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相关概念视频

Per-Unit Sequence Models01:26

Per-Unit Sequence Models

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An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
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Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich...
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相关实验视频

Updated: Sep 14, 2025

Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
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海月:从蛋白质语言模型到持续的结构异质性

Valentin Lombard1, Dan Timsit1, Sergei Grudinin2

  • 1Sorbonne Université, CNRS, IBPS, Department of Computational, Quantitative and Synthetic Biology (CQSB, UMR7238), 75005 Paris, France.

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概括

通过深度学习,SeaMoon直接从氨基酸序列预测蛋白质的运动. 这种方法捕获了替代蛋白质构造,进步了我们对细胞功能的理解.

关键词:
在PCA中,PCA是PCA.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.主要组成部分预测预测.蛋白质语言模型的模型蛋白质运动预测预测小空间预测预测子空间预测转移学习转移学习

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科学领域:

  • 计算生物学是一种计算生物学.
  • 结构生物信息学 结构生物信息学
  • 蛋白质科学中的深度学习

背景情况:

  • 蛋白质动态对于细胞功能至关重要,驱动相互作用和细胞过程.
  • 预测蛋白质3D结构已经取得了进展,重点转移到采样替代构造.
  • 深度学习模型越来越多地用于探索蛋白质结构空间.

研究的目的:

  • 为了研究从序列中直接预测连续蛋白质运动表示.
  • 开发一种深度学习模型,绕过对3D结构信息的需求.
  • 评估模型捕捉各种蛋白质动态的能力.

主要方法:

  • 利用蛋白质语言模型 (pLM) 的嵌入作为输入.
  • 使用轻量级卷积神经网络架构.
  • 训练和评估模型与实验性符合性数据集对比.

主要成果:

  • 海月号成功预测了40%的测试蛋白质的地面真实运动,准确度合理.
  • 该模型捕获了蛋白质运动,这种运动无法通过传统的基于物理的方法 (如正常模式分析) 来检测.
  • 海洋月亮证明了对蛋白质的概括能力,与训练数据没有显著的序列相似性.

结论:

  • 从序列直接预测蛋白质运动是可行的使用深度学习.
  • 海月提供了一种新的方法来样本蛋白质构造,补充现有方法.
  • 该模型的可回收性允许适应新的蛋白质语言模型和数据.