Interplay of inherent promiscuity and specificity in protein biochemical function with applications to drug discovery and exome analysis

 描述(由申请人提供)：就观察到的氨基酸变异的功能后果及其与人类疾病的联系而言，如何解释大量已测序的人类外显子体内的信息存在空白。这一差距也是未能开发出无副作用的药物来治疗这些疾病的原因。一个给定的药物分子与许多细胞过程中涉及的不同蛋白质结合的事实加剧了这一失败。这项提案详细说明了这些问题是如何和为什么会发生的，以及在蛋白质结构的背景下，我们现有的和拟议的进展如何帮助克服这些问题。我们首先阐明了潜在的蛋白质结构和功能的设计原则，然后将它们应用于FDA批准的治疗孟德尔疾病的药物的重新用途，并确定这些疾病潜在的基因变异。我们首先检查小分子药物和内源性代谢物的立体化学空间是否完整，以及药物和代谢物性质的差异。从这些分析中，我们将建议如何从高度混杂的背景中出现约束性特异性。这可能会使设计出副作用最小的更好的药物，并更好地了解细胞的工作原理。利用这些洞察力，我们开发了更好的基于结构的方法来筛选虚拟配体和酶功能推断。预测酶功能的能力尤其重要，因为与酶功能丧失相关的残基突变是与孟德尔病相关的最重要的错义突变。这些方法将在立体化学相似的配基结合部位或不同蛋白质的活性部位使用配体-蛋白质微环境的保守，而不管它们之间的进化关系。我们将探索我们发现的一类酶-双酶，即在两个不同的活性部位执行两种不同酶活性的单域蛋白的生化后果。对于有代表性的案件， 我们将通过实验测试我们对配体结合和酶活性的预测，以及它们对细胞生化功能的影响。所有开发的工具将以一种全面的exome注释方法组合在一起。首先，它将识别与疾病相关的残留变异。然后，它将预测一种蛋白质可能与之相关的疾病，并建议最佳蛋白质靶标。最后，它将建议哪些药物可能是治疗这种疾病的最佳药物。

 DESCRIPTION (provided by applicant): A gap exists as to how to interpret the information in the enormous number of sequenced human exomes in terms of the functional consequences of the observed variations in amino acids and their connection to human diseases. This gap also underlies the failure to develop drugs, without side effects, to treat these diseases. This failureis exacerbated by the fact that a given drug molecule binds to different proteins involved in numerous cellular processes. This proposal lays out the details as to how and why these problems occur, and in the context of protein structure, how our existing and proposed progress can help surmount them. We first elucidate the design principles underlying protein structure and function and then apply them to repurpose FDA approved drugs to treat Mendelian diseases and to identify the genetic variations underlying such diseases. We begin by examining whether the stereo chemical space of small molecule drugs and endogenous metabolites is complete and also the differences in the properties of drugs and metabolites. From these analyses, we will suggest how binding specificity might emerge from a highly promiscuous background. This might enable the design of better drugs with minimal side effects and a better understanding of how cells work. Employing these insights, we then develop better structure-based approaches to virtual ligand screening and enzyme function inference. The ability to predict enzymatic function is particularly essential as residue mutations associated wit loss of enzymatic function are the most important missense mutations associated with Mendelian disease. These approaches will use the conservation of ligand-protein microenvironments in stereochemically similar ligand binding sites or active sites in different proteins, regardless of their evolutionary relationship. We will explore the biochemical consequences of a class of enzymes that we discovered - dizymes, single domain proteins that perform two different enzymatic activities at two different active sites. For representative cases, we will experimentally test our predictions of ligand binding and enzymatic activity and their influence on cellular biochemical function. All developed tools will be combined in a comprehensive exome annotation approach. First, it will identify disease associated residue variations. Then, it will predict diseases a protein might be associated with and suggest the best protein targets. Finally, it will suggest what might be the best drugs to treat the disease.