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相关概念视频

Protein and Protein Structure02:15

Protein and Protein Structure

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Proteins are one of the most abundant organic molecules in living systems and have the most diverse range of functions of all macromolecules. Proteins may be structural, regulatory, contractile, or protective. They may serve in transport, storage, or membranes; or they may be toxins or enzymes. Their structures, like their functions, vary greatly. They are all, however, amino acid polymers arranged in a linear sequence.
A protein's shape is critical to its function. For example, an enzyme...
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Structural Protein Function01:56

Structural Protein Function

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Structural proteins are a category of proteins responsible for functions ranging from cell shape and movement to providing support to major structures such as bones, cartilage, hair, and muscles. This group includes proteins such as collagen, actin, myosin, and keratin.
Collagen, the most abundant protein in mammals, is found throughout the body. In connective tissue, such as skin, ligaments, and tendons, it provides tensile strength and elasticity.  In bones and teeth, it mineralizes to...
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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Graphical Representation of Inequalities01:28

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The graph of the equation where y equals x squared forms a curve known as a parabola. This curve acts as a boundary in the coordinate plane, dividing it into distinct regions based on the relative position of points.When the equality sign in the equation is replaced with an inequality—such as greater than, less than, greater than or equal to, or less than or equal to—the graphical representation changes from a single curve into a broader shaded area that signifies the set of all...
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CPE-Pro:一种结构敏感的深度学习方法,用于蛋白质表示和原产地评估.

Wenrui Gou1, Wenhui Ge1, Yang Tan1

  • 1School of Information Science and Engineering, East China University of Science and Technology, Shanghai, 200237, China.

Interdisciplinary sciences, computational life sciences
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PubMed
概括
此摘要是机器生成的。

我们开发了一个深度学习模型,CPE-Pro,以准确确定蛋白质结构的起源. 这种方法通过关注结构特征来增强蛋白质的表征,改善实验和预测数据的可靠性评估.

关键词:
深度学习是一种深度学习.原产地评价 原产地评价蛋白质表示表示蛋白质表示结构序列的结构序列.

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科学领域:

  • 结构生物学 结构生物学
  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.

背景情况:

  • 蛋白质结构的确定对于理解生物功能至关重要.
  • 不断扩大的蛋白质结构数据库需要可靠的来源评估方法.
  • 目前的蛋白质表示方法与微妙的结构变化作斗争,影响可追溯性.

研究的目的:

  • 开发一种用于蛋白质结构起源评估的新型深度学习模型.
  • 通过捕捉结构特定特征来增强蛋白质的表现.
  • 改善对实验和预测蛋白质结构可靠性的评估.

主要方法:

  • 建议的水晶与蛋白质结构预测评估器 (CPE-Pro),一个监督的深度学习模型.
  • 集成了一个预训练的蛋白质结构序列语言模型 (SSLM) 与一个几何向量感知子-图形神经网络 (GVP-GNN).
  • 使用结构意识表示,专注于局部结构特征进行分类.

主要成果:

  • CPE-Pro准确地将蛋白质结构分为四个来源.
  • 来自结构序列的结构感知表示表现优于传统的基于序列的模型.
  • 该模型有效地捕捉了对原产地确定至关重要的微妙结构差异.

结论:

  • CPE-Pro为蛋白质结构表示和原产地评估提供了一种精细的方法.
  • 通过局部结构特征来丰富表示,可以提高模型性能.
  • 未来的工作包括将模型扩展到各种蛋白质结构范式和低信心预测.