COMPUTATIONAL STUDIES OF THE NEUROPROTEIN TDP-43

神经蛋白 TDP-43 的计算研究

基本信息

批准号：
8171924
负责人：
John Beale
金额：
$ 0.11万
依托单位：
CARNEGIE-MELLON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-08-01 至 2013-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8171924
关键词：
Active Sites Age-Years Algorithms Alzheimer&apos s Disease Amber Amino Acids Amyotrophic Lateral Sclerosis Blast Cell Brain C-terminal Computer Retrieval of Information on Scientific Projects Database Computers Cytoplasm DNA Sequence Rearrangement Data Databases Dementia Disease Docking Doctor of Philosophy Exons Frontotemporal Lobar Degenerations Fugue Functional disorder Funding Glycine Grant Hour Institution Lead Maps Modeling Molecular Conformation Mutation Nerve Neurons Pathology Pathway interactions Persons Pharmaceutical Chemistry Pharmaceutical Preparations Pharmacy facility Proteins RNA Recognition Motif RNA Splicing Research Research Personnel Resources Running Saints Sampling Screening procedure Side Simulate Site Source Structure System Temperature Testing Time United States National Institutes of Health Variant Work abstracting college computer studies drug candidate interest molecular dynamics mutant nanosecond professor programs protein TDP-43 protein folding research study simulation small molecule three dimensional structure virtual

项目摘要

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. TDP-43 is a protein that is normally involved in splicing if RNA and exon rearrangement in neural cells. In disease, it is responsible for the pathophysiology of amyotrophic lateral sclerosis (Lou Gehrigs disease), frontotemporal lobar degeneration (the most common form of dementia in persons under 60 years of age), and Alzheimer disease. In the latter disease, TDP-43 folds abnormally and aggregates in the cytoplasm of neural cells in the brain, causing misfunctioning of the nerve pathways. The protein consists of two primary regions: a dimeric RNA recognition motif (RRM) and a glycine-rich C-terminal region. The glycine-rich region is the site of a number of mutants, each causing a different variation of disease. Little is known about the three-dimensional structure of the glycine-rich region, but we do have structural data on the RRMs. We are interested in characterizing the glycine-rich region to better understand the structural differences that cause the mutations to encode different disease pathologies. On the RRM, we will perform virtual screening to identify small molecules that may serve as drugs to counteract the function of TDP-43 in disease. Work described in this abstract will involve three experimental fronts. 1) Virtual screening of the RRMs of TDP-43 with about a million small molecules to identify potential drug candidates; 2) molecular dynamics unfolding experiments on the glycine-rich region and the RRMs to identify the folding pathways that lead to misfolding in Alzheimer disease; and 3) molecular dynamics simulations on the wild type glycine rich moiety as well as on mutant forms of this protein segment. For the virtual screening experiments we will use the program Surflex-Dock. On a Pentium-4 system, it requires about 7 hours to dock 25,000 small molecules into the mapped active site of the RRM. We plan to use 10,000 CPU hours to dock small molecules into the RRM structure. The highest scoring candidates will be collected and chemically synthesized for testing in the native TDP-43. Protein folding experiments with molecules of any size require extremely large amounts of computer time. In practice, it is typical to assume that unfolding of a protein follows the same pathways as does folding, only in reverse. Since one begins with a known structure, the experiments require much less time. To conduct unfolding experiments on the glycine-rich region, we will begin with a structure that was deduced using the Tripos package Fugue. This package essentially uses a BLAST algorithm and an extensive database to compute likely structures of the molecule. The unfolding experiments will involve molecular dynamics simulations on the model using the AMBER-10 package which will be run for 100-250 nanoseconds to fully sample the molecule in space. A perturbation (increasing temperature) will be used to start the unfolding cycle. Targeted molecular dynamics will be used to drive the structure to the form found for Alzheimer disease. We plan to use 50,000 CPU hours to conduct unfolding experiments. Lastly, we will employ molecular dynamics simulations using AMBER-10 to simulate the 140 amino acid wild type form of the glycine-rich moiety, deduce its conformational structure, and compare it to dynamics simulations of mutant glycine-rich proteins. These simulations will show the conformational structures of the amino acid side chains and will be used to compare wild type with mutants. Nothing is known about these conformations, so these experiments will be extremely important. We will use 10,000 CPU hours to simulate these systems. John M. Beale, Ph.D. Associate Professor of Medicinal Chemistry Saint Louis College of Pharmacy

