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相关概念视频

Nuclear Localization Signals and Import01:46

Nuclear Localization Signals and Import

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Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of  2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...
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Nuclear Protein Sorting01:34

Nuclear Protein Sorting

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Nuclear protein sorting is the selective trafficking of histones, polymerases, gene regulatory proteins into the nucleus and exporting RNAs and ribosomes to the cytosol. It is a tightly controlled process that regulates gene expression within a cell.
Proteins targeted to the nucleus carry nuclear localization signals or NLS recognized by import receptors in the cytosol. Similarly, proteins with nuclear export signals are recognized by export receptors. Import and export receptors are...
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SCLpred-ECL:通过深度N-to-1卷积神经网络进行亚细胞定位预测.

Maryam Gillani1, Gianluca Pollastri1

  • 1School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.

International journal of molecular sciences
|May 25, 2024
PubMed
概括

这项研究介绍了一种新的计算工具,用于使用深度学习来预测蛋白质细胞下定位. 这种方法提供了一种更快,更有效的替代实验技术,以了解蛋白质功能和帮助药物发现.

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
  • 基因组学就是基因组学.

背景情况:

  • 蛋白质亚细胞定位对于药物设计,发现和基因组学至关重要.
  • 确定蛋白质位置的实验方法昂贵且耗时.
  • 精确的计算方法为预测蛋白质定位提供了更有效的替代方案.

研究的目的:

  • 介绍一个对蛋白质细胞下定位的初始计算预测器.
  • 为了利用深度 N-to-1 卷积神经网络进行预测.
  • 提供一个工具,弥合了蛋白质序列和功能之间的差距.

主要方法:

  • 开发了一个使用深度N-to-1卷积神经网络组合的初始预测器.
  • 在严格冗余减少的数据集上训练并测试了预测器.
  • 评估了各种各样的蛋白质类别的预测器的准确性.

主要成果:

  • 在预测蛋白质亚细胞定位方面获得了63%的准确性.
  • 证明了深度学习方法对此任务的有效性.
  • 在严格策划的数据集上验证了性能.

结论:

关键词:
N-to-1 卷积神经网络 卷积神经网络生物信息学是一种生物信息学.深度学习是一种深度学习.蛋白质亚细胞局部化预测预测

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  • 开发的预测器是朝着高效的蛋白质功能分析迈出的重要一步.
  • 这种计算工具可以帮助理解蛋白质与蛋白质之间的相互作用.
  • 潜在的应用包括促进药物设计和疫苗生产.