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

• 批准号: 10445567
• 负责人: Alexander Dickson
• 金额: $ 30.73万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 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/10445567
• 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; Plant Roots; 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

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.

项目概要 纳米尺度的生物学是由分子识别驱动的。细胞内布满了识别某种物质的受体 无数的生物分子并将信号传输到细胞内部。这构成了细胞间通讯的基础 使多细胞生物能够蓬勃发展。对分子识别的理解是巨大的 对人类健康很重要，因为它是我们的免疫系统对病毒、细菌和病毒威胁做出反应的基础 癌细胞。不幸的是，这些识别过程通常比规范的识别过程复杂得多。 “锁和钥匙”范式。许多神经肽和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。在这种动态系统中分子识别的研究是一个 挑战，因为仅从结构方法中只能收集到对相互作用的有限理解。 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合率，并研究它们的分子决定因素。一个 MD 众所周知的缺点是很难在不陷入困境的情况下探索构象景观 局部自由能最小值。 PI (Dickson) 拥有十多年开发新方法的经验 解决这个问题，能够计算复杂系统中的转换率。特别是REVO 方法（“通过变异优化重新加权集成”）已在解除绑定路径方面取得了成功 类药物配体的平均首次通过时间长达 34 分钟，比典型配体的数十亿倍 直接MD的范围。这是在不向系统施加偏压力或进行任何操作的情况下完成的 关于平衡条件的假设。 REVO本质上是轨迹空间中的进化算法， 其中一大组轨迹并行运行，并且使用异常值轨迹来识别 测量与其他乐团成员的距离。这些异常值被“克隆”，从而增加了 看到长期事件发生的概率，即使是在短期轨迹中。 所提出的工作将通过自动检测慢速的最新进展来增强该方法 集体变量（或“CV”）。 VAMPnet（“马尔可夫过程的变分方法”）方法将是 用于检测用于 REVO 距离计算的 CV 组。一种迭代改进方法 还提出了速率计算的准确性（“异步加权集成”）。一起这些 发展将成为研究复杂生物分子中的长期事件的强大工具 系统将免费提供给其他研究人员。通过与实验机构的合作 合作伙伴，该方法将用于揭示以下分子机制：i) B 类肽信号传导 GPCR（目标 2），以及 ii) 噬菌体尾部刺突蛋白对细菌 LPS 的特异性识别（目标 3）。这些 机制将为具有潜在治疗应用的新分子的设计提供信息。