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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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Improving Translational Accuracy02:07

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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Signal Sequences and Sorting Receptors01:41

Signal Sequences and Sorting Receptors

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Signal sequences are short amino acid sequences that guide newly synthesized proteins to their proper location within the cell. Classical signal sequences are fifteen to sixty amino acids long and present at the N-terminus of a polypeptide chain. Each signal sequence has a conserved segment of basic residues towards their N terminus, a hydrophobic core, and a C-terminus rich in polar residues. The C-terminus also contains a signal cleavage site and features a -3 -1 sequence motif. The -3-1...
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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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From DNA to Protein03:06

From DNA to Protein

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The flow of genetic information in cells from DNA to mRNA to protein is described by the central dogma, which states that genes specify the sequence of mRNAs, which in turn specify the sequence of amino acids making up all proteins. The decoding of one molecule to another is performed by specific proteins and RNAs. Because the information stored in DNA is so central to cellular function, it makes intuitive sense that the cell would make mRNA copies of this information for protein synthesis...
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Conservation of Protein Domains02:26

Conservation of Protein Domains

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相关实验视频

Updated: Jul 17, 2025

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
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通过转移卷积序列表示方式预测和解释蛋白质发育能力.

Alexander W Golinski1, Zachary D Schmitz1, Gregory H Nielsen1

  • 1Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States.

ACS synthetic biology
|August 29, 2023
PubMed
概括
此摘要是机器生成的。

神经网络使用高通量数据从氨基酸序列预测蛋白质开发能力. 这种方法改善了重组表达预测,并可视化了蛋白质健身景观.

关键词:
开发能力 开发能力景观景观 景观 景观 景观模型模型模型模型模型模型预测性 预测性 预测性蛋白质蛋白质是蛋白质蛋白质的组成部分.这是一个序列的序列.

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Application of I TASSER, trRosetta, UCSF Chimera, HADDOCK server, and HEX loria for De Novo and In Silico Design of Proteins
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A Protocol for Computer-Based Protein Structure and Function Prediction
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相关实验视频

Last Updated: Jul 17, 2025

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A Protocol for Computer-Based Protein Structure and Function Prediction
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A Protocol for Computer-Based Protein Structure and Function Prediction

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科学领域:

  • 蛋白质工程是指蛋白质工程.
  • 计算生物学 计算生物学
  • 生物物理学的生物物理.

背景情况:

  • 工程蛋白质是有价值的工具,但往往患有不良的开发能力 (表达,溶解性,稳定性).
  • 从氨基酸序列预测蛋白质的可开发性可以简化候选物选择并降低实验成本.
  • 高通量选已经产生了大量的蛋白质可开发性数据集,使机器学习方法成为可能.

研究的目的:

  • 评估神经网络从高通量数据中学习蛋白质可开发性表示的能力.
  • 为了预测未见的蛋白质序列的重组表达.
  • 想象和理解蛋白质适应性景观和影响开发能力的关键氨基酸特性.

主要方法:

  • 开发一个神经网络模型,使用高通量可开发性数据集用于Gp2蛋白支架.
  • 卷积方法学习氨基酸特性和预测表达水平.
  • 非线性维度缩小和嵌套采样用于健身景观分析.

主要成果:

  • 神经网络模型预测表达水平比对照更接近实验变异的44%.
  • 学习的氨基酸嵌入揭示了氨酸,水性和电荷的意义,而芳香性对小蛋白质发育能力不那么重要.
  • 蛋白质健身景观的直接可视化确定了进化瓶和不同的亚群.

结论:

  • 神经网络可以有效地预测蛋白质表达,并从有限的数据中解释可开发性.
  • 了解氨基酸贡献和健身景观有助于设计改进的蛋白质支架.
  • 这项工作将应用蛋白质工程与基础生物物理结合起来,以表征蛋白质的开发能力.