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相关概念视频

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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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...
5.9K
Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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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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Leaky Scanning02:28

Leaky Scanning

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During most eukaryotic translation processes, the small 40S ribosome subunit scans an mRNA from its 5' end until it encounters the first start AUG codon. The large 60S ribosomal subunit then joins the smaller one to initiate protein synthesis. The location of the translation initiation is largely determined by the nucleotides near the start codon as there may be multiple translation initiation sites present on the mRNA.  Marilyn Kozak discovered that the sequence RCCAUGG (where R...
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相关实验视频

Updated: May 27, 2025

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
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Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

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使用语言模型的几何深度学习识别RNA-小分子结合点.

Weimin Zhu1, Xiaohan Ding1, Hong-Bin Shen1

  • 1Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China.

Journal of molecular biology
|February 17, 2025
PubMed
概括
此摘要是机器生成的。

RNABind是一个新的框架,通过将RNA大语言模型 (LLM) 与几何深度学习集成,准确地预测RNA-小分子结合点. 这通过改善治疗相互作用的计算预测来推进RNA向药物发现.

关键词:
RNA语言模型RNA语言模型RNA-小分子结合部位几何深度学习的几何深度学习

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Exploring Sequence Space to Identify Binding Sites for Regulatory RNA-Binding Proteins
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Identification of RNAs Engaged in Direct RNA-RNA Interaction with a Long Non-Coding RNA

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相关实验视频

Last Updated: May 27, 2025

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科学领域:

  • 计算生物学是一种计算生物学.
  • 药物发现 药物发现
  • 结构生物信息学 结构生物信息学

背景情况:

  • RNA分子是关键的治疗点,但识别小分子结合剂是具有挑战性的.
  • 需要准确的计算方法来预测RNA-小分子相互作用.
  • 大型语言模型 (LLM) 对生物序列分析有希望.

研究的目的:

  • 开发一个准确和高效的计算框架来预测RNA-小分子结合点.
  • 为了实现这一任务,利用RNA特定LLM和几何深度学习的进步.
  • 改进新型RNA向疗法的发现.

主要方法:

  • 开发了RNABind,这是一个嵌入式信息的几何深度学习框架.
  • 集成RNALLMs具有几何深度学习来编码RNA序列和结构.
  • 从多链复合体中编制了最大的RNA-小分子相互作用数据集.
  • 评估了8个预训练的RNALLM在结合部位预测方面.

主要成果:

  • RNABind显著优于现有的最先进的方法来预测RNA-小分子结合点.
  • 该框架有效地利用RNA序列和结构信息.
  • 综合评估表明RNABind. 的稳定性和准确性.

结论:

  • RNABind提供了一种强大的计算工具,用于预测RNA-小分子结合点.
  • 这项工作促进了RNA向药物发现的未来创新.
  • 该研究强调了将LLMs和几何深度学习整合到结构生物信息学中的潜力.