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:10445567
- 负责人:
- 金额:$ 30.73万
- 依托单位:
- 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:
- 关键词:AffectAlgorithmsAwardBacteriaBacteriophagesBindingBinding ProteinsBinding SitesBiological ProcessBiologyCarbohydratesCell CommunicationCell LineCell WallCell surfaceCellsChemicalsCollaborationsCommunitiesComplexComputational algorithmComputer softwareComputing MethodologiesData SetDetectionDevelopmentEquilibriumEventEvolutionExhibitsExtracellular DomainFree EnergyFreedomG-Protein-Coupled ReceptorsGleanHealthHormonesHumanImmune responseKineticsKnowledgeLearningLengthLigandsLipopolysaccharidesMarkov ChainsMeasurementMethodologyMethodsModelingMolecularMolecular BiologyMolecular ComputationsMolecular ConformationMotionNatureNeuropeptidesNeurotransmitter ReceptorOrganismParentsPathway interactionsPeptide ReceptorPeptide Signal SequencesPeptidesPharmaceutical PreparationsPlant RootsProbabilityProcessProtein ConformationProteinsResearch PersonnelRoleRunningSamplingSerotypingShigella flexneriSignal TransductionSpecificitySystemTherapeuticTimeUncertaintyUnited States National Institutes of HealthVariantVirionVirusWorkantagonistbacteriophage tailspike proteinbiological systemscalcitonin receptor-like receptorcancer cellchemical reactioncofactordesigndynamic systemexperienceextracellularflexibilityhuman pathogenimprovedinnovationinsightinterestmembermigraine treatmentmolecular dynamicsmolecular recognitionmutantnanoscalenanosecondnovel therapeuticspeptide hormonereceptorreceptor-activity-modifying proteinsimulationsuccesstool
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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Alexander Dickson其他文献
Alexander Dickson的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Alexander Dickson', 18)}}的其他基金
Revealing the Ligand Binding Landscape with Advanced Molecular Simulation Methods
利用先进的分子模拟方法揭示配体结合景观
- 批准号:
10166872 - 财政年份:2018
- 资助金额:
$ 30.73万 - 项目类别:
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:
10618938 - 财政年份:2018
- 资助金额:
$ 30.73万 - 项目类别:
相似海外基金
IGF::OT::IGF: SBIR Phase I Award for A System for the Specification of Acute THC Impairment Using Validated Algorithms Period of Performance 09/30/2018 to 03/30/2019
IGF::OT::IGF:使用经过验证的算法的急性 THC 损伤规范系统获得 SBIR 第一阶段奖 执行期间 09/30/2018 至 03/30/2019
- 批准号:
9806025 - 财政年份:2018
- 资助金额:
$ 30.73万 - 项目类别:
ICS IG 2014 Computational Biology Undergraduate Summer Student Health Research award - Design and implementation of algorithms for finding short motifs in protein-protein interactions associated with prostate cancer.
ICS IG 2014 计算生物学本科生暑期学生健康研究奖 - 设计和实现算法,用于寻找与前列腺癌相关的蛋白质-蛋白质相互作用中的短基序。
- 批准号:
308975 - 财政年份:2014
- 资助金额:
$ 30.73万 - 项目类别:
Studentship Programs
Research Initiation Award: Efficient Algorithms for Automatic Parallel Program Decomposition
研究启动奖:自动并行程序分解的高效算法
- 批准号:
9409736 - 财政年份:1994
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Research Initiation Award: Parallel Algorithms for Scalable Multicomputers
研究启动奖:可扩展多计算机并行算法
- 批准号:
9308966 - 财政年份:1993
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Research Initiation Award: Algorithms for On-Line and Distributed Systems
研究启动奖:在线和分布式系统算法
- 批准号:
9309456 - 财政年份:1993
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Presidential Young Investigator Award: Efficient Algorithms in Combinatorial Optimization
总统青年研究员奖:组合优化中的高效算法
- 批准号:
9157199 - 财政年份:1991
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Presidential Young Investigator Award: Parallel Algorithms for Integer and Mixed Integer Nonlinear Programs Arising in the Management and Design of Chemical Processes
总统青年研究员奖:化学过程管理和设计中出现的整数和混合整数非线性程序的并行算法
- 批准号:
9058073 - 财政年份:1990
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Presidential Young Investigator Award-Genetic Algorithms andMachine Learning in Dynamic Systems Control
总统青年研究员奖-动态系统控制中的遗传算法和机器学习
- 批准号:
9096245 - 财政年份:1990
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Presidential Young Investigator Award: Rapid Numerical Algorithms for Scientific Computation
总统青年研究员奖:科学计算快速数值算法
- 批准号:
9058579 - 财政年份:1990
- 资助金额:
$ 30.73万 - 项目类别:
Continuing Grant
Research Initiation Award: Techniques for Design and Analysis of Short Memory Stochastic Adaptive Control Algorithms
研究启动奖:短记忆随机自适应控制算法设计与分析技术
- 批准号:
8910088 - 财政年份:1989
- 资助金额:
$ 30.73万 - 项目类别:
Standard Grant














{{item.name}}会员




