Toward a deeper understanding of allostery and allotargeting by computational approaches

通过计算方法更深入地理解变构和异体靶向

• 批准号: 10887238
• 负责人: Ivet Bahar
• 金额: $ 30.88万
• 依托单位: STATE UNIVERSITY NEW YORK STONY BROOK
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-05 至 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10887238
• 关键词: Toward; deeper; understanding; allostery; allotargeting

Toward a deeper understanding of allostery and allotargeting by computational approaches Understanding allosteric mechanisms of action and their modulation by ligand binding (allo- targeting) gained importance in recent years, as allosteric modulators allow for selective interference with specific protein-protein interactions (PPI) or cellular pathways. Yet, despite the growth of data and methodologies, we still lack a solid understanding of allosteric mechanisms that underlie biological function. We propose that a completely new framework, with focus on the change in structural dynamics rather than changes in the states only, is needed. Furthermore, rather than limiting our attention to transitions between two end-states (e.g. open/closed forms of a protein), one needs to consider the complete ensemble of conformers, and evaluate the effect of intermolecular interactions or mutations vis-à-vis the changes elicited in the conformational landscape. Toward this goal, we propose to develop, implement, and apply innovative computational models and methods that will focus on the essential dynamics of biomolecular systems. Essential dynamics refers to the global modes of motions intrinsically accessible to the overall structure, i.e. they cooperatively engage most, if not all, structural elements of the biological assembly. We propose to: (1) develop, test, and validate an essential site scanning analysis (ESSA) methodology for predicting ‘essential’ sites that dominate the essential dynamics, and discriminating allosteric sites among them (Aim 1), (2) enhance the capability and accuracy of our pathogenicity predictor, RHAPSODY, for evaluating the impact of mutations (single amino acid variants) on biological function, by including in our machine learning algorithm the features derived from global motions of biomolecular systems, the signature dynamics of protein families, and the experimentally resolved PPIs (Aim 2), and (3) develop a hybrid methodology for efficient assessment of conformational landscapes applicable to proteins containing cryptic sites and cryo- EM structures (Aim 3), and finally extend and integrate these new methodologies to enable their efficient translation to biomedical and pharmacological applications. Method development, testing, validation, and further extensions will entail rigorous benchmarking against other methods and/or relevant databases where applicable, in addition to detailed case studies in collaboration with other labs (see support letters from six experimental and one computational collaborator). Integration of the methodologies within our well-established application programming interface ProDy will enable efficient dissemination and wide usage of the new technologies by the broader community.

要深入了解计算的变构和分配 方法 了解作用的变构机制及其通过配体结合的调节（同种 定位）近年来变得重要，因为变构调节剂允许选择性 干扰特定蛋白质蛋白质相互作用（PPI）或细胞途径。但是，dospite 数据和方法的增长，我们仍然缺乏对变构机制的牢固理解 这是生物学功能的基础。我们建议一个全新的框架，重点是 需要更改结构动力学而不是状态的变化。此外， 而不是将我们的注意力限制在两个最终国家之间的过渡（例如 一种蛋白质），需要考虑完整的构象异构体，并评估效果 分子间相互作用或突变相对于构象引起的变化 景观。为了实现这一目标，我们建议开发，实施和应用创新 计算模型和方法将集中于生物分子的基本动力学 系统。基本动力学是指在本质上可以访问的全局动作模式 整体结构，即它们合作地参与生物学的大多数（如果不是全部）结构元素 集会。我们建议：（1）开发，测试和验证必不可少的站点扫描分析 （ESSA）预测主导基本动态的“基本”站点的方法论 区分它们之间的变构位点（AIM 1），（2）提高我们的能力和准确性 致病性预测指标，狂想曲，用于评估突变的影响（单氨基酸） 变体）关于生物学功能，通过在我们的机器学习算法中包括得出的功能 来自生物分子系统的全球运动，蛋白质家族的特征动力学和 实验解决的PPI（AIM 2），（3）开发一种混合方法，以提高 评估适用于含有加密位点和冷冻蛋白的构象景观 EM结构（AIM 3），最后扩展并整合这些新方法以使其能够 有效地翻译为生物医学和药物应用。方法开发，测试， 验证，进一步的扩展将需要根据其他方法和/或进行严格的基准测试 适用的相关数据库，除了与详细的案例研究合作 其他实验室（请参阅来自六个实验和一个计算合作者的支持信）。 将方法集成在我们公认的应用程序编程界面中 Prody将使更广泛的新技术有效地传播和广泛使用新技术 社区。