Toward a deeper understanding of allostery and allotargeting by computational approaches

通过计算加深对变构和同种异构靶向的理解 方法 了解变构的作用机制及其通过配体结合的调节(异构化) 靶向)近年来变得越来越重要，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 尽管数据和方法的增长，我们仍然缺乏对变构机制的坚实理解 这是生物功能的基础。我们建议一个全新的框架，重点放在 需要的是结构动态的变化，而不仅仅是状态的变化。此外， 而不是将我们的注意力局限于两个结束状态之间的转换(例如， 一种蛋白质)，需要考虑构象的完整集合，并评估其效果 分子间相互作用或突变相对于构象引起的变化 风景。为了实现这一目标，我们建议开发、实施和应用创新 侧重于生物分子基本动力学的计算模型和方法 系统。基本动力学指的是从本质上可获得的全局运动模式 整体结构，即它们协同工作，即使不是全部，也是生物体的大部分结构元素 集合。我们建议：(1)开发、测试和验证基本的站点扫描分析 (ESSA)预测主导基本动态的“基本”位置的方法，以及 识别它们之间的变构位置(目标1)，(2)提高我们的能力和准确性 致病性预测因子，Rhapsody，用于评估突变的影响(单一氨基酸 变体)，通过在我们的机器学习算法中包括派生的特征 从生物分子系统的全球运动，蛋白质家族的标志性动力学，以及 实验解决的PPI(目标2)，以及(3)开发一种混合方法，以提高效率 适用于含有隐蔽位点和冷冻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方法，以使其 高效地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与其他方法和/或 适用的相关数据库，以及与以下机构合作的详细案例研究 其他实验室(见六名实验合作者和一名计算合作者的支持信)。 将这些方法集成到我们完善的应用程序编程接口中 Prody将使新技术得到更广泛的有效传播和广泛使用 社区。

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.