Targeting the ET domain of BET proteins: specificity and selectivity

靶向 BET 蛋白的 ET 结构域：特异性和选择性

• 批准号: 10637266
• 负责人: Alberto Perez
• 金额: $ 30.5万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10637266
• 关键词: Address; Affinity; Bayesian Analysis; Binding; Binding Proteins; Bioinformatics; Biology; Breast; Bromodomain; Cells; Chemicals; Chromatin; Clinical Trials; Communities; Competitive Binding; Complex; Computing Methodologies; Data; Development; Disease; Docking; Drug Targeting; Drug resistance; Epigenetic Process; Epitopes; Foundations; Genes; Goals; Grant; Hallucinations; Heart; Hematologic Neoplasms; Inflammatory; Light; Lung; Machine Learning; Modeling; Molecular Conformation; Nature; Oncogenes; Outcome; Pathologic; Pathology; Peptide Library; Peptides; Pharmaceutical Preparations; Physics; Prostate; Protein Family; Proteins; Public Health; Rapid screening; Reporting; Research; Role; Sampling; Solid Neoplasm; Specificity; Structure; Structure-Activity Relationship; System; Tertiary Protein Structure; Testing; Therapeutic; Therapeutic Agents; Tissues; Toxic effect; Virus; Virus Diseases; Work; bioinformatics tool; cancer type; computational pipelines; computerized tools; design; driving force; experimental study; flexibility; immunoregulation; improved; inhibitor; interest; member; molecular dynamics; novel; novel strategies; novel therapeutic intervention; novel therapeutics; peptide structure; prevent; protein aminoacid sequence; protein protein interaction; simulation; structural biology; success

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.

项目概要 BET 蛋白直接参与病毒感染和不同类型的疾病等病理过程。 尽管不同的BET蛋白在细胞中发挥不同的作用并且优先发挥作用。 在不同组织中表达，目前的 BET 抑制策略是非特异性的 - 导致 BET 蛋白的 ET 结构域最近已成为具有毒性的蛋白质相互作用中心。 结合物对有前途的特定 BET 蛋白的选择性 – 使 ET 结构域成为 新型药物疗法设计的有趣目标然而，人们对 ET 知之甚少。 该提案旨在增加我们对相互作用组或其结合机制的理解。 ET 相互作用组（目标 1），通过使用计算工具来： a) 识别可能的结合物 b) 选择 使用机器学习和基于物理的方法来表征最强的粘合剂； 通过核磁共振实验最有希望的线索面临的挑战在于解决这个问题。 ET的多态性：它可以发生构象变化以结合不同的肽 序列——依次沿着不同的结合模式结合。 此外，特定的肽同时产生广泛的结合亲和力。 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 然而，目前尚不清楚。 特异性的根源在于，ET 结构域具有高度的序列和结构相似性 因此，Aim 2 将揭示肽与 ET 和 ET 的结合机制。 通过使用自适应采样分子动力学阐明特异性的起源 策略和马尔可夫状态模型之间的结合亲和力和结合模式之间的关系。 目前尚不清楚，病毒可能正在利用一种结合模式，从而导致更高的水平。 该提案的目标 3 解决了这个问题。 通过设计新型肽结合剂来确定肽结合剂之间的结合亲和力的极限 这些计算设计将导致热点的识别。 结合域中的区域可用于实现治疗设计的长期目标 策略。