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.

通过计算更深入地理解变构和异源靶向 方法 了解变构作用机制及其通过配体结合（allo- 靶向）在近年来获得了重要性，因为变构调节剂允许选择性地 干扰特定的蛋白质-蛋白质相互作用（PPI）或细胞途径。然而，尽管 尽管数据和方法不断增长，但我们仍然缺乏对变构机制的深入了解 是生物功能的基础我们建议建立一个全新的框架， 需要改变结构动态，而不仅仅是改变状态。此外，委员会认为， 而不是将我们的注意力限制在两个最终状态之间的转换（例如， 一种蛋白质），人们需要考虑构象异构体的完整集合，并评估 分子间相互作用或突变维斯维斯构象变化引起的变化， 景观为了实现这一目标，我们建议开发、实施和应用创新的 计算模型和方法，将集中在生物分子的基本动力学 系统.基本动力学是指自然界固有的运动的全局模式。 整体结构，即它们协同地接合生物学的大部分（如果不是全部）结构元件。 组装件.我们建议：（1）开发，测试和验证一个必要的网站扫描分析 （ESSA）预测主导基本动态的“基本”地点的方法，以及 区分其中的变构位点（目的1），（2）提高我们的能力和准确性， 致病性预测因子RHAPSODY，用于评估突变（单个氨基酸）的影响 变体）对生物功能的影响，通过在我们的机器学习算法中包括 从生物分子系统的全局运动，蛋白质家族的特征动力学， 实验解决PPI（目标2），（3）开发一种混合方法， 适用于含有隐蔽位点和冷冻的蛋白质的构象景观的评估， EM结构（目标3），并最终扩展和集成这些新的方法，使其 高效转化为生物医学和药理学应用。方法开发、测试， 验证和进一步扩展将需要对其他方法进行严格的基准测试和/或 在适用的情况下，除了详细的个案研究， 其他实验室（参见来自六位实验合作者和一位计算合作者的支持信）。 在我们完善的应用程序编程接口中集成方法 ProDy将使更广泛的人能够有效地传播和广泛使用新技术。 社区