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Revealing pathways and kinetics of molecular recognition with advanced molecular simulation algorithms

通过先进的分子模拟算法揭示分子识别的途径和动力学

基本信息

批准号：
10618938
负责人：
Alexander Dickson
金额：
$ 32.75万
依托单位：
MICHIGAN STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-20 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10618938
关键词：
Affect Algorithms Award Bacteria Bacteriophages Binding Binding Proteins Binding Sites Biological Process Biology Carbohydrates Cell Communication Cell Line Cell Wall Cell surface Cells Chemicals Collaborations Communities Complex Computational algorithm Computer software Computing Methodologies Data Set Detection Development Equilibrium Event Evolution Exhibits Extracellular Domain Free Energy Freedom G-Protein-Coupled Receptors Glean Health Hormones Human Immune response Kinetics Knowledge Learning Length Ligands Lipopolysaccharides Markov Chains Measurement Methodology Methods Modeling Molecular Molecular Biology Molecular Computations Molecular Conformation Motion Nature Neuropeptides Neurotransmitter Receptor Organism Parents Pathway interactions Peptide Receptor Peptide Signal Sequences Peptides Pharmaceutical Preparations Probability Process Protein Conformation Proteins Research Personnel Role Running Sampling Serotyping Shigella flexneri Signal Transduction Specificity System Therapeutic Time Uncertainty United States National Institutes of Health Variant Virion Virus Work antagonist bacteriophage tailspike protein biological systems calcitonin receptor-like receptor cancer cell chemical reaction cofactor design dynamic system experience extracellular flexibility human pathogen improved innovation insight interest member migraine treatment molecular dynamics molecular recognition mutant nanoscale nanosecond novel therapeutics peptide hormone receptor receptor-activity-modifying protein simulation success tool transmission process

项目摘要

Project Summary Biology at the nanoscale is driven by molecular recognition. Cells are lined with receptors that recognize a myriad of biomolecules and transmit signals to the cell interior. This forms the basis of cell-cell communication that allows multicellular organisms to thrive. An understanding of molecular recognition is tremendously important for human health, as it underlies our immune system’s response to threats from viruses, bacteria and cancer cells. Unfortunately, these recognition processes are often much more complicated than the canonical “lock-and-key” paradigm. Many neuropeptides and peptide hormones are dozens of residues in length and can exhibit tremendous structural plasticity. The study of molecular recognition in such dynamic systems is a challenge, as only a limited understanding of the interactions can be gleaned from structural approaches alone. Computational molecular dynamics (MD) simulation allows us to view complex biomolecular systems in atomic detail, allowing us to compute binding and unbinding rates, and to study their molecular determinants. A well-known drawback of MD is the difficulty of exploring conformational landscapes without getting trapped in local free energy minima. The PI (Dickson) has over a decade of experience developing new methods for tackling this problem, which are able to calculate transition rates in complex systems. Particularly, the REVO method (“Reweighting Ensembles by Variation Optimization”) has shown success on unbinding pathways of drug-like ligands with mean first passage times up to 34 minutes, which is billions of times beyond the typical reach of straightforward MD. This is done without applying biasing forces to the system or making any assumptions about equilibrium conditions. REVO is essentially an evolutionary algorithm in trajectory space, where a large group of trajectories are run in parallel, and outlier trajectories are identified using a measurement of distance to other ensemble members. These outliers are “cloned”, which increases the probability of seeing long-timescale events happen, even in short-time trajectories. The proposed work will augment this method with recent advances in the automatic detection of slow collective variables (or “CVs”). The VAMPnet (“Variational Approach for Markov Processes”) approach will be used to detect groups of CVs to use in the REVO distance calculation. A method for iteratively improving the accuracy of rate calculations (“Asynchronous weighted ensemble”) is also proposed. Together these developments will constitute a powerful tool for studying long-timescale events in complex biomolecular systems that will be made freely available to other researchers. Through collaborations with experimental partners, this method will be applied to reveal the molecular mechanisms of: i) peptide signaling for a class-B GPCR (Aim 2), and ii) specific recognition of bacterial LPS by a phage tail-spike protein (Aim 3). These mechanisms will inform the design of new molecules with potential therapeutic applications.

项目摘要纳米尺度的生物学是由分子识别驱动的。细胞内排列着识别A蛋白的受体无数的生物分子，并将信号传输到细胞内部。这构成了细胞间通信的基础这使得多细胞有机体能够茁壮成长。对分子识别的理解是非常重要的对人类健康很重要，因为它是我们免疫系统应对病毒、细菌和癌细胞。不幸的是，这些识别过程往往比规范的识别过程复杂得多 “锁和钥匙”模式。许多神经肽和多肽激素的长度是几十个残基，可以表现出巨大的结构可塑性。这类动态系统中分子识别的研究是一个这是一项挑战，因为仅从结构性方法只能对相互作用有一个有限的了解。计算分子动力学(MD)模拟使我们能够观察复杂的生物分子系统原子细节，使我们能够计算结合和解结率，并研究它们的分子决定因素。一个众所周知，MD的缺点是很难在不陷入困境的情况下探索构象景观局部自由能极小值。PI(Dickson)有十多年开发新方法的经验解决这个问题，它能够计算复杂系统中的转换率。尤其是Revo 方法(通过变异优化重新加权集合)已经在解开约束路径上显示出成功类药物配体平均首次通过时间高达34分钟，是典型药物的数十亿倍直截了当的MD范围。这是在不对系统施加偏向力的情况下完成的，或者使任何关于均衡条件的假设。REVO本质上是一种轨迹空间的进化算法，其中一大组轨迹是并行运行的，并且使用距离其他乐团成员的距离的测量。这些离群值是“克隆的”，这增加了看到长时间尺度事件发生的概率，即使在短时间轨迹中也是如此。拟议的工作将利用自动检测慢波的最新进展来增强这种方法集合变量(或“CV”)。VAMPnet(马尔可夫过程的变分方法)的方法将是用于检测要在Revo距离计算中使用的CV组。一种迭代改进的方法还提出了速率计算的精确度(“异步加权集合”)。把这些放在一起这些进展将成为研究复杂生物分子中长时间尺度事件的有力工具将向其他研究人员免费提供的系统。通过与实验中心合作这一方法将被应用于揭示：I)B类分子信号转导的分子机制 GPCR(目标2)，以及ii)噬菌体尾钉蛋白对细菌内毒素的特异性识别(目标3)。这些机制将为设计具有潜在治疗应用的新分子提供信息。