Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Nucleic Acids02:43

Nucleic Acids

44.2K
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,...
44.2K
The Equilibrium Binding Constant and Binding Strength02:18

The Equilibrium Binding Constant and Binding Strength

12.9K
The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
12.9K
Ligand Binding Sites02:40

Ligand Binding Sites

12.8K
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...
12.8K
Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Nucleic Acid Structure01:25

Nucleic Acid Structure

6.1K
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...
6.1K
Nucleic acids02:43

Nucleic acids

162.3K
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,...
162.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Epigenetic Silencing of Carotid Body TRPM7 Attenuates Hypertension in Obese Mice.

bioRxiv : the preprint server for biology·2026
Same author

Classification of driver and passenger mutations in different cancer types using deep neural networks.

Bioinformatics advances·2026
Same author

Herpes Simplex Virus Glycoprotein D Associated with Aβ<sub>1-42</sub> Tetramers Mediates Neurotoxicity by Perturbing Neuronal Membrane Integrity: A Molecular Dynamics Simulation.

ACS chemical neuroscience·2025
Same author

Computational design of protein complexes: influence of binding affinity.

Chemical communications (Cambridge, England)·2025
Same author

Towards next-generation 5-hydroxytryptamine 2C receptor modulators: Greener synthesis and evaluation of novel isocoumarin derivatives as PAAMs of 5-HT<sub>2C</sub>R.

Bioorganic chemistry·2025
Same author

Blind prediction of complex water and ion ensembles around RNA in CASP16.

bioRxiv : the preprint server for biology·2025

相关实验视频

Updated: Jul 5, 2025

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA
07:55

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

Published on: February 17, 2023

3.7K

使用机器学习预测RNA-小分子相互作用的结合亲和力的可靠方法.

Sowmya R Krishnan1,2, Arijit Roy2, M Michael Gromiha1,3,4

  • 1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.

Briefings in bioinformatics
|January 23, 2024
PubMed
概括

研究人员开发了机器学习模型,以预测小分子和各种核糖核酸 (RNA) 类型之间的结合亲和力. 这些模型加速了新型RNA药物标和疾病治疗抑制剂的发现.

关键词:
RNA-小分子相互作用在 RSAP 中,红色是 RSAP.结合性亲缘关系预测预测机器学习是机器学习.网络服务器是Web服务器.

更多相关视频

An Assay for Quantifying Protein-RNA Binding in Bacteria
07:02

An Assay for Quantifying Protein-RNA Binding in Bacteria

Published on: June 12, 2019

6.6K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

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

Published on: September 28, 2017

8.1K

相关实验视频

Last Updated: Jul 5, 2025

An Optimized Quantitative Pull-Down Analysis of RNA-Binding Proteins Using Short Biotinylated RNA
07:55

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

Published on: February 17, 2023

3.7K
An Assay for Quantifying Protein-RNA Binding in Bacteria
07:02

An Assay for Quantifying Protein-RNA Binding in Bacteria

Published on: June 12, 2019

6.6K
Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions
10:52

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

Published on: September 28, 2017

8.1K

科学领域:

  • 计算生物学 计算生物学
  • 药物发现 药物发现 药物发现
  • 分子生物学分子生物学

背景情况:

  • 核糖核酸 (RNA) 对于细胞调节至关重要,它们的调节失调与各种人类疾病有关.
  • 越来越多的RNA被认为是治疗干预的潜在药物标.
  • 识别新型RNA标及其小分子抑制剂对于推进疾病治疗至关重要.

研究的目的:

  • 开发机器学习模型,用于预测小分子与六种特定RNA亚型的结合亲和力.
  • 加速与疾病相关的RNA标和小分子抑制剂的识别.
  • 为这些预测模型提供免费访问的Web服务器.

主要方法:

  • 为六种RNA亚型量身定制的机器学习模型的开发:aptamers,miRNAs,重复,核糖体RNAs,核糖体开关和病毒RNAs.
  • 分析RNA序列组成,灵活性和RNA结合联体的极性质作为关键预测特征.
  • 使用刀测试进行评估,并使用外部盲测数据集进行验证.

主要成果:

  • 开发的模型实现了0.83的平均皮尔森相关性 (r) 和0.66.6的平均绝对误差.
  • 这些模型即使对几个RNA亚型的数据有限,也证明了可靠性.
  • 性能超过了外部验证数据集上现有的定量结构-活动关系 (QSAR) 模型.

结论:

  • 机器学习模型可以可靠地预测RNA-小分子结合亲和力.
  • 这些模型是加速发现RNA向疗法的宝贵工具.
  • 结合RNA-小分子的Affinity Predictor网络服务器可供公众使用.