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A MULTISCALE APPROACH TO TARGET THE ACHILLES HEEL OF P53 CANCER MUTANTS

针对 P53 癌症突变体致命弱点的多尺度方法

基本信息

批准号：
10391499
负责人：
Rommie E Amaro
金额：
$ 31.17万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-04-05 至 2024-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10391499
关键词：
Architecture Binding Binding Proteins Binding Sites Biological Assay Cell Survival Chemicals Closure by clamp Complex Computer Simulation Computing Methodologies DNA DNA Binding DNA Sequence Detection Deuterium Development Feedback Goals Human Hydrogen Individual Length Ligands Malignant Neoplasms Membrane Proteins Methodology Methods Modeling Molecular Conformation Motion Mutate Mutation Outcome Papillomavirus Transforming Protein E6 Pharmaceutical Preparations Point Mutation Protein Dynamics Proteins Response Elements Signal Transduction Signaling Protein Structure System TP53 gene Testing Therapeutic Time Translations Tumor Suppressor Proteins Viral Proteins Work base cost data exchange drug discovery falls lead optimization molecular dynamics mutant novel novel strategies protein structure screening simulation small molecule theories therapeutic target transcription factor tumor progression viral DNA viral rescue virtual screening

项目摘要

SUMMARY p53 is arguably one of the most important tumor suppressor proteins in humans. In almost 50% of all human cancers, p53 is found to be nonfunctional mostly due to single point mutations. The primary goals of this proposal are to gain a detailed understanding of the effect of most-frequent p53 cancer mutations on the protein structure and dynamics, and to leverage a novel computational methodology that identifies small molecules able to reactivate destabilized p53 cancer mutants—a method that represents a promising new approach to drug discovery. We aim to extend our understanding of the structural dynamics of truncated and full-length p53 with state- of-the-art molecular dynamics simulations in order to discovery novel druggable pockets that have not yet been experimentally characterized. Subsequently we plan to use this new structural information to identify small molecules with novel mechanisms of action and reveal new potential therapeutic avenues targeting this vital transcription factor.

总结

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling.

DOI：
10.1021/acs.jctc.2c00282
发表时间：
2023-01
期刊：
Journal of chemical theory and computation
影响因子：
5.5
作者：
Anupam Anand Ojha;Saumya Thakur;Surl-Hee Ahn;Rommie E. Amaro
通讯作者：
Anupam Anand Ojha;Saumya Thakur;Surl-Hee Ahn;Rommie E. Amaro

A potential interaction between the SARS-CoV-2 spike protein and nicotinic acetylcholine receptors.

DOI：
10.1016/j.bpj.2021.01.037
发表时间：
2021-03-16
期刊：
Biophysical journal
影响因子：
3.4
作者：
Oliveira ASF;Ibarra AA;Bermudez I;Casalino L;Gaieb Z;Shoemark DK;Gallagher T;Sessions RB;Amaro RE;Mulholland AJ
通讯作者：
Mulholland AJ

An integrated view of p53 dynamics, function, and reactivation.

p53 动态、功能和重新激活的综合视图。