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相关概念视频

Conserved Binding Sites01:49

Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...
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Single-Strand DNA Binding Proteins01:03

Single-Strand DNA Binding Proteins

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For successful DNA replication, the unwinding of double-stranded DNA must be accompanied by stabilization and protection of the separated single strands of the DNA. This crucial task is performed by single-strand DNA-binding (SSB) proteins. They bind to the DNA in a sequence-independent manner, which means that the nitrogenous bases of the DNA need not be present in a specific order for binding of SSB proteins to it. The binding of SSB proteins straightens single-stranded DNA (ssDNA) and makes...
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Protein-protein Interfaces02:04

Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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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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深度WET:一种基于深度学习的方法,用于使用加权特征的词嵌入技术预测DNA结合蛋白.

S M Hasan Mahmud1,2, Kah Ong Michael Goh3, Md Faruk Hosen4

  • 1Department of Computer Science, American International University-Bangladesh (AIUB), Kuratoli, Dhaka, 1229, Bangladesh. hasan.swe@aiub.edu.

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概括

深度WET是一种新的深度学习方法,从序列数据中准确识别DNA结合蛋白 (DBPs). 这种计算方法为DBP预测提供了比实验方法更快,更可靠的替代方案.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 生物信息学是一种生物信息学.
  • 蛋白质组学是指蛋白质组学.

背景情况:

  • 结合DNA的蛋白质 (DBPs) 对于DNA修复和修改等遗传过程至关重要.
  • 在抗生素和抗癌剂的药物发现中,DBP是重要的目标.
  • 实验性DBP识别是昂贵的,并且可能有偏见,需要计算解决方案.

研究的目的:

  • 从初级序列信息中开发一种新,准确和快速的计算方法来识别DNA结合蛋白 (DBPs).
  • 为了利用深度学习和高级功能工程来增强DBP预测.

主要方法:

  • 提出了Deep-WET,这是一个使用全球向量,Word2Vec和fastText进行蛋白质序列编码的深度学习模型.
  • 使用差异演化 (DE) 进行特征权重和SHapley添加式扩展 (SHAP) 进行特征选择.
  • 为最终预测器集成了一个最佳特征子集到卷积神经网络 (CNN) 中.

主要成果:

  • 在交叉验证和独立测试中,Deep-WET在传统机器学习分类器上表现出优异的预测性能.
  • 在广泛的独立测试中实现了高精度 (78.08%),MCC (0.559) 和AUC (0.805).
  • 超过了几种最先进的DNA结合蛋白质预测方法.

结论:

  • 深度WET表现出重要的预测能力,用于识别DNA结合蛋白.
  • 开发的方法为蛋白质组学研究中大规模DBP识别提供了有价值的工具.
  • 一个Web服务器和数据集是公开可用的,以支持科学界.