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相关概念视频

Nucleic Acid Structure01:25

Nucleic Acid Structure

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The pentose sugar in DNA is deoxyribose, while in RNA the pentose sugar is ribose. The difference between the sugars is the presence of the hydroxyl group on the ribose's second carbon and a hydrogen on the deoxyribose's second carbon. The phosphate residue attaches to the hydroxyl group of the 5′ carbon of one sugar and the hydroxyl group of the 3′ carbon of the sugar of the next nucleotide, which forms  a 5′ to 3′ phosphodiester linkage.
DNA Structure
DNA...
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RNA Structure01:23

RNA Structure

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Overview
The basic structure of RNA consists of a five-carbon sugar and one of four nitrogenous bases. Although most RNA is single-stranded, it can form complex secondary and tertiary structures. Such structures play essential roles in the regulation of transcription and translation.
Different Types of RNA Have the Same Basic Structure
There are three main types of ribonucleic acid (RNA): messenger RNA (mRNA), transfer RNA (tRNA), and ribosomal RNA (rRNA). All three RNA types consist of a...
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Nucleic Acids02:43

Nucleic Acids

46.0K
Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
46.0K
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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Nucleic acids02:43

Nucleic acids

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Nucleic acids are the most important macromolecules for the continuity of life. They carry the cell's genetic blueprint and carry instructions for its functioning.
DNA and RNA
The two main types of nucleic acids are deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). DNA is the genetic material in all living organisms, ranging from single-celled bacteria to multicellular mammals. It is in the nucleus of eukaryotes and in the organelles, chloroplasts, and mitochondria. In prokaryotes,...
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RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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Updated: Sep 19, 2025

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA
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An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA

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可解释的RNA-小分子结合亲和力预测基于多视图增强学习.

Zeyu Wu1,2, Zhaohong Deng1,2, Qunzhuo Wu1,2

  • 1School of Artificial Intelligence and Computer Science, Jiangnan University, 1800 Lihu Avenue, Wuxi 214122, China.

Journal of chemical information and modeling
|June 3, 2025
PubMed
概括

预测RNA-小分子结合亲和力对于药物发现至关重要. 一个新的深度学习模型,EMMPTNet,使用物理化学和拓特性准确预测结合亲和力,优于现有方法.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 由于RNA是潜在的药物标,因此需要选RNA与小分子相互作用的方法.
  • 准确预测RNA-小分子结合亲和力是药物开发中的一个重大挑战.

研究的目的:

  • 开发一种可解释的深度学习模型,用于预测RNA-小分子结合亲和力.
  • 用物理化学和拓学属性准确预测结合亲和关系的挑战.

主要方法:

  • 提出了一个可解释的多视图,多尺度深度学习网络 (EMMPTNet).
  • EMMPTNet使用四个模块来有效地从多个数据视图中提取特征.
  • 一个多层感知子从提取的多视图,多尺度特征中预测结合亲和力.

主要成果:

  • EMMPTNet实现了0.058的平均绝对误差 (MAE) 和0.773.7的皮尔森相关系数 (PCC).
  • 与当前最先进的方法相比,该模型表现出优越的性能.
  • 对特征提取和重要性可视化的分析证实了模型的可解释性.

结论:

  • EMMPTNet提供了一种有效和可解释的方法来预测RNA-小分子结合亲和力.
  • 该模型的概括能力通过对新型化合物的验证得到证实.
  • 这项工作通过改善关键分子相互作用的预测来推进计算药物发现.