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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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Conservation of Protein Domains02:26

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Improving Translational Accuracy

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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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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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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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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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OrgNet:使用卷积神经网络进行定向-认知蛋白质稳定性评估.

Ilya Buyanov1, Anastasia Sarycheva2,3,4, Petr Popov2,3,4

  • 1iMolecule, Skolkovo Institute of Science and Technology, Moscow 121205, Russia.

Bioinformatics (Oxford, England)
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新的深度学习模型OrgNet准确地预测了突变导致的蛋白质稳定性变化. 它克服了3D卷积神经网络的定向偏差,用于可靠的蛋白质工程和疾病研究.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物技术是生物技术.
  • 结构生物学是结构生物学.

背景情况:

  • 准确预测单点突变对蛋白质稳定性的影响,对于理解疾病机制和推进蛋白质工程至关重要.
  • 深度学习 (DL) 模型对预测蛋白质热稳定性表现有前途,可能会超过传统方法.
  • 现有的基于结构的DL模型,如卷积神经网络 (CNN),存在定向偏差,影响预测的一致性.

研究的目的:

  • 介绍OrgNet,一种新的导向不可知DL模型,用于预测蛋白质热稳定性在点突变后的变化.
  • 解决和消除基于结构的DL模型中固有的定向偏差,用于蛋白质稳定性预测.

主要方法:

  • 欧尔格网利用3D CNN编码蛋白质结构作为voxel网格,捕获详细的原子特征.
  • 该模型结合了空间转换来标准化蛋白质定向,减轻定向偏差.
  • OrgNet是根据已建立的基准,如Ssym和S669.9,进行评估的.

主要成果:

  • 在预测蛋白质热稳定性变化方面,OrgNet实现了最先进的性能.
  • 与现有的预测方法相比,该模型表现出卓越的准确性和强大的性能.
  • 消除导向偏差导致更一致和可靠的预测.

结论:

  • OrgNet在预测突变对蛋白质稳定性的影响方面取得了重大进展.
  • 导向不可知论的方法克服了以前基于结构的DL模型的一个关键局限性.
  • 在疾病研究和蛋白质工程方面,OrgNet具有应用潜力.