majority of the human p53-REs there are no spacers between the two repeats; those with spacers, particularly with sizes beyond two nucleotides, are rare. This raises the question of what it indicates about the factors determining the p53-RE genomic organization. Clearly, given the double helical DNA conformation, the orientation of two p53 core domain dimers with respect to each other will vary depending on the spacer size: a small spacer of 0 to 2 bps will lead to the closest p53 dimer-dimer orientation; a 10-bp spacer will locate the p53 dimers on the same DNA face but necessitate DNA looping; while a 5-bp spacer will position the p53 dimers on opposite DNA faces. Via conformational analysis we showed that when there are 0-2 bp spacers, p53-DNA binding is cooperative; however, cooperativity is greatly diminished when there are spacers with sizes beyond 2 bp. Cooperative binding is broadly recognized to be crucial for biological processes, including transcriptional regulation. Our results clearly indicated that cooperativity of the p53-DNA association dominates the genomic organization of the p53-REs, raising questions of the structural organization and functional roles of p53-REs with larger spacers. We further propose that a dynamic landscape scenario of p53 and p53-REs can better explain the selectivity of the degenerate p53-REs. Our conclusions bear on the evolutionary preference of the p53-RE organization and as such, are expected to have broad implications to other multimeric transcription factor response element organization. Inspection of protein-protein interaction maps illustrates that a hub protein can interact with a very large number of proteins, reaching tens and even hundreds. Since a single protein cannot interact with such a large number of partners at the same time, this presents a challenge: can we figure out which interactions can occur simultaneously and which are mutually excluded? Addressing this question adds a fourth dimension into interaction maps: that of time. Including the time dimension in structural networks is an immense asset; time dimensionality transforms network node-and-edge maps into cellular processes, assisting in the comprehension of cellular pathways and their regulation. While the time dimensionality can be further enhanced by linking protein complexes to time series of mRNA expression data, current robust, network experimental data are lacking. Here we outline how, using structural data, efficient structural comparison algorithms and appropriate datasets and filters can assist in getting an insight into time dimensionality in interaction networks; in predicting which interactions can and cannot co-exist; and in obtaining concrete predictions consistent with experiment. As an example, we presented p53-linked processes. A key step in drug development is identification of the protein to be targeted and its topological cellular network location and interactions. These relate to the information flow in disease-causing events and to medication effects. Information flow involves a cascade of binding or covalent modification processes; with each step affected by previous ones. Proteins are flexible, and information flows via dynamic changes of the distributions of their conformational ensembles; and molecular recognition is largely determined by these changes. Drug discovery often focuses on signaling proteins, at the cross-roads of cellular networks. Signaling proteins have multiple partners binding through shared binding sites. We have highlighted these shared binding sites. The reviewed data suggest that partners binding at these sites appear to interact via different energetically-dominant hot spot residues and that despite the dynamic changes in the distribution of the conformational ensembles, the hot spot conformations are retained in their pre-organized states. A new amino acid has been designed as a replacement for arginine (Arg, R) to protect the tumor-homing pentapeptide CREKA (Cys-Arg-Glu-Lys-Ala) from proteases. This amino acid, denoted (Pro)hArg, is characterized by a proline skeleton bearing a specifically oriented guanidinium side chain. This residue combines the ability of Pro to induce turn-like conformations with the Arg side-chain functionality. The conformational profile of the CREKA analogue incorporating this Arg substitute has been investigated by a combination of simulated annealing and molecular dynamics. Comparison of the results with those previously obtained for the natural CREKA shows that (Pro)hArg significantly reduces the conformational flexibility of the peptide. Although some changes are observed in the backbone...backbone and side-chain...side-chain interactions, the modified peptide exhibits a strong tendency to accommodate turn conformations centered at the (Pro)hArg residue and the overall shape of the molecule in the lowest energy conformations characterized for the natural and the modified peptides exhibit a high degree of similarity. In particular, the turn orients the backbone such that the Arg, Glu, and Lys side chains face the same side of the molecule, which is considered important for bioactivity. Our results suggested that replacement of Arg by (Pro)hArg in CREKA may be useful in providing resistance against proteolytic enzymes while retaining conformational features which are essential for tumor-homing activity. Peptides and proteins find an ever-increasing number of applications in the biomedical and materials engineering fields. The use of non-proteinogenic amino acids endowed with diverse physicochemical and structural features opens the possibility to design proteins and peptides with novel properties and functions. Moreover, non-proteinogenic residues are particularly useful to control the three-dimensional arrangement of peptidic chains, which is a crucial issue for most applications. However, information regarding such amino acids--also called non-coded, non-canonical, or non-standard--is usually scattered among publications specialized in quite diverse fields as well as in patents. Making all these data useful to the scientific community requires new tools and a framework for their assembly and coherent organization. We have successfully compiled, organized, and built a database (NCAD, Non-Coded Amino acids Database) containing information about the intrinsic conformational preferences of non-proteinogenic residues determined by quantum mechanical calculations, as well as bibliographic information about their synthesis, physical and spectroscopic characterization, conformational propensities established experimentally, and applications. The architecture of the database is presented in this work together with the first family of non-coded residues included, namely, alpha-tetrasubstituted alpha-amino acids. Furthermore, the NCAD usefulness is demonstrated through a test-case application example.