该副本是利用众多研究子项目之一由NIH/NCRR资助的中心赠款提供的资源。子弹和调查员（PI）可能已经从其他NIH来源获得了主要资金，因此可以在其他清晰的条目中代表。列出的机构是对于中心，这不一定是调查员的机构。 TDP-43是一种蛋白质，通常参与神经细胞中的RNA和外显子重排的剪接。在疾病中，它是造成肌萎缩性外侧硬化症（Lou Gehrigs疾病），额颞叶变性（60岁以下患者最常见的痴呆形式）和阿尔茨海默氏病的原因。在后一种疾病中，TDP-43异常折叠并在大脑中神经细胞的细胞质中骨聚集，从而导致神经途径失功。该蛋白质由两个主要区域组成：二聚体RNA识别基序（RRM）和富含甘氨酸的C末端区域。富含甘氨酸的区域是许多突变体的部位，每种突变体的位置导致疾病的不同变化。关于富含甘氨酸的区域的三维结构知之甚少，但是我们确实具有RRMS的结构数据。我们有兴趣表征富含甘氨酸的区域，以更好地理解导致突变编码不同疾病病理的结构差异。在RRM上，我们将进行虚拟筛查，以鉴定可能用作药物以抵消TDP-43在疾病中的功能的小分子。此摘要中描述的工作将涉及三个实验方面。 1）用大约一百万个小分子对TDP-43的RRM进行虚拟筛选，以鉴定潜在的候选药物； 2）在富含甘氨酸的区域和RRMS上展开实验的分子动力学，以识别导致阿尔茨海默氏病错误折叠的折叠途径； 3）富含甘氨酸的部分以及该蛋白段的突变形式上的分子动力学模拟。对于虚拟筛选实验，我们将使用程序表面滴。在Pentium-4系统上，需要大约7个小时才能将25,000个小分子停放到RRM的映射活性位点。我们计划使用10,000个CPU小时将小分子停靠到RRM结构中。将收集最高评分的候选者，并在天然TDP-43中进行化学合成以进行测试。具有任何大小的分子的蛋白质折叠实验需要大量的计算机时间。实际上，通常假设蛋白质的展开遵循与折叠相同的途径，仅在相反的情况下。由于一个从已知的结构开始，因此实验需要更少的时间。为了在富含甘氨酸的区域进行展开的实验，我们将从使用Tripos包装赋格推导的结构开始。该软件包实质上使用BLAST算法和广泛的数据库来计算分子的可能结构。展开的实验将使用Amber-10封装涉及模型上的分子动力学模拟，Amber-10包将用于100-250纳秒，以完全采样太空中的分子。扰动（温度升高）将用于开始展开周期。靶向分子动力学将用于将结构推向阿尔茨海默氏病的形式。我们计划使用50,000个CPU小时进行展开的实验。最后，我们将使用Amber-10使用分子动力学模拟来模拟富含甘氨酸的部分的140个氨基酸野生型形式，推断其构象结构，并将其与富含突变甘氨酸蛋白的动力学模拟进行比较。这些模拟将显示氨基酸侧链的构象结构，并将用于将野生型与突变体进行比较。这些构象对这些构型一无所知，因此这些实验将非常重要。我们将使用10,000个CPU小时来模拟这些系统。约翰·M·比尔（John M. Beale）博士药化学副教授圣路易斯药学院