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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：
10462594
负责人：
Ivet Bahar
金额：
$ 2.73万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
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 2023-01-03
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10462594
关键词：
Address Algorithms Allosteric Site Amino Acids 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 Elements Event Family member Four-dimensional Frequencies Goals Grain Growth Human Hybrids Intervention Letters Libraries Ligand Binding Light Maps Mechanics Methodology Methods Modeling Molecular Molecular Conformation Motion Mutation Orthologous Gene Pathogenicity Pathway interactions Performance Pharmacologic Substance Pharmacology Point Mutation Protein Dynamics Protein Family Proteins Resources Sampling Scanning Site Solid Structure System Technology Testing Time Translations Validation Variant Work analytical method application programming interface base computerized tools computing resources conformer cryptic protein design 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 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.

更深入地了解变构和分配目标的计算