Targeting the ET domain of BET proteins: specificity and selectivity

项目摘要 BET蛋白直接参与诸如病毒感染和不同类型的 癌症。尽管不同的BET蛋白在细胞中满足不同的作用，并且优先 在不同的组织中表达，目前的BET抑制策略是非特异性的-导致 毒性。BET蛋白的ET结构域最近已成为与 结合蛋白对特定BET蛋白有希望的选择性-使ET结构域成为 有趣的目标是设计新的药物疗法。然而，人们对外星人知之甚少。 相互作用体或其结合机制。这项建议旨在增加我们对 Et Interactome(目标1)，使用计算工具：a)确定可能的活页夹；b)选择 使用机器学习和基于物理的方法的最强粘合剂；以及c)表征 最有希望的线索是核磁共振实验。挑战在于解决 ET的多态性质：它可以通过构象变化与不同的多肽结合 序列--这些序列依次以不同的结合模式结合。这种结合可塑性 随之而来的是广泛的结合亲和力。此外，一种特定的多肽 序列以不同的结合亲和力结合不同BET蛋白的ET结构域，设置 为设计针对每种BET蛋白的抑制剂奠定了基础。然而，目前还不清楚 特异性的来源在哪里，因为ET结构域具有很高的序列和结构相似性 所有投注会员。因此，Aim 2将揭示多肽与ET和 利用自适应采样分子动力学阐明特异性的来源 策略和马尔可夫状态模型。结合亲和力与结合方式的关系 目前还不得而知。可能是病毒正在利用一种绑定模式，从而导致更高 结合亲和力高于调节宿主蛋白所使用的亲和力。提案的目标3解决了这一问题 通过设计新的多肽结合剂来确定结合亲和力的极限 不同的绑定模式。这些计算设计将导致热点的识别 结合域中的区域可用于治疗设计的长期目标 战略。

Project Summary BET proteins are directly involved in pathologies such as viral infection and different types of cancer. Although the different BET proteins satisfy different roles in the cell and are preferentially expressed in different tissues, current BET inhibition strategies are non-specific – resulting in toxicity. The ET domain of BET proteins has recently emerged as a protein interaction hub with promising selectivity of binders towards specific BET proteins – making the ET domain an interesting target towards the design of novel drug therapeutics. Yet, little is known about the ET interactome or its binding mechanism. This proposal aims to increase our understanding of the ET interactome (Aim 1) by using computational tools to: a) identify possible binders; b) select the strongest binders using machine learning and physics-based approaches; and c) characterize the most promising leads through NMR experiments. The challenge lies in addressing the polymorphic nature of ET: it can undergo conformational changes to bind different peptide sequences – which in turn bind along different binding modes. Such binding plasticity concomitantly leads to a wide range of binding affinities. Furthermore, a particular peptide sequence binds the ET domain of different BET proteins with different binding affinities, setting the foundation for the design of inhibitors specific to each BET protein. However, it is not clear where the origin of specificity lies, as the ET domains have high sequence and structural similarity across BET members. Thus, Aim 2 will unveil the binding mechanism for peptides to ET and elucidate the origin of specificity through the use of adaptive sampling molecular dynamics strategies and Markov State Models. The relationship between binding affinity and binding mode is currently unknown. It could be that virus are exploiting a binding mode that leads to higher binding affinity than those used by regulatory host proteins. Aim 3 of the proposal addresses this issue by designing novel peptide binders to identify the limits of binding affinity amongst the different binding modes. These computational designs will lead to the identification of hot-spot regions in the binding domain to exploit towards the long-term goal of therapeutic design strategies.