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相关概念视频

Flow Cytometry01:23

Flow Cytometry

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The development of flow cytometry techniques began in 1934 with initial attempts by Andrew Moldavan, a bacteriologist who counted the cells in a flowing capillary system. Moldavan pumped cells through a capillary tube focused under a microscope for visualization. The invention of photometry allowed the measurement of differentially-stained cells, and Louis Kamentsky developed the first multiparameter flow cytometer in 1965 to identify and count the cancer cells in cervical tissue specimens.
In...
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Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction
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回流:一种通用流程匹配方法,用于生物分子回映图.

Michael S Jones1, Smayan Khanna1, Andrew L Ferguson1,2

  • 1Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United States.

Journal of chemical information and modeling
|January 8, 2025
PubMed
概括

作为一种深度生成模型,FlowBack从粗的分子模拟中准确地恢复了所有原子的细节. 这种新的方法增强了对生物分子动力学和结构机制的理解.

科学领域:

  • 生物分子建模模型
  • 计算生物物理学的计算生物物理学
  • 结构生物学 结构生物学

背景情况:

  • 粗粒度 (CG) 模型加速了缓慢动态的分子模拟,例如蛋白质折叠.
  • 从CG轨迹中恢复全原子 (AA) 细节对于机械学见解至关重要,但仍然具有挑战性.
  • 现有的CG到AA重建方法往往缺乏准确性和效率.

研究的目的:

  • 介绍FlowBack,这是一个深度生成模型,用于准确高效的CG到AA结构重建.
  • 为FlowBack开发一个灵活的预先分布,独立于CG映射和分子类型.
  • 评估FlowBack对蛋白质和DNA-蛋白质复杂系统的性能.

主要方法:

  • 在深度生成模型 (FlowBack) 中使用了流程匹配目标.
  • 为生成模型构建了一个分子类型不可知先前分布.
  • 在蛋白质数据库 (PDB) 数据上训练了蛋白质特异性和DNA蛋白质特异性模型.

主要成果:

  • 与现有方法相比,在蛋白质的结构指标上取得了最先进的性能.
  • 证明了DNA-蛋白质复合体的优秀重建和生成能力.
  • 展示了FlowBack在静态结构,AA模拟和CG动态轨迹上的有效性.

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Flow-pattern Guided Fabrication of High-density Barcode Antibody Microarray
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A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
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结论:

  • FlowBack提供了一个准确,高效和用户友好的工具,可以从CG模拟中恢复所有原子的细节.
  • 该模型表现出优越的强度和比以前的方法更少的硬体碰撞.
  • FlowBack作为一个开源的Python包发布,供社区使用.