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

基本信息

批准号：
10612069
负责人：
Ivet Bahar
金额：
$ 34.88万
依托单位：
STATE UNIVERSITY NEW YORK STONY BROOK
依托单位国家：
美国
项目类别：
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/10612069
关键词：
Address Algorithms Allosteric Site Amino Acids Assessment tool Attention Benchmarking Biological Biological Process Case Study Cells Collaborations Communities Complement Complex Computer Models Computing Methodologies Crowding Cryoelectron Microscopy Data Data Set Databases Development Dimensions Discrimination Distal Elasticity Elements Event Family member Four-dimensional Frequencies Goals Grain Growth Human Hybrids Intervention Laboratories Learning Letters Libraries Ligand Binding Light Maps Mechanics Methodology Methods Modeling Molecular Molecular Conformation Motion Mutation Orthologous Gene Pathogenicity Pathway interactions Pattern Performance Pharmacologic Substance Point Mutation Productivity Protein Dynamics Protein Family Proteins Resources Sampling Scanning Site Solid Structure System Technology Tertiary Protein Structure Testing Time Translations Validation Variant Work analytical method application programming interface computerized tools computing resources conformer cryptic protein design dynamic system effective intervention experimental study improved innovation intermolecular interaction loss of function machine learning algorithm machine learning method method development molecular dynamics multi-scale modeling network models new technology novel paralogous gene pharmacologic pharmacophore protein protein interaction response simulation three dimensional structure tool

项目摘要

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同种靶向方法了解变构作用机制及其通过配体结合的调节（allo- 靶向）近年来变得越来越重要，因为变构调节剂允许选择性干扰特定的蛋白质-蛋白质相互作用（PPI）或细胞途径。随着数据和方法的增长，我们仍然缺乏对变构机制的扎实理解我们提出了一个全新的框架，重点关注需要的是结构动态的改变，而不仅仅是国家的改变。而不是将我们的注意力限制在两个最终状态之间的转变（例如开放/封闭形式一种蛋白质），需要考虑构象异构体的完整集合，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为了实现这一目标，我们建议开发、实施和应用创新。专注于生物分子基本动力学的计算模型和方法基本动力学是指系统本质上可访问的全局运动模式。整体结构，即它们合作地参与生物的大部分（如果不是全部）结构元素我们建议：(1) 开发、测试和验证基本的现场扫描分析。 (ESSA) 预测主导基本动态的“基本”位点的方法，以及区分其中的变构位点（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（单个氨基酸生物功能的变体，通过将派生的特征包含在我们的机器学习算法中来自生物分子系统的整体运动、蛋白质家族的特征动力学以及通过实验解决 PPI（目标 2），以及 (3) 开发一种混合方法以实现高效适用于含有隐秘位点和冷冻位点的蛋白质的构象景观评估 EM 结构（目标 3），并最终扩展和集成这些新方法，使其能够有效转化为生物医学和药理学应用。验证和进一步扩展将需要针对其他方法和/或进行严格的基准测试相关数据库（如适用）以及与以下机构合作进行的详细案例研究其他实验室（参见来自六个实验和一个计算合作者的支持信）。将方法集成到我们完善的应用程序编程接口中 ProDy 将使更广泛的人能够有效传播和广泛使用新技术社区。

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods.

基于结构的蛋白质动力学建模和机器学习方法的互利融合。

DOI：
发表时间：
2023-02
期刊：
影响因子：
6.8
作者：
Banerjee, Anupam;Saha, Satyaki;Tvedt, Nathan C;Yang, Lee;Bahar, Ivet
通讯作者：
Bahar, Ivet

Structural mechanisms for VMAT2 inhibition by tetrabenazine.

丁苯那嗪抑制 VMAT2 的结构机制。

DOI：
发表时间：
2024-02-01
期刊：
影响因子：
0
作者：
Dalton, Michael P;Cheng, Mary Hongying;Bahar, Ivet;Coleman, Jonathan A
通讯作者：
Coleman, Jonathan A

An interpretable machine learning approach to identify mechanism of action of antibiotics.

一种可解释的机器学习方法来识别抗生素的作用机制。

DOI：
发表时间：
2022-06-20
期刊：
影响因子：
4.6
作者：
Mongia, Mihir;Guler, Mustafa;Mohimani, Hosein
通讯作者：
Mohimani, Hosein

Cooperative mechanics of PR65 scaffold underlies the allosteric regulation of the phosphatase PP2A.

PR65 支架的协同机制是磷酸酶 PP2A 变构调节的基础。

DOI：
发表时间：
2023-05-04
期刊：
影响因子：
0
作者：
Kaynak, Burak T;Dahmani, Zakaria L;Doruker, Pemra;Banerjee, Anupam;Yang, Shang;Gordon, Reuven;Itzhaki, Laura S;Bahar, Ivet
通讯作者：
Bahar, Ivet

Predicting allosteric pockets in protein biological assemblages.

预测蛋白质生物组合中的变构袋。