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相关概念视频

Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Single-pass Transmembrane Proteins01:25

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Integral membrane proteins are tightly associated with the cell membrane and play a crucial role in cell communication, signaling, adhesion, and transport of the molecules. Some integral membrane proteins are present only in the membrane monolayer. For example, the enzyme fatty acid amide hydrolase is present in the cytoplasmic side of the membrane monolayer. In contrast, another type of integral membrane protein, also known as a transmembrane protein, spans across the membrane. Transmembrane...
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In multi-pass transmembrane proteins, the polypeptide chain crosses the membrane more than once. The transmembrane polypeptide chain either forms an α-helix or β-strand structure. α-Helix containing multi-pass transmembrane proteins are ubiquitous, whereas β-strand containing ones are mainly found in gram-negative bacteria, mitochondria, and chloroplasts.
α-Helix containing multi-pass transmembrane proteins
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Mitochondrial precursors are translocated to the internal subcompartments via independent mechanisms involving distinct protein machineries called translocases.
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The rough ER membrane synthesizes, assembles, and embeds transmembrane proteins in diverse topologies. These proteins function as transporters or channels and can remain in the ER membrane or are sent to the Golgi complex, lysosome, and cell membrane.
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相关实验视频

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A Protocol for Computer-Based Protein Structure and Function Prediction
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AT-TSVM:使用主动转移传导支向量机改进跨膜蛋白跨螺旋残留接触预测.

Bander Almalki1, Aman Sawhney1, Li Liao1

  • 1Department of Computer and Information Sciences, University of Delaware, Smith Hall, 18 Amstel Avenue, Newark, DE 19716, USA.

International journal of molecular sciences
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概括

这项研究引入了一种用于预测α螺旋式跨膜蛋白质结构的新方法. 该方法通过在训练期间使用序列和原子特征来提高接触预测的准确性,即使只有序列数据可用于测试.

关键词:
生物信息学是一种生物信息学.联系人地图 联系人地图接触残留物 接触残留物传导式学习是指传导式学习.转移学习转移学习跨膜蛋白质是一种跨膜蛋白质.

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相关实验视频

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科学领域:

  • 生物化学 生物化学
  • 结构生物学 结构生物学
  • 计算生物学 计算生物学

背景情况:

  • 阿尔法螺旋式跨膜蛋白是重要的膜蛋白,占所有跨膜蛋白的近三分之一,在细胞功能中发挥关键作用.
  • 准确的结构预测这些蛋白质对于理解它们的功能至关重要,但实验结构的确定是有限的.
  • 目前用于预测蛋白质结构的计算方法通常仅依赖于序列特征,导致接触预测和随后的3D结构生成的准确性较低.

研究的目的:

  • 开发一种新的计算方法,以提高跨膜蛋白中螺旋间残留物接触预测的准确性.
  • 为了应对转移学习的挑战,训练数据包括序列和原子特征,但测试数据只有序列特征.
  • 通过利用独特的转移学习范式来改进跨膜蛋白的3D结构预测.

主要方法:

  • 提出了一种新的方法,AT-TSVM (Active Transfer for Transductive Support Vector Machines),将转移学习,主动学习和转导学习整合在一起.
  • 该方法从训练数据中使用序列和原子特征训练模型.
  • 应用训练模型来测试仅包含序列特征的数据,优于传统方法.

主要成果:

  • 与仅使用序列特征的方法相比,AT-TSVM方法显著提高了接触预测的准确性.
  • 在基准数据集上,平均比感应分类器提高5-6%,比传导分类器提高2.5-4%.
  • 证明了拟议的转移学习方法在提高预测准确性的有效性.

结论:

  • 通过有效利用有限的特征集,AT-TSVM方法在预测跨膜蛋白质结构方面取得了重大进展.
  • 这种方法为结构预测提供了一个实用的解决方案,当测试数据中无法获得原子特征时.
  • 该研究强调了将主动转移和传导学习结合起来,提高生物结构预测准确性的潜力.