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相关概念视频

Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

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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.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to...
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The function of proteins depends on their native three-dimensional structure, which is dictated by the amino acid sequence of the specific protein. Folding of the polypeptide chain takes place under specific conditions that energetically favor the folded conformation. In contrast, protein denaturation occurs spontaneously under unfavorable conditions that disrupt the integrity of the folded conformation. Thus, the chemical and physical environment of a protein, such as significant changes in pH...
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RNA Stability01:53

RNA Stability

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Intact DNA strands can be found in fossils, while scientists sometimes struggle to keep RNA intact under laboratory conditions. The structural variations between RNA and DNA underlie the differences in their stability and longevity. Because DNA is double-stranded, it is inherently more stable. The single-stranded structure of RNA is less stable but also more flexible and can form weak internal bonds. Additionally, most RNAs in the cell are relatively short, while DNA can be up to 250 million...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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THPLM:一种基于序列的深度学习框架,用于使用预训练的蛋白质语言模型预测蛋白质稳定性变化的点变化.

Jianting Gong1,2, Lili Jiang1,2, Yongbing Chen1,2

  • 1School of Information Science and Technology, Institution of Computational Biology, Northeast Normal University, Changchun 130117, China.

Bioinformatics (Oxford, England)
|October 24, 2023
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概括
此摘要是机器生成的。

我们开发了THPLM,一种使用蛋白语言模型 (PLM) 的深度学习模型,用于从序列中预测蛋白质稳定性的变化. THPLM表现出竞争力的性能,增强了蛋白质的设计和功能预测.

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科学领域:

  • 生物化学和分子生物学
  • 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:

  • 准确预测蛋白质热力学稳定性对于蛋白质和药物设计至关重要.
  • 现有的基于结构和基于序列的方法在表示全球序列对稳定性变化的影响方面存在局限性.
  • 蛋白质语言模型 (PLM) 的进步提供了从序列中捕获结构信息的新方法.

研究的目的:

  • 开发一种新的基于序列的深度学习模型,用于预测蛋白质稳定性的变化.
  • 利用蛋白质语言模型 (PLM) 来改善蛋白质序列的表示.
  • 与现有方法对比,评估拟议模型的性能.

主要方法:

  • 利用Meta的ESM-2,一个强大的蛋白质语言模型,用于序列表示.
  • 开发了THPLM,这是一个基于序列的深度学习模型,包含一个卷积神经网络.
  • 评估了THPLM在预测蛋白质稳定性变化的表现.

主要成果:

  • THPLM实现了与现有的基于序列和基于结构的方法相匹配或优越的性能.
  • 该模型有效地利用PLM生成的序列表示来进行稳定性预测.
  • PLM的表示能力增强了蛋白质功能预测能力.

结论:

  • THPLM提供了一种强大而有效的基于序列的方法来预测蛋白质稳定性的变化.
  • 该研究强调了PLM在推进计算蛋白质设计和分析方面的潜力.
  • 开发的模型和代码是公开可用的,用于进一步的研究和应用.