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Protein Folding01:22

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Protein Organization

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Proteins are polymers of amino acid residues. They are versatile and responsible for different cellular functions, including DNA replication, molecular transport, catalysis, and structural support. Proteins have a hierarchical structure comprising at least three levels of organization: primary, secondary, and tertiary structure. Some large proteins have a quaternary structure where individual protein subunits are linked together.
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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.
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ER is the primary site for the maturation and folding of soluble and transmembrane secretory proteins. The calnexin cycle is a specific chaperone system that folds and assesses the confirmation of N-glycosylated proteins before they can exit the ER lumen. The primary players of this quality check pipeline are the lectins, ER-resident chaperones, and a glucosyl transferase enzyme. In case the calnexin system in the lumen fails to salvage a misfolded protein, it is transported to the cytoplasm...
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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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后处理通过二次深度学习和嵌入来增强蛋白质二次结构预测.

Sotiris Chatzimiltis1,2, Michalis Agathocleous1,3, Vasilis J Promponas4

  • 1University of Cyprus, Department of Computer Science, Nicosia, Cyprus.

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概括

本研究引入了用于蛋白质二次结构预测 (PSSP) 的深度学习模型,使用卷积神经网络 (CNN) 和语言模型嵌入实现高精度. 后处理显著提高了基准数据集的预测性能.

关键词:
000000 这样就好了.第1111章 这是一件好事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.嵌入式 嵌入式黑塞尼亚州的自由优化蛋白质二次结构预测和预测

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科学领域:

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

背景情况:

  • 蛋白质二次结构预测 (PSSP) 对于理解蛋白质功能至关重要.
  • 深度学习方法在PSSP准确性方面提供了有前途的进展.

研究的目的:

  • 开发和评估一个新的深度学习模型,用于准确的PSSP.
  • 评估输入表示和后处理技术对预测性能的影响.

主要方法:

  • 采用了经过子样本赫西安牛顿 (SHN) 方法训练的卷积神经网络 (CNN).
  • 使用从预训练的蛋白质语言模型中嵌入的二维输入表示.
  • 集成和过技术用于后处理.

主要成果:

  • 在没有后处理的情况下,CNN模型实现了79.96% (CB513) 和81.45% (PISCES) 的Q3精度.
  • 后处理提高了Q3精度,达到93.65% (CB513) 和87.13% (PISCES).
  • 在CASP13上,Q3精度达到98.12%,SOV得分达到96.98%,后期处理优化.

结论:

  • 建议的CNN模型与SHN优化和语言模型嵌入显示了PSSP的高效性.
  • 后处理技术显著提高了预测的准确性.
  • 嵌入可以像传统的多个序列对齐一样有效,用于输入表示.