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.

项目摘要 纳米级的生物学是由分子识别驱动的。细胞衬有识别的接收器 无数的生物分子并将信号传输到细胞内部。这构成了细胞电池通信的基础 这允许多细胞生物蓬勃发展。对分子识别的理解非常高 对人类健康很重要，因为它是我们免疫系统对病毒，细菌和 癌细胞。不幸的是，这些识别过程通常比规范要复杂得多 “锁定”范式。许多神经肽和胡椒恐怖量是数十个残留物，可以 表现出巨大的结构可塑性。这种动态系统中分子识别的研究是 挑战，因为仅凭结构方法才能收集对相互作用的有限理解。 计算分子动力学（MD）模拟使我们能够查看复杂的生物分子系统 原子细节，使我们能够计算结合和解开速率，并研究其分子决定剂。一个 众所周知的MD缺点是难以探索构象景观而不会被困 局部自由能级。 PI（Dickson）拥有十多年的经验 解决此问题，该问题能够计算复杂系统中的过渡速率。特别是Revo 方法（“通过变化优化重新授权合奏”）显示了在解开路径上的成功 类似于药物的配体，平均第一次通过时间长达34分钟，超出了典型的数十亿次 直接医学博士的到达。这是在不将偏置力施加到系统或制造任何的情况下完成的 关于平衡条件的假设。 Revo本质上是轨迹空间中的进化算法， 并行运行大量轨迹的地方，并且使用A 测量与其他合奏成员的距离。这些离群值是“克隆的”，这增加了 即使在短期轨迹中，也会发生长时间观察长时间事件的概率。 拟议的工作将通过自动检测缓慢的最新进展来增强这种方法 集体变量（或“ CVS”）。 Vampnet（“马尔可夫流程的变异方法”）方法将是 用于检测CVS组以用于Revo距离计算。一种迭代改善的方法 还提出了速率计算的准确性（“异步加权集合”）。在一起 发展将构成一个强大的工具，用于研究复杂的生物分子中的长时间尺度事件 将免费提供给其他研究人员的系统。通过与实验的合作 合作伙伴，该方法将应用于揭示以下分子机制：i）B类肽信号传导 GPCR（AIM 2）和II）通过噬菌体尾尖蛋白对细菌LPS的特定识别（AIM 3）。这些 机制将为使用潜在的治疗应用的新分子设计。